Papers by Lin Lin

Progra: Progress-Aware Reinforcement Learning for Multi-Turn Function Calling (2026.findings-acl)

Challenge:	Existing studies focus on building a meta-learner from input text but ignore abundant semantic information beneath class labels.
Approach:	They propose a framework to make full use of label semantics in few-shot text classification systems.
Outcome:	The proposed framework can be plugged into the existing few-shot text classification system.

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Huacan Chai, Zijie Cao, Maolin Ran, Yingxuan Yang, Jianghao Lin, Xin Peng, Hairui Wang, Renjie Ding, Ziyu Wan, Muning Wen, Weiwen Liu, Weinan Zhang, Fei Huang, Ying Wen

Challenge:	Existing methods for multi-turn function calling are limited by redundancy and lack explicit integration of progress awareness into training.
Approach:	They propose a framework that explicitly integrates progress awareness into LLM training for multi-turn function calling.
Outcome:	Empirical results show that Progra outperforms existing methods on two public benchmarks.

Hierarchy-aware Label Semantics Matching Network for Hierarchical Text Classification (2021.acl-long)

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Haibin Chen, Qianli Ma, Zhenxi Lin, Jiangyue Yan

Monitoring Decoding: Mitigating Hallucination via Evaluating the Factuality of Partial Response during Generation (2025.findings-acl)

Challenge:	Existing methods ignore the semantic relationship between text and labels, so they cannot make full use of hierarchical information.
Approach:	They propose a hierarchy-aware label semantics matching network to model the semantic relationship between text and labels in a semantic matching problem.
Outcome:	The proposed model captures the text-label semantics matching relationship among coarse-grained labels and fine-grain labels in a hierarchy-aware manner.

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Yurui Chang, Bochuan Cao, Lu Lin

De-biasing Distantly Supervised Named Entity Recognition via Causal Intervention (2021.acl-long)

Challenge:	Existing methods to mitiga hallucinations rely on sampling multiple full-length generations, which introduces significant response latency and becomes ineffective when the model consistently produces hallucines.
Approach:	They propose a framework that dynamically monitors the generation process and selectively applies in-process interventions to revise hallucination-prone tokens.
Outcome:	The proposed framework outperforms self-consistency-based approaches in both effectiveness and efficiency, achieving higher factual accuracy while significantly reducing computational overhead.

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Wenkai Zhang, Hongyu Lin, Xianpei Han, Le Sun

Named Entity Recognition

Challenge:	Existing methods for Named entity recognition (NER) rely on labeled data, which is labor-intensive.
Approach:	They propose a method to de-biase DS-NER models by a structural Causal Model . they propose to use a causal invariance regularizer to make them more robust .
Outcome:	The proposed method significantly improves DS-NER models on four datasets and three DS NER models.

AdaMix: Adaptive Mixing for Short and Long Reasoning Adapters (2026.acl-long)

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Hao Luo, Xiao Yan, Xinyan Li, Qiming Zeng, Yuhao Lin, Shanshan Feng, Hao Wang, Jiawei Jiang

NarrativePlay: Interactive Narrative Understanding (2024.eacl-demo)

Challenge:	Existing methods for large reasoning models have improved efficiency but still face limitations such as conflicting objectives and limited adaptability.
Approach:	They propose an adaptive reasoning framework that applies a uniform, computation-intensive deep reasoning strategy to all problems.
Outcome:	The proposed framework reduces the average response length of DeepSeek-R1-Distill-Qwen-7B by 54.9% while improving accuracy by up to 4.8% on five mathematical datasets.

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Runcong Zhao, Wenjia Zhang, Jiazheng Li, Lixing Zhu, Yanran Li, Yulan He, Lin Gui

Challenge:	Existing systems for interactive agents focus on specific capabilities in predetermined scenarios.
Approach:	They propose a novel system that allows users to role-play a fictional character and interact with other characters in narratives in an immersive environment.
Outcome:	The proposed system generates human-like responses guided by personality traits extracted from narratives.

CE-RM: A Pointwise Generative Reward Model Optimized via Two-Stage Rollout and Unified Criteria (2026.findings-acl)

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Xinyu Hu, Yancheng He, Weixun Wang, Tao Feng, Li Lin, Jiashun Liu, Wenbo Su, Bo Zheng, Xiaojun Wan

Challenge:	Existing studies have shown that rule-based evaluation methods are ineffective for open-ended natural language generation.
Approach:	They propose a pointwise generative reward model with a dedicated two-stage rollout method and unified query-based criteria that can be trained with 5.7K high-quality data.
Outcome:	The proposed model achieves superior performance on diverse reward model benchmarks, especially in Best-of-N scenarios, and delivers more effective improvements in downstream RL practice.

PECAN: LLM-Guided Dynamic Progress Control with Attention-Guided Hierarchical Weighted Graph for Long-Document QA (2025.findings-acl)

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Xinyu Wang, Yanzheng Xiang, Lin Gui, Yulan He

Training-free Deep Concept Injection Enables Language Models for Video Question Answering (2024.emnlp-main)

Challenge:	Long-document Question Answering (QA) challenges with large-scale text and long-distance dependencies.
Approach:	They propose a method that leverages large language models to control retrieval process . they propose 'attention-based' retrieval methods that construct hierarchical graphs .
Outcome:	The proposed method achieves LLM-level performance while maintaining computational complexity comparable to RAG methods.

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Xudong Lin, Manling Li, Richard Zemel, Heng Ji, Shih-Fu Chang

ANAH: Analytical Annotation of Hallucinations in Large Language Models (2024.acl-long)

Challenge:	Existing methods to train pretrained language models for zero-shot crossmodal tasks require crossmodal pretraining.
Approach:	They propose to inject visual concepts into the input text embedding space of a pretrained language model and build adaptation layers based on the intermediate representation of concepts.
Outcome:	The proposed model performs zero-shot crossmodal tasks without crossmodal pretraining . it is based on the injection of visual concepts as input tokens and augmentation in intermediate features . the proposed model achieves competitive or even better results in zero- shot and fine-tuning settings .

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Ziwei Ji, Yuzhe Gu, Wenwei Zhang, Chengqi Lyu, Dahua Lin, Kai Chen

Recyclable Tuning for Continual Pre-training (2023.findings-acl)

Challenge:	a comprehensive and fine-grained measurement of the hallucination is crucial for LLMs' wide applications.
Approach:	They propose a dataset that offers ANalytical Annotation of Hallucinations in Large Language Models.
Outcome:	The proposed dataset can be used to train and evaluate hallucination annotators.

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Yujia Qin, Cheng Qian, Xu Han, Yankai Lin, Huadong Wang, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou

mAggretriever: A Simple yet Effective Approach to Zero-Shot Multilingual Dense Retrieval (2023.emnlp-main)

Challenge:	Continual pre-training is the paradigm where pre-trained language models acquire fresh knowledge and gradually get upgraded.
Approach:	They propose to use adapted weights to recycle old PLMs for continual pre-training . they propose to combine initialization and distillation methods to achieve better performance .
Outcome:	The proposed method improves the convergence and performance of the upgraded PLM.

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Sheng-Chieh Lin, Amin Ahmad, Jimmy Lin

Controllable Fake Document Infilling for Cyber Deception (2022.findings-emnlp)

Challenge:	MLIR requires human annotations in multiple languages, making training labor-intensive.
Approach:	They propose a multilingual information retrieval model that leverages pre-trained multilingual transformers for dense retrieval.
Outcome:	Empirical results show that mAggretriever outperforms state-of-the-art models fine-tuned on English training data.

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Yibo Hu, Yu Lin, Erick Skorupa Parolin, Latifur Khan, Kevin Hamlen

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs (2026.acl-long)

Challenge:	Existing approaches to deter malicious intrusion generate multiple fake versions of a document that are context-agnostic and produce sub-optimal outputs.
Approach:	They propose a context-aware model for creating fake documents that masks important concepts of varied lengths and then infills a realistic but fake alternative considering both the previous and future contexts.
Outcome:	The proposed model outperforms baseline models in generating highly believable fakes with moderate modification to protect critical information and deceive adversaries.

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Yu Li, Xiaoran Shang, Qizhi Pei, Yun Zhu, Xin Gao, Honglin Lin, Zhanping Zhong, Zhuoshi Pan, Zheng Liu, Xiaoyang Wang, Conghui He, Dahua Lin, Feng Zhao, Lijun Wu

Disentangling Reasoning Capabilities from Language Models with Compositional Reasoning Transformers (2023.findings-acl)

Challenge:	High-quality post-training data is the primary engine driving LLM capabilities . datasets are often treated as isolated artifacts, overlooking their true developmental context .
Approach:	They propose a framework to reconstruct the evolutionary graph of dataset development using data lineage.
Outcome:	The proposed framework characterizes domain-specific structural patterns in Math-oriented datasets and general-domain corpora.

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Wanjun Zhong, Tingting Ma, Jiahai Wang, Jian Yin, Tiejun Zhao, Chin-Yew Lin, Nan Duan

Tree-of-Evolution: Tree-Structured Instruction Evolution for Code Generation in Large Language Models (2025.acl-long)

Challenge:	ReasonFormer is a unified reasoning framework for complex decision-making . it is based on the dual-process theory of cognitive science, where two cognitive systems interact to form a whole reasoning process.
Approach:	They propose a unified reasoning framework that mirrors the modular reasoning process of humans . they decouple the representation module and the reasoning modules to capture different levels of cognition .
Outcome:	The proposed framework shows that humans can perform better in complex decision-making tasks.

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Ziyang Luo, Kaixin Li, Hongzhan Lin, Yuchen Tian, Mohan Kankanhalli, Jing Ma

Rhetorical Device-Aware Sarcasm Detection with Counterfactual Data Augmentation (2025.findings-acl)

Challenge:	Data synthesis is a key research area in large language models (LLMs).
Approach:	They propose a framework that models code instruction synthesis process with a tree structure and optimization-driven evolution to alleviate constraints of unidirectional synthesis and randomness-driven generation.
Outcome:	The proposed framework outperforms open-weight code LLMs on five widely-used benchmarks.

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Qingqing Hong, Dongyu Zhang, Jiayi Lin, Dapeng Yin, Shuyue Zhu, Junli Wang

Rethinking Data Selection at Scale: Random Selection is Almost All You Need (2025.findings-emnlp)

Challenge:	Sarcasm is a complex form of sentiment expression widely used in human daily life.
Approach:	They propose a device-aware sarcasm dataset with counterfactually augmented data to capture its complexity.
Outcome:	The proposed dataset shows that it is more balanced than zero-shot models.

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Tingyu Xia, Bowen Yu, Kai Dang, An Yang, Yuan Wu, Yuan Tian, Yi Chang, Junyang Lin

Give Me More Feedback II: Annotating Thesis Strength and Related Attributes in Student Essays (P19-1)

Challenge:	Existing data selection techniques are designed for small data pools, a study finds . filtering data by token length is an efficient method for improving results .
Approach:	They use self-scoring methods that do not rely on external help to perform fine-tuning . they also find that filtering data by token length offers a stable and efficient method .
Outcome:	The proposed methods outperform random selection on large datasets on large data pools.

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Zixuan Ke, Hrishikesh Inamdar, Hui Lin, Vincent Ng

Challenge:	Existing work on automated essay scoring has focused on holistic scoring, but there is limited annotated corpus of essays with thesis strength scores.
Approach:	They propose a scoring rubric for persuasive essay quality and annotate corpus of essays with thesis strength scores.
Outcome:	The proposed scoring rubric could provide feedback to students on why essay gets thesis strength score . the rubric can be used to score persuasive essay quality, thesis strength, and organization .

Reinforcement Tuning for Detecting Stances and Debunking Rumors Jointly with Large Language Models (2024.findings-acl)

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Ruichao Yang, Wei Gao, Jing Ma, Hongzhan Lin, Bo Wang

HutCRS: Hierarchical User-Interest Tracking for Conversational Recommender System (2023.emnlp-main)

Challenge:	Social media has become a fertile ground for nurturing rumors and misinformation due to its lack of systematic moderation.
Approach:	They propose a framework to enhance the joint predictive capabilities of LLMs for stance detection and rumor verification tasks.
Outcome:	The proposed framework outperforms state-of-the-art methods and generalizes to non-LLMs accommodated as task models.

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Mingjie Qian, Yongsen Zheng, Jinghui Qin, Liang Lin

OmniCharacter: Towards Immersive Role-Playing Agents with Seamless Speech-Language Personality Interaction (2025.acl-long)

Challenge:	Existing CRSs assume that users like all attributes of the target item and dislike those unrelated to it, which can introduce bias in attribute-level feedback and impede the system’s ability to accurately identify the target items.
Approach:	They propose a framework that allows users to explicitly acquire user preferences through natural language conversations by providing explicit answers (yes/no) for each attribute they require.
Outcome:	The proposed framework portrays the conversation as a hierarchical interest tree that consists of two stages.

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Haonan Zhang, Run Luo, Xiong Liu, Yuchuan Wu, Ting-En Lin, Pengpeng Zeng, Qiang Qu, Feiteng Fang, Min Yang, Lianli Gao, Jingkuan Song, Fei Huang, Yongbin Li

Codec-SUPERB: An In-Depth Analysis of Sound Codec Models (2024.findings-acl)

Challenge:	Existing methods focus on replicating dialogues in textual form, neglecting the role’s voice traits as a crucial effect in interaction, which tends to be more immersive experiences in realistic scenarios.
Approach:	They propose a first seamless speech-language personality interaction model to achieve immersive RPAs with low latency.
Outcome:	The proposed model exhibits role-specific personality traits and vocal traits throughout the interaction, enabling a mixture of speech and language responses.

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Haibin Wu, Ho-Lam Chung, Yi-Cheng Lin, Yuan-Kuei Wu, Xuanjun Chen, Yu-Chi Pai, Hsiu-Hsuan Wang, Kai-Wei Chang, Alexander Liu, Hung-yi Lee

Challenge:	Researchers have developed a sound codec that can be used as tokenizers for preserving audio data and minimizing data transmission latency.
Approach:	They propose to use codec-SUPERB to assess codec models across representative sound applications and signal-level metrics rooted in sound domain knowledge.
Outcome:	The proposed codec-SUPERB model is evaluated on selected experimental settings.

Thread: A Logic-Based Data Organization Paradigm for How-To Question Answering with Retrieval Augmented Generation (2025.emnlp-main)

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Kaikai An, Fangkai Yang, Liqun Li, Junting Lu, Sitao Cheng, Shuzheng Si, Lu Wang, Pu Zhao, Lele Cao, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang, Baobao Chang

Contrastive Video-Language Learning with Fine-grained Frame Sampling (2022.aacl-main)

Challenge:	Recent advances in retrieval-augmented generation (RAG) have substantially improved question-answering systems, particularly for factoid ‘5Ws’ questions.
Approach:	They propose a data organization paradigm where large language models transform documents into more structured and loosely interconnected LUs.
Outcome:	Experiments in open-domain and industrial settings show that the proposed paradigm outperforms existing paradigms and shows high adaptability across diverse document formats.

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Zixu Wang, Yujie Zhong, Yishu Miao, Lin Ma, Lucia Specia

Chart-to-Text: A Large-Scale Benchmark for Chart Summarization (2022.acl-long)

Challenge:	despite recent progress in video and language representation learning, the weak or sparse correspondence between the two modalities remains a bottleneck.
Approach:	They propose a fine-grained contrastive objective for video frame sampling to improve cross-modal correspondence.
Outcome:	The proposed approach achieves state-of-the-art performance on YouCookII with long videos.

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Shankar Kantharaj, Rixie Tiffany Leong, Xiang Lin, Ahmed Masry, Megh Thakkar, Enamul Hoque, Shafiq Joty

Advancing Vision-Language Models with Adapter Ensemble Strategies (2024.findings-emnlp)

Challenge:	Inferring key insights from charts can be challenging and time-consuming.
Approach:	They propose a task where the goal is to explain a chart and summarize key takeaways from it in natural language.
Outcome:	The proposed model produces fluent summaries but suffers from hallucinations and factual errors . the proposed model is compared with other models and can be used to generate BLEU scores .

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Yue Bai, Handong Zhao, Zhe Lin, Ajinkya Kale, Jiuxiang Gu, Tong Yu, Sungchul Kim, Yun Fu

RealFin: How Well Do LLMs Reason About Finance When Users Leave Things Unsaid? (2026.findings-acl)

Challenge:	CLIP revolutes vision-language pretraining by using contrastive learning on paired web data.
Approach:	They propose to combine a "adapter ensemble" with traditional machine learning techniques to augment large-scale pretrained vision-language models.
Outcome:	The proposed model outperforms baselines and derives improvement when the number of ensemble parameters increases.

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Yuyang Dai, Yan Lin, Zhuohan Xie, Yuxia Wang

Mechanistic Unveiling of Transformer Circuits: Self-Influence as a Key to Model Reasoning (2025.findings-naacl)

Challenge:	General-purpose models tend to over-commit and guess, while most finance-specialized models fail to clearly identify missing premises.
Approach:	They propose a bilingual benchmark that removes premises from exam-style questions while keeping them linguistically plausible.
Outcome:	The proposed model overcommits and guesses while most finance-specialized models fail to clearly identify missing premises.

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Lin Zhang, Lijie Hu, Di Wang

Enhancing Dialogue Generation via Dynamic Graph Knowledge Aggregation (2023.acl-long)

Challenge:	Existing studies have shown that large language models implicitly embed reasoning trees, but their internal mechanisms remain largely opaque due to the complexity of non-linear interactions and high-dimensional operations.
Approach:	They propose to use circuit analysis and self-influence functions to map the reasoning process of large models.
Outcome:	The proposed model is able to map human-interpretable reasoning paths and a model's underlying circuits reveal human-mediated reasoning processes.

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Chen Tang, Hongbo Zhang, Tyler Loakman, Chenghua Lin, Frank Guerin

Two Heads Are Better Than One: Dual-Model Verbal Reflection at Inference-Time (2025.emnlp-main)

Challenge:	Existing graph neural networks (GNNs) teach message passing on a graph from text, resulting in a semantic gap between graph knowledge and text.
Approach:	They propose a framework to integrate external graph knowledge into chatbots by coagulating representations of both text and graph knowledge.
Outcome:	The proposed framework outperforms state-of-the-art (SOTA) baselines on dialogue generation.

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Jiazheng Li, Yuxiang Zhou, Junru Lu, Gladys Tyen, Lin Gui, Cesare Aloisi, Yulan He

Visual Pivoting Unsupervised Multimodal Machine Translation in Low-Resource Distant Language Pairs (2024.findings-emnlp)

Challenge:	Existing LLMs struggle to reliably detect subtle reasoning errors in ASAS tasks.
Approach:	They propose a dual-model framework with a dedicated Critic model trained for effective reflection that generates precise verbal feedback.
Outcome:	The proposed framework outperforms existing ASAS benchmarks and provides valuable insights into the performance of the proposed framework.

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Turghun Tayir, Lin Li, Xiaohui Tao, Mieradilijiang Maimaiti, Ming Li, Jianquan Liu

Encoding Spreadsheets for Large Language Models (2024.emnlp-main)

Challenge:	Existing studies show that neural MT achieves much worse translation quality than statistical MT with a small number of corpora.
Approach:	They propose a visual pivoting method for alignment between distant language pairs . they first construct a dataset and then apply it to pre-training and fine-tuning .
Outcome:	The proposed method outperforms baselines on DLPs and close language pairs.

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Haoyu Dong, Jianbo Zhao, Yuzhang Tian, Junyu Xiong, Mengyu Zhou, Yun Lin, José Cambronero, Yeye He, Shi Han, Dongmei Zhang

CFSum Coarse-to-Fine Contribution Network for Multimodal Summarization (2023.acl-long)

Challenge:	Spreadsheets are characterized by their extensive two-dimensional grids, flexible layouts, and varied formatting options, which pose significant challenges for large language models (LLMs).
Approach:	They propose a structural-anchor-based compression, inverse index translation, and data-format-aware aggregation module to compress spreadsheets effectively.
Outcome:	The proposed method outperforms the existing model in GPT4 and achieves a state-of-the-art 78.9% F1 score.

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Min Xiao, Junnan Zhu, Haitao Lin, Yu Zhou, Chengqing Zong

Backdoor Collapse: Eliminating Unknown Threats Via Known Backdoor Aggregation In Language Models (2026.acl-long)

Challenge:	Existing multimodal summarization models ignore the contribution of visual modalities . we propose a novel contribution network to consider different contributions of images .
Approach:	They propose a Coarse-to-Fine contribution network for multimodal summarization to consider different contributions of images for summarizing.
Outcome:	The proposed system outperforms baselines on the visual and textual modalities.

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Liang Lin, Miao Yu, Moayad Aloqaily, Zhenhong Zhou, Kun Wang, Linsey Pang, Prakhar Mehrotra, Qingsong Wen

Are U a Joke Master? Pun Generation via Multi-Stage Curriculum Learning towards a Humor LLM (2024.findings-acl)

Challenge:	Existing defenses rely on impractical assumptions about trigger settings to mitigate backdoor attacks . a recent study found that small amounts of training data can systematically induce harmful behaviors in large language models.
Approach:	They propose a backdoor defense framework that requires no prior knowledge of trigger settings . they use a two-stage process to aggregate backdoor representations and fine-tune recovery .
Outcome:	The proposed defense reduces the average Attack Success Rate to 4.41% across multiple benchmarks . the proposed framework generalizes across different types of backdoors, confirming its robustness in practical deployment scenarios.

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Yang Chen, Chong Yang, Tu Hu, Xinhao Chen, Man Lan, Li Cai, Xinlin Zhuang, Xuan Lin, Xin Lu, Aimin Zhou

ScaleBox: Enabling High-Fidelity and Scalable Code Verification for Large Language Models (2026.acl-demo)

Challenge:	Existing research has demonstrated that the ability of large language models (LLMs) to generate humorous sentences is limited to producing 25 unique jokes.
Approach:	They propose a multi-stage curriculum preference learning framework to optimize both pun structure preferences and humor preferences by a Chinese Pun dataset.
Outcome:	The proposed method significantly outperforms baseline models on Chinese and English benchmark datasets.

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Jiasheng Zheng, Xin Zheng, Boxi Cao, Pengbo Wang, Zhengzhao Ma, Qiming Zhu, Jiazhen Jiang, Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun

Syntax-guided Localized Self-attention by Constituency Syntactic Distance (2022.findings-emnlp)

Challenge:	Existing code sandboxes fail to provide accurate verification and efficiency under high-concurrency workloads.
Approach:	They propose a high-fidelity code verification system that provides sandbox feedback for RL training and evaluation.
Outcome:	The proposed system outperforms heuristic-matching baselines on LiveCodeBench and training stability on high-concurrency workloads.

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Shengyuan Hou, Jushi Kai, Haotian Xue, Bingyu Zhu, Bo Yuan, Longtao Huang, Xinbing Wang, Zhouhan Lin

Enhancing Neural Data-To-Text Generation Models with External Background Knowledge (D19-1)

Challenge:	Recent studies have shown that Transformers is implicitly learning syntactic information from data, albeit is highly dependent on the quality and scale of the training data.
Approach:	They propose a syntax-guided localized self-attention model that allows directly incorporating grammar structures from an external constituency parser.
Outcome:	The proposed model improves translation performance on a variety of datasets, from small to large datasets and with different source languages.

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Shuang Chen, Jinpeng Wang, Xiaocheng Feng, Feng Jiang, Bing Qin, Chin-Yew Lin

Operational Advice for Dense and Sparse Retrievers: HNSW, Flat, or Inverted Indexes? (2025.acl-industry)

Challenge:	Recent neural models for data-to-text generation rely on parallel pairs of data and text to learn writing knowledge.
Approach:	They propose to enhance neural models with external knowledge to improve fidelity of generated text.
Outcome:	The proposed model improves on Wikipedia infobox-to-text datasets on 21 datasets.

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Jimmy Lin

S2S-Arena: Evaluating Paralinguistic Instruction Following in Speech-to-Speech Models (2026.acl-long)

Challenge:	Currently, practitioners working on dense retrieval face a bewildering number of choices.
Approach:	They propose a framework for thinking about retrieval in terms of nearest-neighbor search over vector representations where these representations can be dense (typically called embeddings, generated from transformers) or flat (with brute-force search)
Outcome:	The proposed model explicates tradeoffs between HNSW and flat indexes from the perspectives of indexing time, query evaluation performance, and retrieval quality.

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Feng Jiang, Zhiyu Lin, Yiyang Liu, Liumeng Xue, Fan Bu, Yuhao Du, Xiangying Chen, Benyou Wang, Haizhou Li

RRHF-V: Ranking Responses to Mitigate Hallucinations in Multimodal Large Language Models with Human Feedback (2025.coling-main)

Challenge:	Existing benchmarks rely heavily on text-based evaluation and largely ignore paralinguistic cues such as prosody, emotion, and speaker traits.
Approach:	They propose a speech-native benchmark for evaluating instruction-following S2S models with explicit assessment of both semantic understanding and paralinguistic expression.
Outcome:	The proposed system enables more natural, robust, and human-aligned speech agents.

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Guoqing Chen, Fu Zhang, Jinghao Lin, Chenglong Lu, Jingwei Cheng

ReFSQL: A Retrieval-Augmentation Framework for Text-to-SQL Generation (2023.findings-emnlp)

Challenge:	Existing methods to mitigate hallucinations generate erroneous or fabricated information.
Approach:	They propose a rank-response-based model that annotates pair-reponses and trains alignment algorithms to improve the correspondence between images and text.
Outcome:	The proposed model outperforms the DPO method and outperfies existing methods on two MLLMs of different sizes and four widely used benchmarks.

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Kun Zhang, Xiexiong Lin, Yuanzhuo Wang, Xin Zhang, Fei Sun, Cen Jianhe, Hexiang Tan, Xuhui Jiang, Huawei Shen

RecLM: Recommendation Instruction Tuning (2025.acl-long)

Challenge:	Existing methods that align natural language with SQL Language underestimate inherent structural characteristics of SQL and lead to structure errors.
Approach:	They propose a retrieval-argument framework that aligns natural language with SQL Language and trains one encoder-decoder-based model to fit all questions.
Outcome:	The proposed framework improves accuracy and robustness of text-to-SQL generation on five datasets.

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Yangqin Jiang, Yuhao Yang, Lianghao Xia, Da Luo, Kangyi Lin, Chao Huang

Identifying Semantic Induction Heads to Understand In-Context Learning (2024.findings-acl)

Challenge:	Modern recommender systems aim to understand user-item relationships through past interactions, but their effectiveness is limited when handling sparse data or zero-shot scenarios.
Approach:	They propose a model-agnostic recommendation instruction-tuning paradigm that integrates large language models with collaborative filtering.
Outcome:	The proposed model-agnostic recommendation instruction-tuning paradigm improves performance across various settings and plug-and-play compatibility with state-of-the-art recommender systems.

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Jie Ren, Qipeng Guo, Hang Yan, Dongrui Liu, Quanshi Zhang, Xipeng Qiu, Dahua Lin

ARise: Towards Knowledge-Augmented Reasoning via Risk-Adaptive Search (2025.acl-long)

Challenge:	Large language models (LLMs) have demonstrated remarkable performance, but lack of transparency in their inference logic raises concerns about their trustworthiness.
Approach:	They conduct a detailed analysis of the operations of attention heads to understand their in-context learning of LLMs.
Outcome:	The proposed analysis of attention heads reveals that they increase the output logits of object tokens and recall objects . the proposed model is a novel approach to understand the in-context learning of large language models.

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Yize Zhang, Tianshu Wang, Sirui Chen, Kun Wang, Xingyu Zeng, Hongyu Lin, Xianpei Han, Le Sun, Chaochao Lu

Masked Thought: Simply Masking Partial Reasoning Steps Can Improve Mathematical Reasoning Learning of Language Models (2024.acl-long)

Challenge:	Large language models (LLMs) have impressive capabilities but their application in open-ended, knowledge-intensive, complex reasoning scenarios is limited.
Approach:	They propose a framework that integrates risk assessment of intermediate reasoning states with dynamic retrieval-augmented generation within a Monte Carlo tree search paradigm.
Outcome:	The proposed framework outperforms the state-of-the-art KAR methods by up to 23.10% and the latest RAG-equipped large reasoning models by upto 25.37%.

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Changyu Chen, Xiting Wang, Ting-En Lin, Ang Lv, Yuchuan Wu, Xin Gao, Ji-Rong Wen, Rui Yan, Yongbin Li

ToolGrad: Efficient Tool-use Dataset Generation with Textual “Gradients” (2026.findings-acl)

Challenge:	Despite the advances in large language models, they still face difficulties with multi-step reasoning tasks.
Approach:	They propose a method that randomly masks certain tokens within the chain of thought to improve model accuracy by 5% over standard supervised fine-tuning.
Outcome:	The proposed method improves accuracy and accuracy by 5% over standard fine-tuning with a few codes modified.

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Zhongyi Zhou, Kohei Uehara, Haoyu Zhang, Jingtao Zhou, Lin Gu, Ruofei Du, Zheng Xu, Tatsuya Harada

DGST: a Dual-Generator Network for Text Style Transfer (2020.emnlp-main)

Challenge:	Prior work synthesizes tool-use LLM datasets by first generating a user query, then complex tool-using annotations like DFS.
Approach:	They propose an agentic framework that synthesizes user queries and generates valid tool-use chains . they propose a dataset with more complex tool use, lower cost, and almost 100% pass rate .
Outcome:	Experiments show that tools trained on ToolGrad outperform expensive baseline datasets and proprietary LLMs.

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Xiao Li, Guanyi Chen, Chenghua Lin, Ruizhe Li

OpenSR: Open-Modality Speech Recognition via Maintaining Multi-Modality Alignment (2023.acl-long)

Challenge:	Existing studies on text style transfer focus on altering sentiment words to preserve attribute-independent information.
Approach:	They propose a Dual-Generator network architecture for text Style Transfer using two generators.
Outcome:	The proposed model performs better than existing models on Yelp and IMDb datasets.

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Xize Cheng, Tao Jin, Linjun Li, Wang Lin, Xinyu Duan, Zhou Zhao

Gödel Agent: A Self-Referential Agent Framework for Recursively Self-Improvement (2025.acl-long)

Challenge:	Speech Recognition often gets stuck in the lack of new domain utterances when training a model of new-domain speech.
Approach:	They propose a training system Open-modality Speech Recognition that enables zero-shot modality transfer . they use multi-modal alignment in phoneme space to maintain multi-modality alignment .
Outcome:	The proposed system achieves zero-shot modality transfer compared to existing methods . it achieves state-of-the-art performance on audio-visual speech recognition and lip-reading with 2.7% and 25.0%, respectively.

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Xunjian Yin, Xinyi Wang, Liangming Pan, Li Lin, Xiaojun Wan, William Yang Wang

CriticBench: Benchmarking LLMs for Critique-Correct Reasoning (2024.findings-acl)

Challenge:	Existing agentic systems cannot search the whole design space due to the restriction of human-designed components.
Approach:	They propose a Gödel Agent framework that allows agents to recursively improve themselves without relying on fixed algorithms or fixed algorithms.
Outcome:	The proposed framework surpasses manual crafted agents in performance, efficiency, and generalizability.

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Zicheng Lin, Zhibin Gou, Tian Liang, Ruilin Luo, Haowei Liu, Yujiu Yang

PivotAttack: Rethinking the Search Trajectory in Hard-Label Text Attacks via Pivot Words (2026.findings-acl)

Challenge:	CriticBench is a benchmark designed to assess LLMs’ abilities to critique and refine their reasoning across a variety of tasks.
Approach:	They propose a benchmark to assess LLMs' ability to critique and correct reasoning across a variety of tasks.
Outcome:	The proposed benchmark examines the performance of 17 large language models in generation, critique, and correction reasoning.

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Yuzhi Liang, Shiliang Xiao, Jingsong Wei, Qiliang Lin, Xia Li

Can LLMs Understand the Implication of Emphasized Sentences in Dialogue? (2024.findings-emnlp)

Challenge:	Existing hard-label text attacks rely on inefficient "outside-in" strategies that traverse vast search spaces.
Approach:	They propose a query-efficient "inside-out" framework that perturbs Pivot Sets to induce label flips.
Outcome:	The proposed framework outperforms state-of-the-art methods in both Attack Success Rate and query efficiency.

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Guan-Ting Lin, Hung-yi Lee

Chat Vector: A Simple Approach to Equip LLMs with Instruction Following and Model Alignment in New Languages (2024.acl-long)

Challenge:	Emphasis is a crucial component in human communication, which indicates speaker’s intention and implication beyond pure text in dialogue.
Approach:	They propose a benchmark dataset with annotated dialogue samples capturing the implications of emphasis.
Outcome:	The proposed evaluation pipeline achieves high correlation with human scoring and commercial LLMs perform better than open-source LLM.

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Shih-Cheng Huang, Pin-Zu Li, Yu-chi Hsu, Kuang-Ming Chen, Yu Tung Lin, Shih-Kai Hsiao, Richard Tsai, Hung-yi Lee

Union-of-Experts: Neurons in Mixture-of-Experts are Secretly Routers (2026.acl-long)

Challenge:	Despite the rapid development of large language models, the language capabilities of most open-source LLMs are primarily focused on English due to data constraints.
Approach:	They propose a chat vector to equip pre-trained language models with instruction following and human value alignment via simple model arithmetic.
Outcome:	The proposed method can be extended to include various languages, base models, and chat vectors.

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Songhao Wu, Ang Lv, Ruobing Xie, Samm Sun, Di Wang, Rui Yan, Yankai Lin

All Languages Matter: Understanding and Mitigating Language Bias in Multilingual RAG (2026.acl-long)

Challenge:	Mixture-of-Experts (MoE) models rely on an external router to assign tokens to experts, resulting in suboptimal performance.
Approach:	They propose an MoE variant that performs "expert-autonomous routing" by pre-designating a fraction of neurons within each expert as "routing neurons" they pre-train UoE models with up to 3B parameters and show they outperform traditional MoEs with matched efficiency.
Outcome:	The proposed model outperforms existing models with 3B parameters and provides valuable insights into expert-autonomous selection and the broader routing mechanisms of MoE models.

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Dan Wang, Guozhao Mo, Yafei Shi, Cheng Zhang, Bo Zheng, Boxi Cao, Xuanang Chen, Yaojie Lu, Hongyu Lin, Ben He, Xianpei Han, Le Sun

MERaLiON-AudioLLM: Advancing Speech and Language Understanding for Singapore (2025.acl-demo)

Challenge:	Existing mRAG systems suffer from a language bias during reranking, systematically favoring English and the query’s native language.
Approach:	They propose a language-agnostic utility-driven reranker alignment technique to mitigate language bias during re-ranking.
Outcome:	The proposed approach mitigates language bias and consistently improves mRAG performance across languages.

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Yingxu He, Zhuohan Liu, Geyu Lin, Shuo Sun, Bin Wang, Wenyu Zhang, Xunlong Zou, Nancy F. Chen, AiTi Aw

Human-Inspired Obfuscation for Model Unlearning: Local and Global Strategies with Hyperbolic Representations (2025.findings-emnlp)

Challenge:	MERaLiON-AudioLLM is the first general-purpose audio-based large language model for multitask learning.
Approach:	They introduce MERaLiON-AudioLLM, a general-purpose audio-based large language model for multitask learning with a focus on Singlish understanding.
Outcome:	The proposed model exhibits strong generalization across a diverse set of tasks . it is a leading solution for region-specific AI applications.

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Zekun Wang, Jingjie Zeng, Yingxu Li, Liang Yang, Hongfei Lin

Distilling ChatGPT for Explainable Automated Student Answer Assessment (2023.findings-emnlp)

Challenge:	Existing methods for unlearning large language models struggle to balance effective forgetting with maintaining model utility.
Approach:	They propose a human-inspired unlearning framework that simulates forgetting on fuzzy data and represents them in hyperbolic and Euclidean spaces.
Outcome:	The proposed framework is able to forget sensitive content while maintaining the model’s language understanding, fluency, and benchmark performance.

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Jiazheng Li, Lin Gui, Yuxiang Zhou, David West, Cesare Aloisi, Yulan He

Small Models Struggle to Learn from Strong Reasoners (2025.findings-acl)

Challenge:	Existing automated student answer assessment models lack explainable and faithful feedback.
Approach:	They propose a framework that leverages ChatGPT for student answer scoring and rationale generation.
Outcome:	The proposed method improves the overall QWK score by 11% compared to ChatGPT.

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Yuetai Li, Xiang Yue, Zhangchen Xu, Fengqing Jiang, Luyao Niu, Bill Yuchen Lin, Bhaskar Ramasubramanian, Radha Poovendran

Generation-Augmented and Embedding Fusion in Document-Level Event Argument Extraction (2025.coling-main)

Challenge:	a small learning gap exists between large and small language models . long CoT data and large model responses are not beneficial for small models - a problem that may be due to the small student model's ability to handle distribution shifts.
Approach:	They propose a mix distillation strategy that balances reasoning complexity by combining long and short CoT examples or reasoning from both larger and smaller models.
Outcome:	The proposed strategy outperforms training on large and small models on short CoT and small model CoT.

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Xingjian Lin, Shengfei Lyu, Xin Wang, Qiuju Chen, Huanhuan Chen

The Art of Abstention: Selective Prediction and Error Regularization for Natural Language Processing (2021.acl-long)

Challenge:	Document-level event argument extraction is a crucial task that aims to extract arguments from the entire document, beyond sentence-level analysis.
Approach:	They propose a novel approach to document-level event argument extraction that integrates predefined templates and generative language models into a foundational embedding derived from a classification model.
Outcome:	The proposed approach is more effective than baseline models and data-efficient in low-resource scenarios.

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Ji Xin, Raphael Tang, Yaoliang Yu, Jimmy Lin

APOLLO: An Optimized Training Approach for Long-form Numerical Reasoning (2024.lrec-main)

Challenge:	Pre-trained language models have improved the state-of-the-art results on many NLP applications.
Approach:	They propose a simple error regularization trick that improves confidence estimation without substantially increasing the computation budget.
Outcome:	The proposed regularization improves confidence estimation without increasing computation budget.

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Jiashuo Sun, Hang Zhang, Chen Lin, Xiangdong Su, Yeyun Gong, Jian Guo

JointCL: A Joint Contrastive Learning Framework for Zero-Shot Stance Detection (2022.acl-long)

Challenge:	Existing methods to generate reasoning programs that ignore the differences between facts treated all facts equally, leading to wrong punishment of programs that differed from the ground truth.
Approach:	They propose an optimized training framework for long-form numerical reasoning that incorporates a number-aware negative sampling strategy and consistency-based reinforcement learning to increase execution accuracy.
Outcome:	The proposed method improves the performance of long-form numerical reasoning on the FinQA and ConvFinQA leaderboards.

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Bin Liang, Qinglin Zhu, Xiang Li, Min Yang, Lin Gui, Yulan He, Ruifeng Xu

ATLAS: Improving Lay Summarisation with Attribute-based Control (2024.acl-short)

Challenge:	Existing methods achieve promising performance in in-target stance detection when trained and tested on the same datasets.
Approach:	They propose a joint contrastive learning framework to generalize stance features for unseen targets.
Outcome:	The proposed framework achieves state-of-the-art on three benchmark datasets.

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Zhihao Zhang, Tomas Goldsack, Carolina Scarton, Chenghua Lin

Logit Space Constrained Fine-Tuning for Mitigating Hallucinations in LLM-Based Recommender Systems (2025.emnlp-main)

Challenge:	Lay summarisation aims to produce scientific summaries that are comprehensible to non-experts.
Approach:	They propose an abstractive summarisation approach that can control properties contributing to overall "layness" they evaluate ATLAS on a combination of biomedical lay summarization datasets.
Outcome:	The proposed approach outperforms state-of-the-art summarisation metrics on biomedical datasets and shows that it can be discriminatory and emergently influenced.

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Jianfeng Deng, Qingfeng Chen, Debo Cheng, Jiuyong Li, Lin Liu

Innovative Image Fraud Detection with Cross-Sample Anomaly Analysis: The Power of LLMs (2025.acl-long)

Challenge:	Existing LLM-based recommender systems rely on standard fine-tuning methodologies, often ignoring hallucination issues during the fine-uning process.
Approach:	They propose a logit space constraint-based fine-tuning framework to mitigate hallucination in LLM-based recommenders by incorporating Kullback–Leibler divergence into the training objective.
Outcome:	Experiments on two recommendation models with distinct LLM backbones and four real-world datasets show that LCFT reduces hallucination and enhances recommendation performance.

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QiWen Wang, Junqi Yang, Zhenghao Lin, Zhenzhe Ying, Weiqiang Wang, Chen Lin

Soft Language Clustering for Multilingual Model Pre-training (2023.acl-long)

Challenge:	Existing methods for document image fraud detection lack visual clues on tampered regions.
Approach:	They propose a framework for detecting logical inconsistencies in document images by leveraging LLMs.
Outcome:	The proposed framework outperforms state-of-the-art fraud detection methods by 79.6% on CrossCred and industrial solutions by 21.7% on business data.

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Jiali Zeng, Yufan Jiang, Yongjing Yin, Yi Jing, Fandong Meng, Binghuai Lin, Yunbo Cao, Jie Zhou

Multilingual and Multi-Aspect Hate Speech Analysis (D19-1)

Challenge:	Multilingual pre-trained language models have demonstrated impressive (zero-shot) cross-lingual transfer abilities, however, their performance is hindered when the target language has distant typology from the source language or when pre-training data is limited in size.
Approach:	They propose a method that contextually retrieves prompts as flexible guidance for encoding instances conditionally.
Outcome:	The proposed method improves on the XTREME task and also for low-resource languages in unsupervised sentence retrieval.

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Nedjma Ousidhoum, Zizheng Lin, Hongming Zhang, Yangqiu Song, Dit-Yan Yeung

Text Classification

Challenge:	Current research on hate speech analysis is oriented towards monolingual and single classification tasks.
Approach:	They propose to use a multilingual multi-aspect hate speech analysis dataset to test current methods . they evaluate the dataset in various classification settings and discuss how to leverage annotations .
Outcome:	The proposed dataset can be used to improve hate speech detection and classification in general.

DialogQAE: N-to-N Question Answer Pair Extraction from Customer Service Chatlog (2023.findings-emnlp)

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Xin Zheng, Tianyu Liu, Haoran Meng, Xu Wang, Yufan Jiang, Mengliang Rao, Binghuai Lin, Yunbo Cao, Zhifang Sui

Semantics of the Unwritten: The Effect of End of Paragraph and Sequence Tokens on Text Generation with GPT2 (2021.acl-srw)

Challenge:	Existing work on question-answer extraction fails to integrate incomplete utterances from dialog context for composite QA retrieval.
Approach:	They propose a task where questions and corresponding answers might be separated across different utterances.
Outcome:	The proposed methods perform well on 5 customer service datasets and set a benchmark for N-to-N DialogQAE with utterance and session level evaluation metrics.

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He Bai, Peng Shi, Jimmy Lin, Luchen Tan, Kun Xiong, Wen Gao, Jie Liu, Ming Li

Neuron-Level Differentiation of Memorization and Generalization in Large Language Models (2025.emnlp-main)

Challenge:	Experimental results show that pre-trained language model GPT2 can generate better continuations by learning to generate the in the fine-tuning stage.
Approach:	They conduct experiments on an English essay dataset using Chinese-GPT2 . they find that the model can generate better continuations by learning to generate the in the fine-tuning stage.
Outcome:	The pre-trained language model GPT2 can generate better continuations by learning to generate the in the fine-tuning stage.

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Ko-Wei Huang, Yi-Fu Fu, Ching-Yu Tsai, Yu-Chieh Tu, Tzu-ling Cheng, Cheng-Yu Lin, Yi-Ting Yang, Heng-Yi Liu, Keng-Te Liao, Da-Cheng Juan, Shou-De Lin

Flames: Benchmarking Value Alignment of LLMs in Chinese (2024.naacl-long)

Challenge:	Existing models exhibit memorization and generalization behaviors in ways that are not easily interpretable or controllable.
Approach:	They propose to use a GPT-2 and LLaMA-3.2 model to identify distinct neuron subsets responsible for each behavior to steer the model toward memorization or generalization.
Outcome:	The proposed models show that inference-time interventions on these neurons can steer the model’s behavior toward memorization or generalization.

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Kexin Huang, Xiangyang Liu, Qianyu Guo, Tianxiang Sun, Jiawei Sun, Yaru Wang, Zeyang Zhou, Yixu Wang, Yan Teng, Xipeng Qiu, Yingchun Wang, Dahua Lin

Open-Ended Visual Question Answering by Multi-Modal Domain Adaptation (2020.findings-emnlp)

Challenge:	Existing benchmarks for large language models (LLMs) do not accurately uncover safety vulnerabilities in LLMs.
Approach:	They propose a value alignment benchmark called Flames that encompasses both harmlessness principles and a unique morality dimension that integrates specific Chinese values such as harmony.
Outcome:	The proposed model performs poorly on Flames, particularly in safety and fairness dimensions.

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Yiming Xu, Lin Chen, Zhongwei Cheng, Lixin Duan, Jiebo Luo

Exploring Diverse Expressions for Paraphrase Generation (D19-1)

Challenge:	Existing approaches to visual question answering (VQA) are not suitable for real-world applications.
Approach:	They propose a supervised multi-modal domain adaptation method for visual question answering in images that exploits supervised domain adaptation.
Outcome:	The proposed method outperforms state-of-the-art methods on the benchmark VQA 2.0 and VizWiz datasets.

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Lihua Qian, Lin Qiu, Weinan Zhang, Xin Jiang, Yong Yu

Self-supervised Quantized Representation for Seamlessly Integrating Knowledge Graphs with Large Language Models (2025.acl-long)

Challenge:	Existing neural paraphrase generation methods focus on single paraphrases while ignoring the fact that diversity is essential for enhancing generalization capability and robustness of downstream applications.
Approach:	They propose a novel approach with two discriminators and multiple generators to generate a variety of different paraphrases.
Outcome:	The proposed model gains significant diversity and improves quality over state-of-the-art datasets.

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Qika Lin, Tianzhe Zhao, Kai He, Zhen Peng, Fangzhi Xu, Ling Huang, Jingying Ma, Mengling Feng

A Generative Pre-Trained Language Model for Channel Prediction in Wireless Communications Systems (2025.emnlp-main)

Challenge:	Large Language Models (LLMs) are gaining popularity due to their lack of knowledge hallucination and lack of a coherent model.
Approach:	They propose a self-supervised quantized representation method to compress KG structural and semantic knowledge into discrete codes that align the format of language sentences.
Outcome:	The proposed framework outperforms existing unsupervised methods producing more distinguishable codes on KG link prediction and triple classification tasks.

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Bo Lin, Huanming Zhang, Yuhua Jiang, Yucong Wang, Tengyu Zhang, Shaoqiang Yan, Hongyao Li, Yihong Liu, Feifei Gao

ACCEPT: Adaptive Codebook for Composite and Efficient Prompt Tuning (2024.findings-emnlp)

Challenge:	Existing model-based channel prediction methods suffer from limited accuracy due to imperfect temporal modeling, while existing AI-based methods suffers from limited generalization due to inadequate training strategies.
Approach:	They propose a generative pre-trained language model for channel prediction based on channel correlation and train it based upon transformer decoder architecture.
Outcome:	The proposed model can learn various channel characteristics and perform impressive tasks across multiple dimensions.

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Yu-Chen Lin, Wei-Hua Li, Jun-cheng Chen, Chu-Song Chen

IEPile: Unearthing Large Scale Schema-Conditioned Information Extraction Corpus (2024.acl-short)

Challenge:	Prompt Tuning has been a popular fine-tuning method for large-scale pretrained language models.
Approach:	They propose a method that allows all soft prompts to share a set of learnable codebook vectors in each subspace, with each prompt differentiated by a number of adaptive weights.
Outcome:	The proposed method achieves superior performance on 17 diverse natural language tasks including natural language understanding (NLU) and question answering (QA) tasks by tuning only 0.3% of parameters of the PLMs.

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Honghao Gui, Lin Yuan, Hongbin Ye, Ningyu Zhang, Mengshu Sun, Lei Liang, Huajun Chen

Decouple knowledge from paramters for plug-and-play language modeling (2023.findings-acl)

Challenge:	Large Language Models exhibit a significant performance gap in Information Extraction (IE) high-quality instruction data is the vital key for enhancing LLMs' specific capabilities .
Approach:	They propose a bilingual (English and Chinese) IE instruction corpus that contains 0.32B tokens.
Outcome:	The proposed model improves the performance of LLMs for IE with zero-shot generalization.

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Xin Cheng, Yankai Lin, Xiuying Chen, Dongyan Zhao, Rui Yan

Exploring the Capability of Multimodal LLMs with Yonkoma Manga: The YManga Dataset and Its Challenging Tasks (2024.findings-emnlp)

Challenge:	Pre-trained language models (PLMs) have made impressive results in a wide range of NLP tasks.
Approach:	They propose a pre-training model with editable and scalable key-value memory and leverage knowledge in an explainable manner by knowledge retrieval in the pasted macro ‘MEMORY’.
Outcome:	The proposed model decouples the knowledge storage from model parameters with an editable and scalable key-value memory and leverages knowledge in an explainable manner by knowledge retrieval in the pasted macro ‘MEMORY’.

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Qi Yang, Jingjie Zeng, Liang Yang, Zhihao Yang, Hongfei Lin

Automatic Label Sequence Generation for Prompting Sequence-to-sequence Models (2022.coling-1)

Challenge:	YManga dataset is the first specifically designed for yonkoma manga understanding .
Approach:	They propose to use a dataset of 1,015 yonkoma strips with 10,150 human annotations to define three tasks for panel sequence detection, intent generation and description generation for masked panels.
Outcome:	The proposed dataset contains 1,015 high-quality yonkoma strips with 10,150 human annotations.

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Zichun Yu, Tianyu Gao, Zhengyan Zhang, Yankai Lin, Zhiyuan Liu, Maosong Sun, Jie Zhou

Farewell Freebase: Migrating the SimpleQuestions Dataset to DBpedia (C18-1)

Challenge:	Prompting has shown to be sample efficient compared to fine-tuning with pre-trained models.
Approach:	They propose a fully automatic prompting method that uses natural language prompts on sequence-to-sequence models and a beam search method to generate a large amount of label sequence candidates.
Outcome:	The proposed method significantly outperforms other no-manual-design methods on single label words and generates large amount of label sequence candidates.

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Michael Azmy, Peng Shi, Jimmy Lin, Ihab Ilyas

CodeScope: An Execution-based Multilingual Multitask Multidimensional Benchmark for Evaluating LLMs on Code Understanding and Generation (2024.acl-long)

Challenge:	Existing datasets for question answering over knowledge graphs lack answer triples from Freebase . a defunct knowledge graph makes it difficult to build "real-world" question answering systems .
Approach:	They propose a benchmark dataset for simple question answering over knowledge graphs that maps SimpleQuestions entities and predicates from Freebase to DBpedia.
Outcome:	The proposed dataset provides simple yet strong baselines with and without neural networks.

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Weixiang Yan, Haitian Liu, Yunkun Wang, Yunzhe Li, Qian Chen, Wen Wang, Tingyu Lin, Weishan Zhao, Li Zhu, Hari Sundaram, Shuiguang Deng

Learning to Solve Domain-Specific Calculation Problems with Knowledge-Intensive Programs Generator (2025.naacl-long)

Challenge:	Existing benchmarks for evaluating the code understanding and generation capacities of Large Language Models are insufficient . existing benchmarks focus on a narrow range of popular programming languages and specific tasks .
Approach:	They propose an execution-based, multilingual, multitask evaluation benchmark for LLMs . they evaluate coding performance from three dimensions: length, difficulty, efficiency .
Outcome:	The proposed benchmark covers 43 programming languages and eight coding tasks.

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Chengyuan Liu, Shihang Wang, Lizhi Qing, Jun Lin, Ji Zhang, Fei Wu, Kun Kuang

Content-Specific Humorous Image Captioning Using Incongruity Resolution Chain-of-Thought (2024.findings-naacl)

Challenge:	Domain Large Language Models (LLMs) are developed for domain-specific tasks based on general LLMs, but it still requires professional knowledge to facilitate the expertise for some domain- specific tasks.
Approach:	They propose a pipeline to solve domain-specific calculation problems with KIPG . they use it to extract key variables and calculate outcomes dependent on domain knowledge .
Outcome:	The proposed pipeline solves domain-specific calculation problems more effectively . it generates knowledge-intensive programs according to the domain- specific documents .

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Kohtaro Tanaka, Kohei Uehara, Lin Gu, Yusuke Mukuta, Tatsuya Harada

Learning In-context Learning for Named Entity Recognition (2023.acl-long)

Challenge:	Existing methods for generating humorous captions are generic and do not capture the content of images.
Approach:	They propose a framework that generates content-specific resolutions from fine details extracted from an image and integrates logit bias and negative sampling to suppress the output of generic resolutions.
Outcome:	The proposed framework generates humorous captions tailored to the content of specific input images.

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Jiawei Chen, Yaojie Lu, Hongyu Lin, Jie Lou, Wei Jia, Dai Dai, Hua Wu, Boxi Cao, Xianpei Han, Le Sun

Semantic-conditioned Dual Adaptation for Cross-domain Query-based Visual Segmentation (2023.findings-acl)

Challenge:	Existing methods to recognize entities in text are limited by the diversity of entity types and the lack of high-quality annotations.
Approach:	They propose an in-context learning-based NER approach that can inject in-const NER ability into PLMs and recognize entities of novel types on-the-fly using only a few demonstrative instances.
Outcome:	The proposed method outperforms the PLMs+fine-tuning counterparts on 4 few-shot NER datasets and significantly outperformed the Plms+initialized extractors.

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Ye Wang, Tao Jin, Wang Lin, Xize Cheng, Linjun Li, Zhou Zhao

Reflect, Not Reflex: Inference-Based Common Ground Improves Dialogue Response Quality (2022.emnlp-main)

Challenge:	Existing approaches to visual segmentation from language queries require expensive labeling and degradation when deployed to an unseen domain.
Approach:	They propose a task to adapt a visual segmentation model from a labeled domain to an unseen domain.
Outcome:	The proposed framework achieves precise feature- and relation-invariant across domains via universal semantic structure.

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Pei Zhou, Hyundong Cho, Pegah Jandaghi, Dong-Ho Lee, Bill Yuchen Lin, Jay Pujara, Xiang Ren

Summarising Historical Text in Modern Languages (2021.eacl-main)

Challenge:	Currently, human communication models fail to explicitly model common ground (CG) . less than half of the responses in current data is rated as high quality .
Approach:	They propose a dataset that annotates dialogues with explicit CG and solicits 9k diverse responses each following one common ground.
Outcome:	The proposed dataset annotates dialogues with explicit CG and solicits 9k diverse responses each following one common ground.

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Xutan Peng, Yi Zheng, Chenghua Lin, Advaith Siddharthan

Weight Distillation: Transferring the Knowledge in Neural Network Parameters (2021.acl-long)

Challenge:	Historical text summarisation is a routine for historians and digital humanities researchers but has never been automated.
Approach:	They propose a model that can be trained even with no cross-lingual data and further benchmark it against state-of-the-art algorithms.
Outcome:	The proposed model outperforms standard cross-lingual benchmarks on historical text summarisation task and identifies distinctness and value of the dataset.

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Ye Lin, Yanyang Li, Ziyang Wang, Bei Li, Quan Du, Tong Xiao, Jingbo Zhu

Sarcasm-R1: Enhancing Sarcasm Detection through Focused Reasoning (2025.findings-emnlp)

Challenge:	Knowledge distillation is an effective method for model acceleration and compression.
Approach:	They propose to use parameters to distill knowledge from large neural networks to small ones . they propose to do this by using a parameter generator to transfer the knowledge to a small neural network .
Outcome:	The proposed method learns a small network 1.88 2.94x faster than the large network but with competitive BLEU points.

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Qi Yang, Jingjie Zeng, Liang Yang, Kai Ma, Hongfei Lin

Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsing (2020.findings-emnlp)

Challenge:	Existing methods for sarcasm detection are limited by supervised learning or prompt engineering . a new approach decomposes sarcasm detection into three dimensions: language, context, and emotion .
Approach:	They propose a method that decomposes sarcasm detection into three dimensions: language, context, and emotion.
Outcome:	The proposed method outperforms state-of-the-art methods in most cases.

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Xi Victoria Lin, Richard Socher, Caiming Xiong

Benchmarking Uncertainty Metrics for LLM Target-Aware Search (2025.findings-emnlp)

Challenge:	BRIDGE is a powerful sequential architecture for cross-modal semantic parsing . BRidege captures cross-modal dependencies between natural language questions and relational databases .
Approach:	They propose a sequential architecture that captures cross-modal dependencies between questions and relational databases in cross-DB semantic parsing.
Outcome:	The proposed architecture performs well on the well-studied Spider benchmark (65.5% dev, 59.2% test).

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Pei-Fu Guo, Yun-Da Tsai, Shou-De Lin

Large Language Models are not Fair Evaluators (2024.acl-long)

Challenge:	Existing uncertainty metrics for LLM search methods do not capture the diverse types of uncertainty needed to guide different optimization goals.
Approach:	They propose a framework for uncertainty benchmarking that captures four different uncertainty types . the uncertainty types Answer, Correctness, Aleatoric, and Epistemic serve different optimization goals .
Outcome:	The proposed framework identifies four different uncertainty types . the uncertainty types serve different optimization goals in LLM search .

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Peiyi Wang, Lei Li, Liang Chen, Zefan Cai, Dawei Zhu, Binghuai Lin, Yunbo Cao, Lingpeng Kong, Qi Liu, Tianyu Liu, Zhifang Sui

Disentangling Logic: The Role of Context in Large Language Model Reasoning Capabilities (2025.findings-acl)

Challenge:	Existing evaluation frameworks that use large language models as referees are insufficient for accurately assessing their alignment with human intent.
Approach:	They propose a calibration framework to address positional bias in large language models as evaluators by manually annotating the “win/tie/lose” outcomes of responses from ChatGPT and Vicuna-13B in the Vicun A Benchmark’s question prompt.
Outcome:	The proposed framework alleviates evaluation bias, resulting in closer alignment with human judgments.

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Wenyue Hua, Kaijie Zhu, Lingyao Li, Lizhou Fan, Mingyu Jin, Shuhang Lin, Haochen Xue, Zelong Li, Jindong Wang, Yongfeng Zhang

LLMEmbed: Rethinking Lightweight LLM’s Genuine Function in Text Classification (2024.acl-long)

Challenge:	Using large language models, large language model models can be used to evaluate reasoning abilities in context-rich scenarios.
Approach:	They construct datasets for both propositional logic and abductive logic reasoning with four difficulty levels across 12 distinct domains based on Wikipedia categorization and those with purely abstract variables.
Outcome:	The proposed model can be used to benchmark LLMs in real-world scenarios, but not in context-rich scenarios.

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ChunLiu ChunLiu, Hongguang Zhang, Kainan Zhao, Xinghai Ju, Lin Yang

SHARP: Unlocking Interactive Hallucination via Stance Transfer in Role-Playing LLMs (2025.findings-acl)

Challenge:	Recent attempts to improve text classification performance are based on heuristic Chain-of-Thought (CoT) LLMEmbed is a simple and effective transfer learning strategy that can be used to improve the performance of large language models.
Approach:	They propose a simple transfer learning strategy to improve text classification using heuristic Chain-of-Thought.
Outcome:	The proposed method achieves strong performance on publicly available datasets while using low training overhead.

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Chuyi Kong, Ziyang Luo, Hongzhan Lin, Zhiyuan Fan, Yaxin Fan, Yuxi Sun, Jing Ma

Causal2Vec: Improving Decoder-only LLMs as Embedding Models through a Contextual Token (2026.acl-long)

Challenge:	Existing studies on social interactions neglect hallucination while struggling with poor generalizability and implicit character fidelity judgments.
Approach:	They propose a generalizable and explicit paradigm for uncovering interactive patterns of Large Language Models across diverse worldviews by defining interactive hallucination through stance transfer and SHARP, a benchmark built by extracting relations from commonsense knowledge graphs.
Outcome:	The proposed paradigm is generalizable and explicit and demonstrates its effectiveness and stability.

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Ailiang Lin, Zhuoyun Li, Yusong Wang, Kotaro Funakoshi, Manabu Okumura

An Attentive Fine-Grained Entity Typing Model with Latent Type Representation (D19-1)

Challenge:	Existing methods modify attention mechanism to be bidirectional, undermining LLMs’ ability to extract semantic information acquired during pre-training.
Approach:	They propose a general-purpose embedding model that pre-encodes input text into a single Contextual token and then prepends it to the LLM's input sequence.
Outcome:	The proposed model improves performance of decoder-only large language models without altering their architectures or introducing significant computational overhead.

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Ying Lin, Heng Ji

CRITICTOOL: Evaluating Self-Critique Capabilities of Large Language Models in Tool-Calling Error Scenarios (2025.emnlp-main)

Challenge:	Existing fine-grained entity typing models are criticized for label independence assumption .
Approach:	They propose a fine-grained entity typing model with a new attention mechanism and a hybrid type classifier to exploit type inter-dependency with latent type representation.
Outcome:	The proposed model significantly advances the state-of-the-art on fine-grained entity typing.

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Shiting Huang, Zhen Fang, Zehui Chen, Siyu Yuan, Junjie Ye, Yu Zeng, Lin Chen, Qi Mao, Feng Zhao

RoadMapper: A Multi-Agent System for Roadmap Generation of Solving Complex Research Problems (2026.findings-acl)

Challenge:	a number of tools are used to perform complex tasks, but the tool utilization process can cause errors.
Approach:	They propose a critique evaluation benchmark for tool learning that analyzes function-calling errors on tool evaluation benchmarks.
Outcome:	The proposed critique evaluation benchmark holds diverse tool-use errors with varying complexities, which better reflects real-world scenarios.

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Jiacheng Liu, Zichen Tang, Zhongjun Yang, Xinyi Hu, Xueyuan Lin, Linwei Jia, Ruofei Bai, Rongjin Li, Shiyao Peng, Haocheng Gao, Haihong E

Dynamic Dialogue Policy for Continual Reinforcement Learning (2022.coling-1)

Challenge:	Existing tools to generate structured content for research tasks are limited in their ability to generate high-quality roadmaps.
Approach:	They propose a benchmark to evaluate the ability of large language models (LLMs) to generate high-quality roadmaps for solving complex research problems.
Outcome:	The proposed system can improve LLMs’ ability for roadmap generation while saving 84% of the time required by human experts.

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Christian Geishauser, Carel van Niekerk, Hsien-chin Lin, Nurul Lubis, Michael Heck, Shutong Feng, Milica Gašić

RWKV: Reinventing RNNs for the Transformer Era (2023.findings-emnlp)

Challenge:	Continual reinforcement learning of the dialogue policy has remained unaddressed . lack of a framework with training protocols, baseline models and suitable metrics has hindered research in this direction.
Approach:	They propose a continual learning algorithm, baseline architectures and metrics for assessing continual reinforcement learning models.
Outcome:	The proposed architecture can integrate new knowledge seamlessly and achieve significant zero-shot performance when exposed to unseen domains.

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Bo Peng, Eric Alcaide, Quentin Anthony, Alon Albalak, Samuel Arcadinho, Stella Biderman, Huanqi Cao, Xin Cheng, Michael Chung, Leon Derczynski, Xingjian Du, Matteo Grella, Kranthi Gv, Xuzheng He, Haowen Hou, Przemyslaw Kazienko, Jan Kocon, Jiaming Kong, Bartłomiej Koptyra, Hayden Lau, Jiaju Lin, Krishna Sri Ipsit Mantri, Ferdinand Mom, Atsushi Saito, Guangyu Song, Xiangru Tang, Johan Wind, Stanisław Woźniak, Zhenyuan Zhang, Qinghua Zhou, Jian Zhu, Rui-Jie Zhu

Teach Small Models to Reason by Curriculum Distillation (2025.emnlp-main)

Challenge:	recurrent neural networks struggle to match the performance of Transformers due to limitations in parallelization and scalability.
Approach:	They propose a model architecture that combines the efficient parallelizable training of transformers with the efficient inference of RNNs.
Outcome:	The proposed model performs on par with similarly sized RNNs, suggesting future work can leverage this architecture to create more efficient models.

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Wangyi Jiang, Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun

Rationalizing Medical Relation Prediction from Corpus-level Statistics (2020.acl-main)

Challenge:	Large Reasoning Models (LRMs) show strong System-2-style reasoning, but at the cost of significant computational overhead.
Approach:	They propose a two-stage curriculum distillation framework which builds a robust internal problem-solving student model and then teaches the student model to externalize this knowledge as explicit reasoning.
Outcome:	The proposed model outperforms single-stage baselines on mathematical benchmarks and significantly outperformed LRMs on complex tasks.

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Zhen Wang, Jennifer Lee, Simon Lin, Huan Sun

Linguistic Theories

Challenge:	Existing work on predicting relations based on text corpus has focused on analyzing raw texts mentioning two entities.
Approach:	They propose a framework that can be used to rationalize medical relation prediction . they recall contexts associated with the target entities and recognize relational interactions between them .
Outcome:	The proposed framework can achieve competitive predictive performance against a comprehensive list of neural baseline models, and present rationales to justify its prediction.

SafeAgent: Safeguarding LLM Agents via an Automated Risk Simulator (2026.acl-long)

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Xueyang Zhou, Weidong Wang, Lin Lu, Jiawen Shi, Guiyao Tie, Xu Yongtian, Lixing Chen, Pan Zhou, Neil Zhenqiang Gong, Lichao Sun

Challenge:	SafeAgent improves agent safety through fully automated synthetic data generation.
Approach:	They propose a framework that improves agent safety through fully automated synthetic data generation.
Outcome:	The proposed framework outperforms closed-source models on two safety benchmarks and one real-world task.

With Ears to See and Eyes to Hear: Sound Symbolism Experiments with Multimodal Large Language Models (2024.emnlp-main)

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Tyler Loakman, Yucheng Li, Chenghua Lin

Denoising Distantly Supervised Open-Domain Question Answering (P18-1)

Challenge:	Large Language Models and Vision Language Model (VLMs) have demonstrated aptitude as potential substitutes for human participants in psycholinguistic experiments.
Approach:	They examine whether large language models and vision language models implicitly understand sound-based phenomena via orthography and imagery alone.
Outcome:	The proposed models demonstrate sound symbolism and ability to "hear" using language and vision modules.

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Yankai Lin, Haozhe Ji, Zhiyuan Liu, Maosong Sun

PDTrim: Targeted Pruning for Prefill-Decode Disaggregation in Inference (2026.acl-long)

Challenge:	Existing DS-QA models ignore rich information contained in other paragraphs and are noisy . Existing systems rely on pre-identified relevant texts, which do not always exist in real-world QA scenarios.
Approach:	They propose a model which uses a paragraph selector to filter out noisy paragraphs and a reader to extract the correct answer from denoised paragraphs.
Outcome:	The proposed model can capture useful information from noisy data and achieve significant improvements on open domain question answering.

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Hao Zhang, Lyu Mengsi, Zhuo Chen, Yulong Ao, Yonghua Lin

LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models (2023.emnlp-main)

Challenge:	Existing pruning methods ignore prefill-decode (PD) disaggregation in practice.
Approach:	They propose a pruning method that is highly integrated with prefill-decode (PD) disaggregation, enabling more precise pruning of blocks.
Outcome:	The proposed method achieves strong performance in both PD disaggregation and PD unified settings, and can be extended to other non-block pruning methods.

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Huiqiang Jiang, Qianhui Wu, Chin-Yew Lin, Yuqing Yang, Lili Qiu

I-MCTS: Enhancing Agentic AutoML via Introspective Monte Carlo Tree Search (2026.findings-eacl)

Challenge:	Large language models (LLMs) are increasingly lengthy and require longer prompts . this paper presents a coarse-to-fine prompt compression method to reduce cost and increase performance.
Approach:	They propose a coarse-to-fine prompt compression method that maintains semantic integrity under high compression ratios and a token-level iterative compression algorithm to better model the interdependence between compressed contents.
Outcome:	The proposed method yields state-of-the-art performance and allows for up to 20x compression with little performance loss over four datasets from different scenarios.

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Zujie Liang, Feng Wei, Wujiang Xu, Yuxi Qian, Lin Chen, Xinhui Wu

GUM-SAGE: A Novel Dataset and Approach for Graded Entity Salience Prediction (2025.findings-acl)

Challenge:	Existing LLM-based agents struggle with low diversity and suboptimal code generation.
Approach:	They propose an approach that iteratively expands tree nodes through an introspective process that meticulously analyzes solutions and results from parent and sibling nodes.
Outcome:	The proposed approach shows a 4% improvement in performance compared to the strong open-source AutoML agents.

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Jessica Lin, Amir Zeldes

Challenge:	Existing methods for graded entity salience are subjective but lack consistency.
Approach:	They propose a method for graded entity salience that combines subjective judgments and summarization-based methods that define saliency as mention-worthiness in a summary.
Outcome:	The proposed approach outperforms existing methods and shows stronger correlation with human summaries and alignments.

Dual Hierarchical Dialogue Policy Learning for Legal Inquisitive Conversational Agents (2026.findings-acl)

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Xubo Lin, Zezhi Deng, Shihao Wang, Grace Hui Yang, Yang Deng

Joint Multimedia Event Extraction from Video and Article (2021.findings-emnlp)

Challenge:	Existing systems for conversational AI are user-driven, but in many real-world situations, they do not extract information to achieve its own objectives.
Approach:	They propose an inquisitive conversational agent that learns when and how to ask probing questions . they also propose a framework for a conversational ICA specifically tailored to the court .
Outcome:	The proposed method outperforms single-agent RL baselines on a U.S. Supreme Court dataset.

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Brian Chen, Xudong Lin, Christopher Thomas, Manling Li, Shoya Yoshida, Lovish Chum, Heng Ji, Shih-Fu Chang

Bag of Tricks for Optimizing Transformer Efficiency (2021.findings-emnlp)

Challenge:	Existing methods to extract multimedia events from video and text are limited to video and images.
Approach:	They propose a task to jointly extract events from video and text documents . they propose 'self-supervised' cross-modal event coreference model and cross-mod transformer architecture .
Outcome:	The proposed method achieves 6.0% and 5.8% absolute F-score gain on video-article pairs . the proposed method can resolve coreference and extract multimodal event frames more accurately than existing methods.

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Ye Lin, Yanyang Li, Tong Xiao, Jingbo Zhu

Multilingual BERT Post-Pretraining Alignment (2021.naacl-main)

Challenge:	Improving Transformer efficiency has become increasingly attractive in recent years.
Approach:	They propose to combine pruning, quantization, new architectures and training strategies to improve Transformer efficiency.
Outcome:	The proposed methods improve the inference efficiency of a strong Transformer system by 3.80x on CPU and 2.52x on GPU.

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Lin Pan, Chung-Wei Hang, Haode Qi, Abhishek Shah, Saloni Potdar, Mo Yu

FOLIO: Natural Language Reasoning with First-Order Logic (2024.emnlp-main)

Challenge:	Recent work improves on the success of monolingual pretrained language models by adding cross-lingual tasks that always involve English.
Approach:	They propose a method to align multilingual contextual embeddings as a post-pretraining step for improved cross-lingual transferability of pretrained language models.
Outcome:	The proposed model outperforms XLM-R_Base on translation-train tasks while using less parallel data and fewer parameters.

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Simeng Han, Hailey Schoelkopf, Yilun Zhao, Zhenting Qi, Martin Riddell, Wenfei Zhou, James Coady, David Peng, Yujie Qiao, Luke Benson, Lucy Sun, Alexander Wardle-Solano, Hannah Szabó, Ekaterina Zubova, Matthew Burtell, Jonathan Fan, Yixin Liu, Brian Wong, Malcolm Sailor, Ansong Ni, Linyong Nan, Jungo Kasai, Tao Yu, Rui Zhang, Alexander Fabbri, Wojciech Kryscinski, Semih Yavuz, Ye Liu, Xi Lin, Shafiq Joty, Yingbo Zhou, Caiming Xiong, Rex Ying, Arman Cohan, Dragomir Radev

Deep Unknown Intent Detection with Margin Loss (P19-1)

Challenge:	Existing benchmarks for logical reasoning in large language models lack language naturalness or limited complexity.
Approach:	They propose to use first-order logic annotations to evaluate logical reasoning capabilities of large language models.
Outcome:	The proposed dataset evaluates the FOL reasoning ability of supervised fine-tuning on medium-sized language models.

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Ting-En Lin, Hua Xu

Bi-Drop: Enhancing Fine-tuning Generalization via Synchronous sub-net Estimation and Optimization (2023.findings-emnlp)

Challenge:	Existing methods for detecting unknown intents are difficult due to lack of examples.
Approach:	They propose a method for detecting unknown intents using bidirectional long-term memory networks with the margin loss as the feature extractor.
Outcome:	The proposed method can yield consistent improvements on two benchmark datasets.

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Shoujie Tong, Heming Xia, Damai Dai, Runxin Xu, Tianyu Liu, Binghuai Lin, Yunbo Cao, Zhifang Sui

Automatic Correction of Human Translations (2022.naacl-main)

Challenge:	Pretrained language models can be fine-tuned on limited training data, which can overfit and thus diminish performance.
Approach:	They propose a fine-tuning strategy that selectively updates model parameters using gradients from various sub-nets dynamically generated by dropout.
Outcome:	The proposed method outperforms existing methods on the GLUE benchmark and exhibits excellent generalization ability and robustness for domain transfer, data imbalance, and low-resource scenarios.

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Jessy Lin, Geza Kovacs, Aditya Shastry, Joern Wuebker, John DeNero

Evaluating Open-Domain Dialogues in Latent Space with Next Sentence Prediction and Mutual Information (2023.acl-long)

Challenge:	Despite recent advances in machine translation, a tremendous amount of translated content in the world is still written by humans.
Approach:	They propose a task of translation error correction (TEC) that corrects human-generated translations by correcting all errors in a source sentence and a human-created translation.
Outcome:	The proposed system improves translation accuracy by 5.1 points compared to MT systems with human errors .

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Kun Zhao, Bohao Yang, Chenghua Lin, Wenge Rong, Aline Villavicencio, Xiaohui Cui

Multi-modal Stance Detection: New Datasets and Model (2024.findings-acl)

Challenge:	Existing evaluation methods for open-domain dialogues are difficult due to the one-to-many issue of the open- domain dialogues.
Approach:	They propose a learning-based automatic evaluation metric which can robustly evaluate open-domain dialogues by augmenting CVAEs with a Next Sentence Prediction objective and employing Mutual Information to model the semantic similarity of text in the latent space.
Outcome:	The proposed method can evaluate open-domain dialogues on two open- domain dialogue datasets.

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Bin Liang, Ang Li, Jingqian Zhao, Lin Gui, Min Yang, Yue Yu, Kam-Fai Wong, Ruifeng Xu

Red-Teaming LLM Multi-Agent Systems via Communication Attacks (2025.findings-acl)

Challenge:	Existing methods for stance detection for pure texts have limited results to multi-modal content.
Approach:	They propose a multi-modal stance detection framework that leverages target information to learn multi-modal stance features from textual and visual modalities.
Outcome:	The proposed framework achieves state-of-the-art in multi-modal stance detection on five datasets based on Twitter .

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Pengfei He, Yuping Lin, Shen Dong, Han Xu, Yue Xing, Hui Liu

Probing Commonsense Explanation in Dialogue Response Generation (2021.findings-emnlp)

Challenge:	Large Language Model-based Multi-Agent Systems (LLM-MAS) have revolutionized complex problem-solving capability by enabling agent collaboration through message-based communications.
Approach:	They propose an attack that exploits communication mechanisms in Large Language Model-based Multi-Agent Systems (LLM-MAS) by intercepting and manipulating inter-agent messages.
Outcome:	The proposed attack exploits communication mechanisms in large language model-based multi-agent systems by intercepting and manipulating inter-agencies.

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Pei Zhou, Pegah Jandaghi, Hyundong Cho, Bill Yuchen Lin, Jay Pujara, Xiang Ren

Themis: A Reference-free NLG Evaluation Language Model with Flexibility and Interpretability (2024.emnlp-main)

Challenge:	Currently, response generation (RG) models do not understand human communication intents.
Approach:	They propose to examine commonsense reasoning implicitly to determine whether RG models produce coherent responses in conversations.
Outcome:	The proposed probing settings show that RG models fail to capture the logical relations between commonsense explanations and responses and fine-tuning on in-domain data do not lead to understanding of CSR for RG.

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Xinyu Hu, Li Lin, Mingqi Gao, Xunjian Yin, Xiaojun Wan

Few Shot Dialogue State Tracking using Meta-learning (2021.eacl-main)

Challenge:	Existing methods for evaluation of natural language generation tasks lack reliable data.
Approach:	They propose to use annotations from human and GPT-4 to construct a corpus for NLG evaluation.
Outcome:	The proposed corpus can perform flexible and interpretable evaluations without references and surpasses existing models.

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Saket Dingliwal, Shuyang Gao, Sanchit Agarwal, Chien-Wei Lin, Tagyoung Chung, Dilek Hakkani-Tur

Balanced Data Sampling for Language Model Training with Clustering (2024.findings-acl)

Challenge:	Existing methods for transferring knowledge from resource-rich domains to unknown domains are data hungry . a meta-learning algorithm is proposed to solve the problem of zero/few-shot DST .
Approach:	They propose a meta-learner for the problem of zero/few-shot DST . they propose to agnostically train any existing chatbot system to improve its performance .
Outcome:	The proposed meta-learner improves on baseline in a low-data setting.

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Yunfan Shao, Linyang Li, Zhaoye Fei, Hang Yan, Dahua Lin, Xipeng Qiu

Distilling Discrimination and Generalization Knowledge for Event Detection via Delta-Representation Learning (P19-1)

Challenge:	Large Language Models (LLMs) are a fundamental part of the training process.
Approach:	They propose to use clustering to balance the text distribution of training data for better model training.
Outcome:	Extensive experiments validate the effectiveness of ClusterClip Sampling under various training datasets and large language models.

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Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun

A Framework for Multi-Language Service Design with the Language Grid (L18-1)

Challenge:	Current neural event detection approaches focus on trigger-centric representations, which work well on distilling discrimination knowledge, but poorly on learning generalization knowledge.
Approach:	They propose a Delta-learning approach to distill discrimination and generalization knowledge by incrementally learning and adaptively fusing event representation.
Outcome:	The proposed method significantly outperforms previous approaches on unseen/sparse trigger words and achieves state-of-the-art performance on ACE2005 and KBP2017 datasets.

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Donghui Lin, Yohei Murakami, Toru Ishida

HERB: Measuring Hierarchical Regional Bias in Pre-trained Language Models (2022.findings-aacl)

Challenge:	International NPO/NGOs are struggling with the design and development of tools and systems for multi-language communication in the real world.
Approach:	They propose a framework for service design with the Language Grid by bridging the gap between language service infrastructures and multi-language systems.
Outcome:	The proposed framework bridges the gap between language service infrastructures and multi-language systems by allowing users to design and develop multilingual communication services and tools in the real world.

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Yizhi Li, Ge Zhang, Bohao Yang, Chenghua Lin, Anton Ragni, Shi Wang, Jie Fu

Aegis:An Advanced LLM-Based Multi-Agent for Intelligent Functional Safety Engineering (2024.emnlp-industry)

Challenge:	Existing methods do not examine social groups categorised by geographical information, leaving the region-related biases in pre-trained LMs unexplored.
Approach:	They propose a hierarchical regional bias evaluation method to quantify regional bias in pre-trained language models.
Outcome:	The proposed method evaluates regional bias with regard to comprehensive topics and measures potential regional bias that can be propagated to downstream tasks.

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Lu Shi, Bin Qi, Jiarui Luo, Yang Zhang, Zhanzhao Liang, Zhaowei Gao, Wenke Deng, Lin Sun

Learning or Self-aligning? Rethinking Instruction Fine-tuning (2024.acl-long)

Challenge:	Aegis is an advanced LLM-based multi-agent for intelligent functional safety engineering that can perform all phases of a vehicle's lifecycle, including design, development, production, operation, and decommissioning.
Approach:	They introduce Aegis: An Advanced LLM-Based Multi-Agent for Intelligent Functional Safety Engineering.
Outcome:	The proposed solution can perform Hazard Analysis and Risk Assessment (HARA), document Functional Safety Requirements (FSR), and plan test cases for Automatic Emergency Braking (AEB) systems.

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Mengjie Ren, Boxi Cao, Hongyu Lin, Cao Liu, Xianpei Han, Ke Zeng, Wan Guanglu, Xunliang Cai, Le Sun

Beneath the Surface: Unveiling Harmful Memes with Multimodal Reasoning Distilled from Large Language Models (2023.findings-emnlp)

Challenge:	Instruction fine-tuning (IFT) is a crucial phase in building large language models (LLMs).
Approach:	They propose a knowledge intervention framework to decouple the potential underlying factors of IFT and enable individual analysis of different factors.
Outcome:	The proposed framework decouples the potential underlying factors of IFT, enabling individual analysis of different factors.

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Hongzhan Lin, Ziyang Luo, Jing Ma, Long Chen

Can Language Models Replace Programmers for Coding? REPOCOD Says ‘Not Yet’ (2025.acl-long)

Challenge:	Existing methods for harmful meme detection ignore in-depth cognition of meme text and image . authors propose a framework for learning reasonable thoughts from LLMs for better multimodal fusion .
Approach:	They propose to use large language models to learn reasonable thoughts from LLMs for better multimodal fusion and lightweight fine-tuning.
Outcome:	The proposed approach achieves superior performance than state-of-the-art methods on the harmful meme detection task.

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Shanchao Liang, Nan Jiang, Yiran Hu, Lin Tan

MetaVL: Transferring In-Context Learning Ability From Language Models to Vision-Language Models (2023.acl-short)

Challenge:	Existing benchmarks for code generation use short completions, synthetic examples, or focus on limited scale repositories, failing to represent real-world coding tasks.
Approach:	They propose a Python code-generation benchmark that contains 980 whole-function generation tasks with realistic dependencies from 11 popular projects.
Outcome:	The proposed benchmarks are short completions, synthetic examples, or focus on limited scale repositories, failing to represent real-world coding tasks.

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Masoud Monajatipoor, Liunian Harold Li, Mozhdeh Rouhsedaghat, Lin Yang, Kai-Wei Chang

Personalizing Dialogue Agents via Meta-Learning (P19-1)

Challenge:	Large-scale pre-trained vision-language models do not possess the ability to conduct in-context learning.
Approach:	They propose to meta-train a language model to perform in-context learning on NLP tasks and then transfer this model to VL tasks by attaching a visual encoder.
Outcome:	The proposed model outperforms the baseline model on VQA, OK-VQA, and GQA while having 20 times fewer parameters.

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Andrea Madotto, Zhaojiang Lin, Chien-Sheng Wu, Pascale Fung

Gated Convolutional Bidirectional Attention-based Model for Off-topic Spoken Response Detection (2020.acl-main)

Challenge:	Existing personalized dialogue models use human designed persona descriptions to improve dialogue consistency.
Approach:	They propose to extend Model-Agnostic Meta-Learning (MAML) to personalized dialogue learning without using persona descriptions.
Outcome:	The proposed model outperforms baseline models in terms of human-evaluated fluency and consistency on a persona-chat dataset.

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Yefei Zha, Ruobing Li, Hui Lin

V-ALPHASOCIAL: Benchmark and Self-Reflective Chain-of-Thought Generation for Visual Social Commonsense Reasoning (2025.findings-acl)

Challenge:	Off-topic spoken response detection is crucial for an automated speaking assessment system.
Approach:	They propose a novel approach for off-topic spoken response detection with high off-top recall on both seen and unseen prompts.
Outcome:	The proposed model achieves significant improvements in detecting off-topic responses with extremely high on-topic recall on both seen and unseen prompts.

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Zongyu Lin, Zhikun Xu, Xiaohan Song, Yixin Wan, Xingcheng Yao, Tsung-Han Lin, Selina Song, Pranav Subbaraman, Ben Zhou, Kai-Wei Chang, Yizhou Sun

Recent Trends in Linear Text Segmentation: A Survey (2024.findings-emnlp)

Challenge:	Social commonsense reasoning is a multimodal task that requires both textual and visual cues.
Approach:	They propose a method that integrates visual cues into social commonsense reasoning tasks.
Outcome:	The proposed method improves social commonsense reasoning on a multimodal foundation model.

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Iacopo Ghinassi, Lin Wang, Chris Newell, Matthew Purver

Manifold Learning-based Word Representation Refinement Incorporating Global and Local Information (2020.coling-main)

Challenge:	Linear text segmentation is the task of automatically tagging text documents with topic shifts . the task is based on coherence modeling and/or local cues to identify topic boundaries .
Approach:	They provide an overview of current advances in linear text segmentation . they highlight limitations of available resources and of the task itself .
Outcome:	The proposed task is based on the most recent literature and under-explored research directions.

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Wenyu Zhao, Dong Zhou, Lin Li, Jinjun Chen

Vittorio Castelli, Rishav Chakravarti, Saswati Dana, Anthony Ferritto, Radu Florian, Martin Franz, Dinesh Garg, Dinesh Khandelwal, Scott McCarley, Michael McCawley, Mohamed Nasr, Lin Pan, Cezar Pendus, John Pitrelli, Saurabh Pujar, Salim Roukos, Andrzej Sakrajda, Avi Sil, Rosario Uceda-Sosa, Todd Ward, Rong Zhang

Challenge:	Recent studies show word embedding models underestimate similarities between similar words and overestimate similarities between distant words.
Approach:	They propose two new word embedding methods that align original and re-fined embeddable spaces to a new refined semantic space.
Outcome:	The proposed methods outperform state-of-the-art methods for word representation refinement.

The TechQA Dataset (2020.acl-main)

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Argue with Me Tersely: Towards Sentence-Level Counter-Argument Generation (2023.emnlp-main)

Challenge:	TECHQA is a domain-adaptation question answering dataset for the technical support domain.
Approach:	They propose a domain-adaptation question-answering dataset for the technical support domain that contains actual questions posed by users on a technical forum .
Outcome:	The TECHQA dataset highlights two real-world issues from the automated customer support domain.

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Jiayu Lin, Rong Ye, Meng Han, Qi Zhang, Ruofei Lai, Xinyu Zhang, Zhao Cao, Xuanjing Huang, Zhongyu Wei

Ada-LEval: Evaluating long-context LLMs with length-adaptable benchmarks (2024.naacl-long)

Challenge:	Existing work describes paragraph-level counter-argument generation task as paragraph-based . however, sentence-level generation can be quite different due to its unique constraints and brevity-focused challenges.
Approach:	They propose a benchmark framework for sentence-level counter-argument generation . they use an annotated debate forum dataset to generate high-quality counter-argments .
Outcome:	The proposed framework and evaluator are competitive in counter-argument generation tasks.

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Chonghua Wang, Haodong Duan, Songyang Zhang, Dahua Lin, Kai Chen

Challenge:	Existing long-text evaluation benchmarks, such as L-Eval and LongBench, focus on QA and summarization tasks.
Approach:	They propose a length-adaptable benchmark for evaluating the long-context understanding of large language models.
Outcome:	The proposed benchmarks do not cover ultralong settings (100k+ tokens) and are difficult to evaluate across different length ranges.

Detecting Troll Tweets in a Bilingual Corpus (2020.lrec-1)

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Lin Miao, Mark Last, Marina Litvak

REInstruct: Building Instruction Data from Unlabeled Corpus (2024.findings-acl)

Challenge:	a large amount of troll accounts have emerged with efforts to manipulate public opinion on social network sites . a recent study found that trolled tweets spread misinformation, fake news, and propaganda . we use supervised classification to detect trol tweets in both English and Russian .
Approach:	They propose to detect troll tweets in English and Russian using machine learning algorithms . they use monolingual, cross-lingual, and bilingual training scenarios .
Outcome:	The proposed method uses monolingual, cross-lingual, and bilingual training scenarios.

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Shu Chen, Xinyan Guan, Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun

RockNER: A Simple Method to Create Adversarial Examples for Evaluating the Robustness of Named Entity Recognition Models (2021.emnlp-main)

Challenge:	Existing methods for annotating instruction data are expensive and difficult to scale.
Approach:	They propose a method to automatically build instruction data from an unlabeled corpus without heavy reliance on proprietary LLMs and human annotation.
Outcome:	The proposed method outperforms existing methods on AlpacaEval leaderboard and other open-source methods.

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Bill Yuchen Lin, Wenyang Gao, Jun Yan, Ryan Moreno, Xiang Ren

Reliably Bounding False Positives: A Zero-Shot Machine-Generated Text Detection Framework via Multiscaled Conformal Prediction (2025.acl-long)

Challenge:	Recent named entity recognition models have great performance on many conventional benchmarks, but it is not reliable in realistic applications.
Approach:	They propose a method to create natural adversarial examples using Wikidata and pre-trained language models.
Outcome:	The proposed method produces natural adversarial examples with a shifted distribution from training data.

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Xiaowei Zhu, Yubing Ren, Yanan Cao, Xixun Lin, Fang Fang, Yangxi Li

Learning Semantic Correspondences from Noisy Data-text Pairs by Local-to-Global Alignments (2020.coling-main)

Challenge:	Existing methods focus excessively on detection accuracy, neglecting the societal risks posed by high false positive rates (FPRs).
Approach:	They propose a Conformal Prediction framework that constrains the upper bound of false positive rates and introduces a real-time detection framework.
Outcome:	The proposed framework reduces false positive rates and improves detection performance.

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Feng Nie, Jinpeng Wang, Chin-Yew Lin

From Facts to Insights: A Study on the Generation and Evaluation of Analytical Reports for Deciphering Earnings Calls (2025.coling-main)

Challenge:	Existing methods for data-to-text generation use a large-scale training corpus to learn semantic correspondences between structured input data and associated texts.
Approach:	They propose a local-to-global alignment framework that uses local and global models to learn semantic correspondences from large-scale datasets.
Outcome:	The proposed framework can be generalized to restaurant and computer domains and improve alignment accuracy.

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Tomas Goldsack, Yang Wang, Chenghua Lin, Chung-Chi Chen

Challenge:	Existing studies have focused on the generation and evaluation of analytical reports derived from Earnings Calls (ECs).
Approach:	They propose to use Large Language Models to generate and evaluate analytical reports derived from Earnings Calls (ECs) they propose to introduce specialized agents that introduce diverse viewpoints and desirable topics into the report generation process.
Outcome:	The proposed model improves the quality of reports in different settings, while human-written reports remain preferred in the majority of cases.

MultiCMET: A Novel Chinese Benchmark for Understanding Multimodal Metaphor (2023.findings-emnlp)

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Dongyu Zhang, Jingwei Yu, Senyuan Jin, Liang Yang, Hongfei Lin

Simple but Challenging: Natural Language Inference Models Fail on Simple Sentences (2022.findings-emnlp)

Challenge:	Existing research on multimodal metaphors does not address categorizing the source and target domains in metaphors beyond the English language.
Approach:	They propose a Cascading Domain Knowledge Integration benchmark to detect metaphors by introducing domain-specific lexical features.
Outcome:	The proposed dataset includes 13,820 text-image pairs of advertisements with manual annotations of the occurrence of metaphors, domain categories, and sentiments metaphors convey.

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Cheng Luo, Wei Liu, Jieyu Lin, Jiajie Zou, Ming Xiang, Nai Ding

Mitigating Bias for Question Answering Models by Tracking Bias Influence (2024.naacl-long)

Challenge:	Natural language inference (NLI) tasks are difficult to perform on large datasets . a small number of simple sentences can improve model performance, authors say .
Approach:	They propose to use syntactically simple sentences to test the inference ability of NLI models.
Outcome:	The proposed set of simple sentences shows that the models fine-tuned on MNLI and SNLI perform poorly on Simple Pair.

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Mingyu Ma, Jiun-Yu Kao, Arpit Gupta, Yu-Hsiang Lin, Wenbo Zhao, Tagyoung Chung, Wei Wang, Kai-Wei Chang, Nanyun Peng

Omni-Chart-600K: A Comprehensive Dataset of Chart Types for Chart Understanding (2025.findings-naacl)

Challenge:	Existing literature observes bias in question answering (QA) models, but there is no method to mitigate it.
Approach:	They propose an approach to mitigate the bias of question answering models by observing the influence of a query instance on another instance.
Outcome:	The proposed method reduces bias level in all 9 bias categories while maintaining comparable QA accuracy.

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Shulei Wang, Shuai Yang, Wang Lin, Zirun Guo, Sihang Cai, Hai Huang, Ye Wang, Jingyuan Chen, Tao Jin

Adaptive Hyper-parameter Learning for Deep Semantic Retrieval (2023.emnlp-industry)

Challenge:	Existing chart-related training methods lack capabilities in information extraction, mathematical reasoning, and understanding of multiple chart types.
Approach:	They propose a two-stage training strategy and method for jointly training a vision encoder tailored for multi-type charts to address the deficiencies in chart types and limited scope of chart tasks in existing datasets.
Outcome:	The proposed dataset includes 21 diverse chart types and tasks, including data retrieval and mathematical reasoning.

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Mingming Li, Chunyuan Yuan, Huimu Wang, Peng Wang, Jingwei Zhuo, Binbin Wang, Lin Liu, Sulong Xu

Cool-Fusion: Fuse Large Language Models without Training (2025.acl-long)

Challenge:	Existing methods for deep semantic retrieval are highly sensitive to hyper-parameters . a novel adaptive metric learning method is proposed to overcome this limitation .
Approach:	They propose a method that adaptively obtains hyper-parameters without fixed or extra-trainable hyper-parmeters . they adopt a symmetric metric learning method to mitigate model collapse issues .
Outcome:	The proposed method outperforms existing methods on a real-world dataset and brings economic benefits.

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Cong Liu, Xiaojun Quan, Yan Pan, Weigang Wu, Xu Chen, Liang Lin

Vision-Language Model Fine-Tuning via Simple Parameter-Efficient Modification (2024.emnlp-main)

Challenge:	Cool-Fusion is a simple yet effective approach to combine two or more heterogeneous large language models .
Approach:	They propose a method that fuses the knowledge of two or more heterogeneous large language models to leverage complementary strengths.
Outcome:	The proposed method increases accuracy from three strong source LLMs on GSM8K by 17.4%.

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Ming Li, Jike Zhong, Chenxin Li, Liuzhuozheng Li, Nie Lin, Masashi Sugiyama

CodeArena: Evaluating and Aligning CodeLLMs on Human Preference (2025.emnlp-main)

Challenge:	Recent advances in fine-tuning Vision-Language Models have seen the success of prompt tuning and adapter tuning.
Approach:	They propose a method to fine-tune CLIP without introducing any overhead of extra parameters.
Outcome:	The proposed method improves CLIP by 7.27% average harmonic mean accuracy.

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Jian Yang, Jiaxi Yang, Wei Zhang, Jin Ke, Yibo Miao, Lei Zhang, Liqun Yang, Zeyu Cui, Yichang Zhang, Zhoujun Li, Binyuan Hui, Junyang Lin

The LLM Already Knows: Estimating LLM-Perceived Question Difficulty via Hidden Representations (2025.emnlp-main)

Challenge:	Code large language models (codeLLMs) focus on synthesizing the correct code snippet, ignoring the alignment with human preferences.
Approach:	They propose a benchmark code-based on 40 categories and 44 programming languages to emulate real-world coding tasks.
Outcome:	The proposed benchmarks show that open-source code LLMs perform better than open-sourced ones.

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Yubo Zhu, Dongrui Liu, Zecheng Lin, Wei Tong, Sheng Zhong, Jing Shao

Neural Retrieval for Question Answering with Cross-Attention Supervised Data Augmentation (2021.acl-short)

Challenge:	Existing methods for difficulty estimation rely on repeated response sampling, auxiliary models, or fine-tuning the target model itself.
Approach:	They propose a method that leverages only the hidden representations produced by large language models.
Outcome:	The proposed method outperforms baselines in difficulty estimation on textual and multimodal tasks and improves adaptive reasoning strategies with fewer generated tokens.

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Yinfei Yang, Ning Jin, Kuo Lin, Mandy Guo, Daniel Cer

From Synthesis to Clinical Assistance: A Strategy-Aware Agent Framework for Autism Intervention based on Real Clinical Dataset (2026.acl-long)

Challenge:	Early fusion models with cross-attention have shown better-than-human performance on some question answer benchmarks, while it is a poor fit for retrieval since it prevents pre-computation of the answer representations.
Approach:	They propose a supervised data mining method to train an efficient late fusion retrieval model by using cross-attention models with cross-references.
Outcome:	The proposed model outperforms retrieval models trained with gold annotations on Precision at N (P@N) and Mean Reciprocal Rank (MRR).

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Junhong Lai, Shuzhong Lai, Yanhao Yu, Wanlin Chen, Chenyu Yan, Haifeng Li, Lin Yao, Yueming Wang

MemSearcher: Iterative Memory Integration for Search Agent via End-to-End Reinforcement Learning (2026.findings-acl)

Challenge:	Applied Behavior Analysis (ABA) is the gold standard for clinical intervention, but large language models struggle to adhere to its standardized procedures.
Approach:	They propose a strategy-aware framework to unify high-fidelity intervention dialogue synthesis and clinical decision support.
Outcome:	Experiments show that ASDAgent achieves nearly 80% strategic consistency with human experts.

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Qianhao Yuan, Jie Lou, Zichao Li, Jiawei Chen, Yaojie Lu, Hongyu Lin, Le Sun, Debing Zhang, Xianpei Han

Self-Evolving Multi-Agent Systems via Textual Backpropagation (2026.findings-acl)

Challenge:	Recent LLM-based search agents often concatenate the full interaction history into the context, producing long and noisy inputs and increasing compute cost and memory overhead.
Approach:	They propose an agent framework that maintains a compact memory during multi-turn interactions.
Outcome:	The proposed framework outperforms strong history-concatenation (ReAct-style) baselines on a range of public datasets while maintaining nearly constant token counts across multi-turn interactions.

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Xiaowen Ma, Yunpu Ma, Chenyang Lin, Sikuan Yan, Jinhe Bi, Zixuan Cao, Yijun Tian, Volker Tresp, Hinrich Schuetze

Inference-Time Policy Adapters (IPA): Tailoring Extreme-Scale LMs without Fine-tuning (2023.emnlp-main)

Challenge:	Large Language Models (LLMs) have proven effective for addressing complex, high-dimensional tasks, but current approaches rely on static, manually engineered multi-agent configurations.
Approach:	They propose a framework that conceptualizes multi-agent collaboration as a layered neural network architecture.
Outcome:	The proposed framework surpasses leading multi-agent baselines under the same configurations, showing consistent performance improvements.

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Ximing Lu, Faeze Brahman, Peter West, Jaehun Jung, Khyathi Chandu, Abhilasha Ravichander, Prithviraj Ammanabrolu, Liwei Jiang, Sahana Ramnath, Nouha Dziri, Jillian Fisher, Bill Lin, Skyler Hallinan, Lianhui Qin, Xiang Ren, Sean Welleck, Yejin Choi

ZipVoice-Dialog: Non-Autoregressive Spoken Dialogue Generation with Flow Matching (2026.findings-acl)

Challenge:	Extreme-scale language models have shown exceptional performance on a variety of language tasks, but the degree of control offered by these models through pure prompting is limited.
Approach:	They propose an inference-time policy adapter which tailors a large base model without fine-tuning it.
Outcome:	The proposed model outperforms baseline methods on five challenging text generation tasks and even over GPT-4.

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Han Zhu, Wei Kang, Liyong Guo, Zengwei Yao, Fangjun Kuang, Weiji Zhuang, Zhaoqing Li, Zhifeng Han, Dong Zhang, Xin Zhang, Xingchen Song, Lingxuan Ye, Long Lin, Daniel Povey

Q-TOD: A Query-driven Task-oriented Dialogue System (2022.emnlp-main)

Challenge:	Existing autoregressive models for dialogue generation suffer from high latency and stability issues.
Approach:	They propose a non-autoregressive (NAR) zero-shot spoken dialogue generation model based on flow-matching.
Outcome:	The proposed model outperforms existing models in speech generation due to poor speech intelligibility and turn-taking precision.

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Xin Tian, Yingzhan Lin, Mengfei Song, Siqi Bao, Fan Wang, Huang He, Shuqi Sun, Hua Wu

GMSA: Enhancing Context Compression via Group Merging and Layer Semantic Alignment (2026.acl-long)

Challenge:	Existing pipelined task-oriented dialogue systems have difficulties adapting to unseen domains . end-to-end systems are plagued by large-scale knowledge bases in practice .
Approach:	They propose a query-driven task-oriented dialogue system that extracts dialogue context information into a natural language query.
Outcome:	The proposed system outperforms strong baselines and establishes a new state-of-the-art performance on three publicly available task-oriented dialogue datasets.

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Jiwei Tang, Zhicheng Zhang, Shunlong Wu, Jingheng Ye, Lichen Bai, Zitai Wang, Tingwei Lu, Lin Hai, Yiming Zhao, Hai-Tao Zheng, Hong-Gee Kim

Towards Knowledge Checking in Retrieval-augmented Generation: A Representation Perspective (2025.naacl-long)

Challenge:	Large Language Models (LLMs) have achieved remarkable performance across NLP tasks . however, in long-context scenarios, they face high computational cost and information redundancy.
Approach:	They propose an encoder-decoder context compression framework that generates a compact sequence of soft tokens for downstream tasks.
Outcome:	Experiments show that GMSA outperforms baselines on multiple long-context question answering and summarization benchmarks while maintaining low end-to-end latency.

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Shenglai Zeng, Jiankun Zhang, Bingheng Li, Yuping Lin, Tianqi Zheng, Dante Everaert, Hanqing Lu, Hui Liu, Hui Liu, Yue Xing, Monica Xiao Cheng, Jiliang Tang

OpenHuEval: Evaluating Large Language Model on Hungarian Specifics (2025.findings-acl)

Challenge:	Existing studies have shown that LLMs struggle to identify the boundaries of their own knowledge and tend to prioritize external information over internal knowledge learned during pre-training.
Approach:	They conduct a comprehensive analysis of LLM representation behaviors and demonstrate the significance of using representations in knowledge checking.
Outcome:	The proposed classifiers improve performance even when dealing with noisy knowledge databases.

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Haote Yang, Xingjian Wei, Jiang Wu, Noémi Ligeti-Nagy, Jiaxing Sun, Yinfan Wang, Győző Zijian Yang, Junyuan Gao, Jingchao Wang, Bowen Jiang, Shasha Wang, Nanjun Yu, Zihao Zhang, Shixin Hong, Hongwei Liu, Wei Li, Songyang Zhang, Dahua Lin, Lijun Wu, Gábor Prószéky, Conghui He

Disentangle to Decay: Linear Attention with Trainable Decay Factor (2025.coling-main)

Challenge:	Recent advances in Large Language Models (LLMs) represent significant strides toward artificial general intelligence (AGI).
Approach:	They introduce OpenHuEval, the first benchmark for LLMs focusing on the Hungarian language and specifics.
Outcome:	The framework reveals intrinsic patterns and mechanisms of LLMs in non-English languages, with Hungarian serving as an example.

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Haibo Tong, Chenyang Zhang, Jiayi Lin, Bingxuan Hou, Qingqing Hong, Junli Wang

CREAD: Combined Resolution of Ellipses and Anaphora in Dialogues (2021.naacl-main)

Challenge:	Existing linear attention models use a decay factor based positional encoding (PE), but the decay factor is manually designed and non-trainable, limiting further optimization.
Approach:	They propose a PE-based positional encoding that disentangles decay factor into two parts to achieve further optimization and stable training.
Outcome:	The proposed model achieves stable training of decay factor and improves inference efficiency in normal context and extrapolation scenarios.

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Bo-Hsiang Tseng, Shruti Bhargava, Jiarui Lu, Joel Ruben Antony Moniz, Dhivya Piraviperumal, Lin Li, Hong Yu

Curriculum-RLAIF: Curriculum Alignment with Reinforcement Learning from AI Feedback (2026.findings-acl)

Challenge:	Traditionally, anaphora resolution and ellipses resolution are limited in dialogues . despite rapid progress in dialogue systems, several difficulties remain .
Approach:	They propose a joint learning framework for modeling coreference resolution and query rewriting for complex, multi-turn dialogues.
Outcome:	The proposed model outperforms the state-of-the-art model on a rewritten dialogue dataset.

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Jiaye Lin, Mengdi Li, Xufeng Zhao, Wenhao Lu, Peilin Zhao, Stefan Wermter, Di Wang

Challenge:	Existing approaches to align large language models with human preferences are limited in generalizability due to distribution shift, preference label noise, and mismatch of challenging samples with model capacity.
Approach:	They propose a framework that constructs preference pairs with varying difficulty levels and then produces a specific curriculum for reward model training.
Outcome:	The proposed framework improves generalizability of reward models by a significant margin without incurring additional inference costs compared to existing non-curriculum baselines.

Can Vision Language Models Understand Mimed Actions? (2025.findings-acl)

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Hyundong Justin Cho, Spencer Lin, Tejas Srinivasan, Michael Saxon, Deuksin Kwon, Natali T. Chavez, Jonathan May

RecGPT: A Foundation Model for Sequential Recommendation (2025.emnlp-main)

Challenge:	Nonverbal communication (NVC) is an integral part of human language, but it has been overlooked in natural language processing research.
Approach:	They propose a multimodal multimodal recognition task that uses a corpus of mimed gestures to evaluate their understanding of NVC.
Outcome:	The proposed task is based on 86 unique gestures with perturbations applied to avatar, background, and viewpoint for evaluating recognition robustness.

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Yangqin Jiang, Xubin Ren, Lianghao Xia, Da Luo, Kangyi Lin, Chao Huang

Task-Oriented Dialogue as Dataflow Synthesis (2020.tacl-1)

Challenge:	Existing approaches fail in cold-start and cross-domain scenarios where new users or items lack sufficient interaction history.
Approach:	They propose a foundation model for sequential recommendation that achieves genuine zero-shot generalization capabilities by deriving item representations exclusively from textual features.
Outcome:	The proposed model achieves zero-shot generalization capabilities in cold-start and cross-domain scenarios.

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Jacob Andreas, John Bufe, David Burkett, Charles Chen, Josh Clausman, Jean Crawford, Kate Crim, Jordan DeLoach, Leah Dorner, Jason Eisner, Hao Fang, Alan Guo, David Hall, Kristin Hayes, Kellie Hill, Diana Ho, Wendy Iwaszuk, Smriti Jha, Dan Klein, Jayant Krishnamurthy, Theo Lanman, Percy Liang, Christopher H. Lin, Ilya Lintsbakh, Andy McGovern, Aleksandr Nisnevich, Adam Pauls, Dmitrij Petters, Brent Read, Dan Roth, Subhro Roy, Jesse Rusak, Beth Short, Div Slomin, Ben Snyder, Stephon Striplin, Yu Su, Zachary Tellman, Sam Thomson, Andrei Vorobev, Izabela Witoszko, Jason Wolfe, Abby Wray, Yuchen Zhang, Alexander Zotov

S+PAGE: A Speaker and Position-Aware Graph Neural Network Model for Emotion Recognition in Conversation (2022.aacl-main)

Challenge:	Existing approaches to task-oriented dialogue represent dialogue state as a dataflow graph . microsoft's SMCalFlow dataset features complex dialogues about events, weather, places, and people .
Approach:	They propose a dataflow graph-based dialogue agent that maps each user utterance to a program that extends this graph.
Outcome:	The proposed framework improves representability and predictability in natural dialogues . it uses dataflow graphs and metacomputation to map user intents to a program .

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Chen Liang, Jing Xu, Yangkun Lin, Chong Yang, Yongliang Wang

MAKAR: a Multi-Agent framework based Knowledge-Augmented Reasoning for Grounded Multimodal Named Entity Recognition (2025.emnlp-main)

Challenge:	Emotion recognition in conversation (ERC) is a task arousing increasing interest in many fields.
Approach:	They propose a novel GNN-based ERC model that captures speaker and position information.
Outcome:	The proposed model captures speaker and position-aware conversation structure information.

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Xinkui Lin, Yuhui Zhang, Yongxiu Xu, Kun Huang, Hongzhang Mu, Yubin Wang, Gaopeng Gou, Li Qian, Li Peng, Wei Liu, Jian Luan, Hongbo Xu

Modalities Should Be Appropriately Leveraged: Uncertainty Guidance for Multimodal Chinese Spelling Correction (2024.lrec-main)

Challenge:	Existing methods for GMNER fail to address semantic ambiguity caused by polysemy and long-tail distribution of datasets.
Approach:	They propose a framework for Grounded Multimodal Named Entity Recognition that leverages a Multimodal Large Language Model to address semantic ambiguity.
Outcome:	Extensive experiments show that the proposed framework outperforms existing methods on two benchmark datasets.

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Yongliang Lin, Zhen Zhang, Mengting Hu, Yufei Sun, Yuzhi Zhang

ChartVerse: Scaling Chart Reasoning via Reliable Programmatic Synthesis from Scratch (2026.acl-long)

Challenge:	Chinese spelling correction (CSC) aims to detect and correct spelling errors in Chinese texts.
Approach:	They propose a framework that incorporates uncertainty into feature learning and correction stages . they propose to combine the uncertainty of multimodal features with model learning .
Outcome:	The proposed framework improves on three public datasets.

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Zheng Liu, Honglin Lin, Xiaoyang Wang, Xin Gao, Yu Li, Mengzhang Cai, Yun Zhu, Zhanping Zhong, Qizhi Pei, Zhuoshi Pan, Xiaoran Shang, Conghui He, Bin Cui, Wentao Zhang, Lijun Wu

Uncovering Scaling Laws for Large Language Models via Inverse Problems (2025.findings-emnlp)

Challenge:	Existing open-source vision language models lack high-quality training data for chart reasoning . current models are simplistic and repetitive, while associated QA pairs are prone to hallucinations .
Approach:	They propose a framework to synthesize complex charts and reliable reasoning data from scratch.
Outcome:	Experimental results show that ChartVerse-8B surpasses existing models in QA and difficulty . lack of high-quality training data hampers development of open-source models .

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Arun Verma, Zhaoxuan Wu, Zijian Zhou, Xiaoqiang Lin, Zhiliang Chen, Rachael Hwee Ling Sim, Rui Qiao, Jingtan Wang, Nhung Bui, Xinyuan Niu, Wenyang Hu, Gregory Kang Ruey Lau, Zi-Yu Khoo, Zitong Zhao, Xinyi Xu, Apivich Hemachandra, See-Kiong Ng, Bryan Kian Hsiang Low

UniRAG: Universal Retrieval Augmentation for Large Vision Language Models (2025.findings-naacl)

Challenge:	Large Language Models (LLMs) have achieved remarkable success across diverse domains.
Approach:	inverse problems can efficiently uncover scaling laws that guide the building of LLMs, authors argue . authors propose brute-force approaches to improve LLM training costs due to high costs .
Outcome:	This paper advocates that inverse problems can efficiently uncover scaling laws that guide the building of LLMs to achieve the desirable performance with significantly better cost-effectiveness.

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Sahel Sharifymoghaddam, Shivani Upadhyay, Wenhu Chen, Jimmy Lin

VALU: A Benchmark for Video Anomaly Temporal Localization and Understanding at Multiple Semantic Levels (2026.acl-long)

Challenge:	Large Vision Language Models (LVLMs) have unlocked many complex use cases that require Multi-Modal (MM) understanding and MM generation.
Approach:	They propose a plug-and-play technique that adds relevant retrieved information to prompts as few-shot examples during inference.
Outcome:	The proposed method significantly improves the output quality of large vision language models when input prompts are augmented with relevant information retrieved by Vision-Language retrievers like UniRAG.

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Yixiao He, Menghao Zhang, Haifeng Sun, Jing Wang, Kangheng Lin, Jinghan Wang, Chenye Xu, Pengfei Ren, Qi Qi, Jingyu Wang

LLM as Effective Streaming Processor: Bridging Streaming-Batch Mismatches with Group Position Encoding (2025.findings-acl)

Challenge:	Recent advances in Video Large Language Models (Video-LLMs) enhance the ability of VAU models to describe and interpret anomalies.
Approach:	They propose a benchmark that explicitly defines anomalies across five semantic levels and provides detailed temporal boundaries and detailed textual descriptions for each.
Outcome:	The proposed benchmark defines anomalies across five semantic levels and provides detailed descriptions for each.

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Junlong Tong, Jinlan Fu, Zixuan Lin, Yingqi Fan, Anhao Zhao, Hui Su, Xiaoyu Shen

Challenge:	Existing methods for adapting LLMs to streaming rely on expensive re-encoding or limited scalability.
Approach:	They propose a group position encoding paradigm built on batch architectures to enhance consistency between streaming and batch modes.
Outcome:	The proposed method outperforms existing methods on cross-lingual and cross-modal tasks.

MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms (N19-1)

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Aida Amini, Saadia Gabriel, Shanchuan Lin, Rik Koncel-Kedziorski, Yejin Choi, Hannaneh Hajishirzi

Personality-aware Student Simulation for Conversational Intelligent Tutoring Systems (2024.emnlp-main)

Challenge:	Existing datasets in this domain do not offer precise operational annotations over diverse problem types due to noise and lack of formal operation-based representations.
Approach:	They propose a representation language to map problems to their operation programs . they also introduce an interpretable neural math problem solver .
Outcome:	The proposed model outperforms baseline models and the AQUA-RAT dataset on the AQuA-rat dataset.

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Zhengyuan Liu, Stella Yin, Geyu Lin, Nancy Chen

Privacy in Action: Towards Realistic Privacy Mitigation and Evaluation for LLM-Powered Agents (2025.findings-emnlp)

Challenge:	Existing large language models (LLMs) can be adopted as tutoring agents for math and language learning.
Approach:	They propose a framework to construct profiles of different student groups by refining and integrating both cognitive and noncognitive aspects, and leverage LLMs for personality-aware student simulation in a language learning scenario.
Outcome:	The proposed framework can construct profiles of different student groups by refining and integrating both cognitive and noncognitive aspects, and leverage LLMs for personality-aware student simulation in a language learning scenario.

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Shouju Wang, Fenglin Yu, Xirui Liu, Xiaoting Qin, Jue Zhang, Qingwei Lin, Dongmei Zhang, Saravan Rajmohan

RepoAgent: An LLM-Powered Open-Source Framework for Repository-level Code Documentation Generation (2024.emnlp-demo)

Challenge:	Existing benchmarks for privacy performance of LLM agents are limited to static, simplified scenarios.
Approach:	They propose a model-agnostic, contextual integrity based mitigation approach that effectively reduces privacy leakage from 36.08% to 7.30% on DeepSeek-R1 and from 33.06% to 8.32% on GPT-4o.
Outcome:	The proposed approach reduces privacy leakage from 36.08% to 7.30% on DeepSeek-R1 and from 33.06% to 8.32% on GPT-4o while preserving task helpfulness.

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Qinyu Luo, Yining Ye, Shihao Liang, Zhong Zhang, Yujia Qin, Yaxi Lu, Yesai Wu, Xin Cong, Yankai Lin, Yingli Zhang, Xiaoyin Che, Zhiyuan Liu, Maosong Sun

ROSE: A Reward-Oriented Data Selection Framework for LLM Task-Specific Instruction Tuning (2025.findings-emnlp)

Challenge:	Xia et al., 2018) demonstrate that a large language model can generate and maintain high-quality code documentation.
Approach:	They propose a large language model powered open-source framework for generating, maintaining, and updating code documentation.
Outcome:	The proposed framework generates high-quality documentation for the entire project.

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Yang Wu, Huayi Zhang, Yizheng Jiao, Lin Ma, Xiaozhong Liu, Jinhong Yu, Dongyu Zhang, Dezhi Yu, Wei Xu

Hierarchical Topic Modeling via Contrastive Learning and Hyperbolic Embedding (2024.lrec-main)

Challenge:	Prevailing methods for task-specific instruction tuning use similarity metrics to select training data . but instruction tuning loss often fails to exhibit a monotonic relationship with actual task performance .
Approach:	They propose a task-specific instruction tuning method that leverages pairwise preference loss as a reward signal.
Outcome:	The proposed method surpasses state-of-the-art methods for task-specific instruction tuning.

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Zhicheng Lin, HeGang Chen, Yuyin Lu, Yanghui Rao, Hao Xu, Hanjiang Lai

SingaKids: A Multilingual Multimodal Dialogic Tutor for Language Learning (2025.acl-industry)

Challenge:	Existing hierarchical topic models are based on Euclidean space, which cannot retain the hierarchically semantic information in the corpus, leading to irrational structure of the generated topics.
Approach:	They propose a novel hierarchical topic model that uses contrastive learning to capture information from documents.
Outcome:	The proposed model performs on topic coherence and topic diversity, and on the rationality of the topic hierarchy.

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Zhengyuan Liu, Geyu Lin, Hui Li Tan, Huayun Zhang, Yanfeng Lu, Xiaoxue Gao, Stella Xin Yin, Sun He, Hock Huan Goh, Lung Hsiang Wong, Nancy F. Chen

Self-supervised Meta-Prompt Learning with Meta-Gradient Regularization for Few-shot Generalization (2023.findings-emnlp)

Challenge:	Empirical studies show that SingaKids provides effective dialogic teaching, benefiting learners at different performance levels.
Approach:	They propose a dialogic tutor designed to facilitate language learning through picture description tasks.
Outcome:	Empirical studies show that SingaKids provides effective dialogic teaching, benefiting learners at different performance levels.

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Kaihang Pan, Juncheng Li, Hongye Song, Jun Lin, Xiaozhong Liu, Siliang Tang

Neural Chain-of-Thought Search: Searching the Optimal Reasoning Path to Enhance Large Language Models (2026.findings-acl)

Challenge:	Existing methods for prompt tuning can overfit to few-shot training samples, causing overfitting . authors propose a new framework for prompt learning with supervised meta-learning .
Approach:	They propose a self-supervised meta-prompt learning framework with MEta-gradient Regularization for few-shot generalization that leverages self-recognized meta-learning with a diverse set of meta-tasks to learn a universal prompt initialization using only unlabeled data.
Outcome:	The proposed framework learns a universal prompt initialization for efficient adaptation using only unlabeled data.

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Guoming Ling, Zhongzhan Huang, Yupei Lin, Junxin Li, Shanshan Zhong, Hefeng Wu, Liang Lin

mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval (2024.emnlp-industry)

Challenge:	Recent research indicates that Large Reasoning Models suffer from a strategic bottleneck at reasoning path planning.
Approach:	They propose a framework that reformulates reasoning as a dynamic search for the optimal thinking strategy.
Outcome:	The proposed framework improves accuracy and computational cost while reducing generation length by over 22%.

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Xin Zhang, Yanzhao Zhang, Dingkun Long, Wen Xie, Ziqi Dai, Jialong Tang, Huan Lin, Baosong Yang, Pengjun Xie, Fei Huang, Meishan Zhang, Wenjie Li, Min Zhang

A Simple and Effective Approach to Robust Unsupervised Bilingual Dictionary Induction (2020.coling-main)

Challenge:	Existing models for text retrieval are based on a multi-stage process that involves retrieving documents from a large corpus.
Approach:	They propose to build a multilingual text representation model and a cross-encoder reranker from scratch for text retrieval.
Outcome:	The proposed models outperform the state-of-the-art models on long-context retrieval benchmarks.

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Yanyang Li, Yingfeng Luo, Ye Lin, Quan Du, Huizhen Wang, Shujian Huang, Tong Xiao, Jingbo Zhu

Platforms for Non-speakers Annotating Names in Any Language (P18-4)

Challenge:	Recent work has questioned the robustness of unsupervised bilingual dictionary induction methods on distant language pairs.
Approach:	They propose an iterative dimension reduction method to bridge this gap . they propose a method that initializes and self-learning and inducing a dictionary .
Outcome:	The proposed method achieves 13.64 55.53% accuracy between English and four distant languages.

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Ying Lin, Cash Costello, Boliang Zhang, Di Lu, Heng Ji, James Mayfield, Paul McNamee

Challenge:	Traditionally, native speakers of a language have been asked to annotate a corpus in that language.
Approach:	They propose two annotation platforms that allow an English speaker to annotate names for any language without knowing the language.
Outcome:	The proposed annotations achieved state-of-the-art performance on two surprise languages and ten languages at TAC-KBP EDL2017.

Jailbreak Open-Sourced Large Language Models via Enforced Decoding (2024.acl-long)

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Hangfan Zhang, Zhimeng Guo, Huaisheng Zhu, Bochuan Cao, Lu Lin, Jinyuan Jia, Jinghui Chen, Dinghao Wu

CrowdAgent: Multi-Agent Managed Multi-Source Annotation System (2025.emnlp-demos)

Challenge:	Existing studies show that Large Language Models can be misused to generate undesired content.
Approach:	They propose to use large language models to manipulate the generation process to generate undesired content without heavy computations or prompt designs.
Outcome:	The proposed method shows that open-sourced large language models could be misused to generate undesired content without heavy computations or prompt designs.

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Maosheng Qin, Renyu Zhu, Mingxuan Xia, null Chenchenkai, Zhen Zhu, Minmin Lin, Junbo Zhao, Lu Xu, Changjie Fan, Runze Wu, Haobo Wang

Beyond Linguistic Cues: Fine-grained Conversational Emotion Recognition via Belief-Desire Modelling (2024.lrec-main)

Challenge:	Recent approaches to annotate data focus on labeling, but lack holistic process control . a novel system that integrates task assignment, data annotation, and quality/cost management is needed .
Approach:	They propose a multi-agent system that integrates task assignment, data annotation, and quality/cost management.
Outcome:	The proposed system automates human management by using a collaborative multi-agent system.

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Bo Xu, Longjiao Li, Wei Luo, Mehdi Naseriparsa, Zhehuan Zhao, Hongfei Lin, Feng Xia

OD-RTE: A One-Stage Object Detection Framework for Relational Triple Extraction (2023.acl-long)

Challenge:	Emotion recognition in conversation (ERC) is essential for dialogue systems to identify the emotions expressed by speakers.
Approach:	They propose a method that incorporates both belief and desire to accurately identify emotions by extracting emotion-eliciting events from utterances and construct graphs that represent beliefs and desires in conversations.
Outcome:	The proposed model outperforms existing models on four popular ERC datasets and validates its performance with multiple state-of-the-art models.

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Jinzhong Ning, Zhihao Yang, Yuanyuan Sun, Zhizheng Wang, Hongfei Lin

Benchmarking Commercial Intent Detection Services with Practice-Driven Evaluations (2021.naacl-industry)

Challenge:	Existing pipelines for relational triple extraction are underutilizing regional information of triple.
Approach:	They propose a one-stage Object Detection framework for Relational Triple Extraction . framework uses vertices-based bounding box detection and global relational triple region detection .
Outcome:	The proposed framework could extract all types of triples on two widely used datasets.

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Haode Qi, Lin Pan, Atin Sood, Abhishek Shah, Ladislav Kunc, Mo Yu, Saloni Potdar

CipherBank: Exploring the Boundary of LLM Reasoning Capabilities through Cryptography Challenge (2025.findings-acl)

Challenge:	Intent detection models require large amounts of labeled data to achieve high accuracy, and in practical scenarios it is more common to find small, unbalanced, and noisy datasets.
Approach:	They benchmark intent detection methods on a variety of datasets and found that Watson Assistant's model outperforms other commercial solutions.
Outcome:	The proposed model outperforms pretrained language models on a variety of datasets while requiring only a fraction of computational resources and training data.

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Yu Li, Qizhi Pei, Mengyuan Sun, Honglin Lin, Chenlin Ming, Xin Gao, Jiang Wu, Conghui He, Lijun Wu

Effective Distillation of Table-based Reasoning Ability from LLMs (2024.lrec-main)

Challenge:	Large language models (LLMs) have demonstrated remarkable capabilities, but their capabilities in cryptographic decryption tasks remain underexplored.
Approach:	They propose a benchmark to evaluate the reasoning capabilities of large language models in cryptographic decryption tasks.
Outcome:	The proposed benchmark examines the reasoning capabilities of large language models in cryptographic decryption tasks.

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Bohao Yang, Chen Tang, Kun Zhao, Chenghao Xiao, Chenghua Lin

A Semi-supervised Scalable Unified Framework for E-commerce Query Classification (2025.acl-industry)

Challenge:	Existing work on table-based reasoning distillation has focused on smaller models with limited performance.
Approach:	They propose a table-based reasoning distillation approach to distill LLMs into smaller models . their results show that a 220 million parameter model fine-tuned using distilled data improves performance .
Outcome:	The proposed model improves on a scientific table-to-text generation dataset and surpasses specific LLMs.

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Chunyuan Yuan, Chong Zhang, Zhen Fang, Ming Pang, Xue Jiang, Changping Peng, Zhangang Lin, Ching Law

Coreferential Reasoning Learning for Language Representation (2020.emnlp-main)

Challenge:	Existing query classification methods rely on posterior click behavior to construct training samples, resulting in insufficient prior information for modeling.
Approach:	They propose a semi-supervised scaleable unified framework that integrates enhanced modules to unify query classification tasks.
Outcome:	The proposed framework outperforms the state-of-the-art models in offline and online A/B experiments.

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Deming Ye, Yankai Lin, Jiaju Du, Zhenghao Liu, Peng Li, Maosong Sun, Zhiyuan Liu

On LLM-Based Scientific Inductive Reasoning Beyond Equations (2025.emnlp-main)

Challenge:	Existing language representation models cannot explicitly handle coreference, which is essential to the coherent understanding of the whole discourse.
Approach:	They propose a language representation model that captures coreferential relations in context.
Outcome:	The proposed model can achieve significant improvements on downstream NLP tasks while maintaining comparable performance to baseline models on other common NLP task.

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Brian S. Lin, Jiaxin Yuan, Zihan Zhou, Shouli Wang, Shuo Wang, Cunliang Kong, Qi Shi, Yuxuan Li, Liner Yang, Zhiyuan Liu, Maosong Sun

Toward Inclusive Language Models: Sparsity-Driven Calibration for Systematic and Interpretable Mitigation of Social Biases in LLMs (2025.findings-emnlp)

Challenge:	Existing research on inductive reasoning models emphasizes rule design without grounding them in specific scenarios.
Approach:	They propose to use LLMs to learn underlying patterns from limited examples in entirely new environments.
Outcome:	The proposed benchmark evaluates the inductive reasoning abilities of large language models in scientific settings.

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Prommy Sultana Hossain, Chahat Raj, Ziwei Zhu, Jessica Lin, Emanuela Marasco

Turning the Tide: Repository-based Code Reflection (2025.findings-emnlp)

Challenge:	a new method to mitigate stereotypical bias in large language models is needed . inherent biases from training on vast Internet datasets can amplify harmful stereotypes .
Approach:	They propose a method to identify stereotypical bias in decoder-only transformer models . they apply a localization mechanism that correlates internal activations with a new Context Influence score .
Outcome:	The proposed method reduces stereotypical biases on BBQ, StereoSet, and CrowS-Pairs while improving reasoning performance on MMLU by 10%.

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Wei Zhang, Jian Yang, Jiaxi Yang, Ya Wang, Zhoujun Li, Zeyu Cui, Binyuan Hui, Junyang Lin

CmdCaliper: A Semantic-Aware Command-Line Embedding Model and Dataset for Security Research (2024.emnlp-main)

Challenge:	Code large language models (LLMs) enhance programming by understanding and generating code across languages.
Approach:	a new benchmark evaluates code understanding and generation in repositories using code large language models.
Outcome:	The proposed model improves code understanding and generation in repositories by evaluating 1,888 test cases across 6 programming languages.

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Sian-Yao Huang, Cheng-Lin Yang, Che-Yu Lin, Chun-Ying Huang

Aggregating Bidirectional Encoder Representations Using MatchLSTM for Sequence Matching (D19-1)

Challenge:	Currently, command-line embeddings are limited due to the lack of comprehensive datasets for the field due to privacy and regulation concerns.
Approach:	They propose a command-line embedding model called CmdCaliper for training and unbiased evaluation using a set of large language models comprising 28,520 similar command- line pairs.
Outcome:	The proposed model suppresses state-of-the-art sentences with ten times more parameters across various tasks.

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Bo Shao, Yeyun Gong, Weizhen Qi, Nan Duan, Xiaola Lin

Skeleton-Guided-Translation: A Benchmarking Framework for Code Repository Translation with Fine-Grained Quality Evaluation (2025.findings-emnlp)

Challenge:	Recent work on text sequence matching tasks uses task specific supervised datasets, which are always limited to the amount due to the cost of annotation.
Approach:	They propose an aggregation method to combine Bidirectional Encoder Representations from Transformer (BERT) with a MatchLSTM layer for Sequence Matching.
Outcome:	The proposed model improves on two publicly available datasets, WikiQA and SNLI.

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Xing Zhang, Jiaheng Wen, Fangkai Yang, Yu Kang, Pu Zhao, Junhao Wang, Maoquan Wang, Yufan Huang, Shengyu Fu, Elsie Nallipogu, Qingwei Lin, Yingnong Dang, Saravan Rajmohan, Dongmei Zhang

Exploring Mode Connectivity for Pre-trained Language Models (2022.emnlp-main)

Challenge:	Existing code translation benchmarks focus on individual functions, overlooking repository-level challenges like intermodule coherence and dependency management.
Approach:	They propose a framework for benchmarking Java-to-C# translation at the repository level . it uses a translation framework guided by skeletons and fine-grained quality evaluation .
Outcome:	The proposed framework improves Java-to-C# translation quality at the repository level.

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Yujia Qin, Cheng Qian, Jing Yi, Weize Chen, Yankai Lin, Xu Han, Zhiyuan Liu, Maosong Sun, Jie Zhou

Benchmarking LLM Faithfulness in RAG with Evolving Leaderboards (2025.emnlp-industry)

Challenge:	Recent years have witnessed the prevalent application of pre-trained language models (PLMs) in NLP. From the perspective of parameter space, PLMs provide generic initialization, starting from which high-performance minima could be found.
Approach:	They investigate the geometric connections of different minima through the lens of mode connectivity, which measures whether two minima can be connected with a low-loss path.
Outcome:	The proposed model can be used to find low-loss paths between two minima, and to understand how their mode connectivity affects their task knowledge.

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Manveer Singh Tamber, Forrest Sheng Bao, Chenyu Xu, Ge Luo, Suleman Kazi, Minseok Bae, Miaoran Li, Ofer Mendelevitch, Renyi Qu, Jimmy Lin

MedicalSum: A Guided Clinical Abstractive Summarization Model for Generating Medical Reports from Patient-Doctor Conversations (2022.findings-emnlp)

Challenge:	Large language models (LLMs) excel in various tasks, but often produce hallucinations . retrieved contexts, misrepresent information, or generate outright contradictions .
Approach:	They propose a framework that measures hallucination faithfulness of large language models . they introduce a leaderboard that leverages diverse human-annotated hallucinian examples .
Outcome:	The proposed framework improves hallucination evaluations by leveraging human-annotated examples.

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George Michalopoulos, Kyle Williams, Gagandeep Singh, Thomas Lin

Packed Levitated Marker for Entity and Relation Extraction (2022.acl-long)

Challenge:	Existing models for summarizing medical conversations do not take clinical knowledge into account and are difficult to control.
Approach:	They propose a transformer-based sequence-to-sequence architecture for summarizing medical conversations by integrating medical domain knowledge from the Unified Medical Language System (UMLS).
Outcome:	The proposed model achieves state-of-the-art ROUGE score improvements of 0.8-2.1 points (including 6.2% error reduction in the PE section) it incorporates medical domain knowledge from the Unified Medical Language System (UMLS).

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Deming Ye, Yankai Lin, Peng Li, Maosong Sun

The African Languages Lab: A Collaborative Approach to Advancing Low-Resource African NLP (2026.acl-long)

Challenge:	Existing work on entity and relation extraction ignores the interrelation between spans . a novel approach to extract better span representations from pre-trained languages is needed .
Approach:	They propose a span representation approach that packs Levitated Markers to consider interrelation between spans.
Outcome:	The proposed model improves on baselines on six NER benchmarks and achieves a 4.1%-4.3% strict relation F1 improvement with higher speed over previous state-of-the-art models.

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Sheriff Issaka, Keyi Wang, Yinka Ajibola, Oluwatumininu Samuel-Ipaye, Zhaoyi Zhang, Nicte Aguillon Jimenez, Evans Kofi Agyei, Abraham Lin, Rohan Ramachandran, Sadick Abdul Mumin, Faith Nchifor, Mohammed Shuraim Issah, Erick Rosas Gonzalez, Lieqi Liu, Sylvester Kpei, Jemimah Kusi Osei, Carlene Ajeneza, Persis Boateng, Prisca Adwoa Dufie Yeboah, Saadia Gabriel

A Chinese Dataset for Evaluating the Safeguards in Large Language Models (2024.findings-acl)

Challenge:	Among the approximately 7,000 languages spoken globally, fewer than 20 receive substantial attention in NLP research.
Approach:	They propose to use African multi-modal speech and text data to validate African multimodal models and validate them on targeted language data.
Outcome:	The African Languages Lab's results show that the proposed model outperforms untrained models in 31 languages and a 1B-parameter model beats the commercial system in Yoruba and Twi.

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Yuxia Wang, Zenan Zhai, Haonan Li, Xudong Han, Shom Lin, Zhenxuan Zhang, Angela Zhao, Preslav Nakov, Timothy Baldwin

Knowledge Inheritance for Pre-trained Language Models (2022.naacl-main)

Challenge:	a recent study has shown that large language models can produce harmful responses, exposing users to unexpected risks.
Approach:	They propose a dataset for the safety evaluation of Chinese LLMs in Mandarin Chinese . they extend the dataset to better identify false negative and false positive examples .
Outcome:	The proposed dataset is for the safety evaluation of Chinese LLMs, and is based on a Chinese dataset.

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Yujia Qin, Yankai Lin, Jing Yi, Jiajie Zhang, Xu Han, Zhengyan Zhang, Yusheng Su, Zhiyuan Liu, Peng Li, Maosong Sun, Jie Zhou

Split-Merge: Scalable and Memory-Efficient Merging of Expert LLMs (2025.emnlp-main)

Challenge:	Existing large-scale pre-trained language models are mainly trained from scratch individually, ignoring that many well-taught PLMs are available.
Approach:	They propose a pre-training framework called knowledge inheritance and propose auxiliary supervision to efficiently learn larger PLMs.
Outcome:	The proposed framework can be used to train large-scale language models with huge parameters and a large dataset can be adapted to domain adaptation and knowledge transfer.

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Sruthi Gorantla, Aditya Rawal, Devamanyu Hazarika, Kaixiang Lin, Mingyi Hong, Mahdi Namazifar

PTD-SQL: Partitioning and Targeted Drilling with LLMs in Text-to-SQL (2024.emnlp-main)

Challenge:	a zero-shot merging framework for large language models consolidates specialized domain experts into a single model without any further training.
Approach:	They propose a zero-shot merging framework that consolidates specialized domain experts into a single model without further training.
Outcome:	Experiments on code generation, mathematical reasoning, medical question answering, and instruction-following benchmarks confirm the versatility and effectiveness of the proposed framework.

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Ruilin Luo, Liyuan Wang, Binghuai Lin, Zicheng Lin, Yujiu Yang

StoryAnalogy: Deriving Story-level Analogies from Large Language Models to Unlock Analogical Understanding (2023.emnlp-main)

Challenge:	Large Language Models (LLMs) are powerful tools for Text-to-SQL tasks . SQL solutions have a relatively fixed pattern, allowing for categorical thinking .
Approach:	They propose that query group partitioning allows LLMs to focus on learning the thought processes specific to a single problem type, thus enhancing their reasoning abilities across diverse difficulty levels and problem categories.
Outcome:	The proposed model outperforms state-of-the-art models on the Spider and BIRD datasets.

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Cheng Jiayang, Lin Qiu, Tsz Chan, Tianqing Fang, Weiqi Wang, Chunkit Chan, Dongyu Ru, Qipeng Guo, Hongming Zhang, Yangqiu Song, Yue Zhang, Zheng Zhang

Direct Behavior Optimization: Unlocking the Potential of Lightweight LLMs (2025.findings-acl)

Challenge:	Analogy-making between narratives is crucial for human reasoning . despite its importance, there has been limited research on story analogies .
Approach:	They construct a large-scale story-level analogy corpus with 24K story pairs . they find that the tasks are incredibly difficult for large language models such as ChatGPT .
Outcome:	The proposed corpus contains 24K story pairs from diverse domains with human annotations on two similarities from the extended Structure-Mapping Theory.

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Hongming Yang, Shi Lin, Jun Shao, Changting Lin, Donghai Zhu, Meng Han, Qinglei Kong

An Investigation of Evaluation Methods in Automatic Medical Note Generation (2023.findings-acl)

Challenge:	Existing prompt optimization methods rely on extensive manual effort or meta-cognitive abilities, making them less effective for LwLLMs.
Approach:	They propose a direct behavior optimization parameter that transforms the optimization of complex prompts into discrete, quantifiable execution sequences using a gradient-free Monte Carlo Tree Search.
Outcome:	The proposed method outperforms current prompt optimization methods on seven challenging tasks where state-of-the-art LLMs excel but LwLLMs generally underperform.

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Asma Ben Abacha, Wen-wai Yim, George Michalopoulos, Thomas Lin

Learning Language Specific Sub-network for Multilingual Machine Translation (2021.acl-long)

Challenge:	Recent studies show that doctors can save significant amounts of time when using automatic note generation.
Approach:	They propose task-specific metrics for automatic note generation from medical conversation summarization and generation, including knowledge-graph embedding-based metrics, customized model-based measures with domain-specific weights, and ensemble metrics.
Outcome:	The proposed evaluation metrics are compared to existing models and can have different behaviors on different types of clinical notes datasets.

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Zehui Lin, Liwei Wu, Mingxuan Wang, Lei Li

MAVEN: A Massive General Domain Event Detection Dataset (2020.emnlp-main)

Challenge:	Multilingual neural machine translation models suffer from performance degradation when learning multiple languages.
Approach:	They propose to use LaSS to jointly train a single unified multilingual MT model.
Outcome:	The proposed model gains on 36 language pairs by up to 1.2 BLEU and zero-shot translation with 8.3 BLUE on 30 language pairs.

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Xiaozhi Wang, Ziqi Wang, Xu Han, Wangyi Jiang, Rong Han, Zhiyuan Liu, Juanzi Li, Peng Li, Yankai Lin, Jie Zhou

TableDreamer: Progressive and Weakness-guided Data Synthesis from Scratch for Table Instruction Tuning (2025.findings-acl)

Challenge:	Existing datasets exhibit data scarcity and limited coverage of general-domain events.
Approach:	They present a MAssive eVENt detection dataset which contains 4,480 Wikipedia documents and 168 event types.
Outcome:	The proposed dataset shows that existing methods cannot achieve promising results on the small datasets.

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Mingyu Zheng, Zhifan Feng, Jia Wang, Lanrui Wang, Zheng Lin, Hao Yang, Weiping Wang

Unified Demonstration Retriever for In-Context Learning (2023.acl-long)

Challenge:	Existing methods for table instruction tuning are limited due to limited data diversity and lack of data quality.
Approach:	They propose a weakness-guided data synthesis framework for table instruction tuning that explores the vast input space of table understanding tasks and then iterates through the input space.
Outcome:	The proposed framework boosts the average accuracy of Llama3.1-8B-instruct by 11.62% with 27K GPT-4o synthetic data and outperforms state-of-the-art data synthesis baselines which use more training data.

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Xiaonan Li, Kai Lv, Hang Yan, Tianyang Lin, Wei Zhu, Yuan Ni, Guotong Xie, Xiaoling Wang, Xipeng Qiu

On the Rigour of Scientific Writing: Criteria, Analysis, and Insights (2024.findings-emnlp)

Challenge:	In-context learning is a new learning paradigm where a language model conditions on a few input-output pairs (demonstrations) and a test input, and directly outputs the prediction.
Approach:	They propose a single model to retrieve demonstrations for a wide range of tasks by combining training signals from various tasks into a unified list-wise ranking formulation by language model’s feedback.
Outcome:	The proposed model outperforms baselines on 30+ tasks across 13 task families and multiple data domains.

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Joseph James, Chenghao Xiao, Yucheng Li, Chenghua Lin

Role-Sensitive Neurons: A Neuron-Level Gain Control Mechanism for Confidence Steering (2026.findings-acl)

Challenge:	despite its importance, little work exists on modelling rigour in scientific writing . despite widespread use of term, scientific literature lacks definition of rigor .
Approach:	They propose a framework to automatically identify and define rigour criteria and assess their relevance in scientific writing.
Outcome:	The proposed framework can be tailored to the evaluation of scientific rigour for different areas.

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Peiwen Huang, Chih-Hao Hsu, Tzu-Hung Huang, Shou-De Lin

Challenge:	Large language models (LLMs) exhibit striking behavioral flexibility.
Approach:	They propose to identify a sparse sub-network of Role-Sensitive Neurons (RSNs) that governs the transition from hesitation to action.
Outcome:	The proposed framework allows precise regulation of abstention behavior by intervention on this subspace.

Executing Natural Language-Described Algorithms with Large Language Models: An Investigation (2024.lrec-main)

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Xin Zheng, Qiming Zhu, Hongyu Lin, Yaojie Lu, Xianpei Han, Le Sun

Knowledge-Guided Dynamic Modality Attention Fusion Framework for Multimodal Sentiment Analysis (2024.findings-emnlp)

Challenge:	Recent advances in large language models (LLMs) have revolutionized the field of natural language processing and artificial intelligence, creating new SOTAs and reaching human-level language understanding performance on a series of tasks and benchmarks.
Approach:	They propose to use an algorithm test set sourced from Introduction to Algorithm to assess LLMs' code execution abilities.
Outcome:	The proposed model can execute programs described in natural language as long as no heavy numeric computation is involved.

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Xinyu Feng, Yuming Lin, Lihua He, You Li, Liang Chang, Ya Zhou

It’s Not Bragging If You Can Back It Up: Can LLMs Understand Braggings? (2025.acl-long)

Challenge:	Existing methods focus on equally treating the contribution of each modality or statically using text as the dominant modality to conduct interaction, which neglects the situation where each modal may become dominant.
Approach:	They propose a Knowledge-Guided Dynamic Modality Attention Fusion Framework (KuDA) that uses sentiment knowledge to guide the model dynamically selecting the dominant modality and adjusting the contributions of each modality.
Outcome:	The proposed model can be used to highlight the contribution of dominant modality through the correlation evaluation loss.

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Jingjie Zeng, Huayang Li, Liang Yang, Yuanyuan Sun, Hongfei Lin

Challenge:	Bragging is a pervasive social-linguistic phenomenon that reflects complex human interaction patterns.
Approach:	They propose to use bragging recognition, bragging explanation, and bragging generation tasks to examine bragging in large language models (LLMs) .
Outcome:	The proposed models can identify bragging intent, social appropriateness, and account for context sensitivity and provide new insights into how LLMs process bragging.

VisDiaHalBench: A Visual Dialogue Benchmark For Diagnosing Hallucination in Large Vision-Language Models (2024.acl-long)

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Qingxing Cao, Junhao Cheng, Xiaodan Liang, Liang Lin

CodeReviewQA: The Code Review Comprehension Assessment for Large Language Models (2025.findings-acl)

Challenge:	Despite the significant success of large vision-language models, some studies have revealed that LVLMs suffer from the hallucination problem when given long-term misleading textual history.
Approach:	They propose a visual dialogue hallucination evaluation benchmark VisDiaHalBench to investigate the halluciation problem of large vision-language models when given long-term misleading textual history.
Outcome:	The proposed benchmark consists of samples with five-turn questions about an edited image and its original version.

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Hong Yi Lin, Chunhua Liu, Haoyu Gao, Patanamon Thongtanunam, Christoph Treude

Importance of Synthesizing High-quality Data for Text-to-SQL Parsing (2023.findings-acl)

Challenge:	State-of-the-art large language models (LLMs) have demonstrated impressive code generation capabilities but struggle with real-world software engineering tasks such as revising source code to address code reviews.
Approach:	They propose a benchmark to evaluate large language models' ability to bridge both technical and conversational contexts by decomposing the generation task of code refinement into three essential reasoning steps.
Outcome:	The proposed benchmark exposes specific model weaknesses in code review comprehension disentangled from their generative automated code refinement results.

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Yiqun Hu, Yiyun Zhao, Jiarong Jiang, Wuwei Lan, Henghui Zhu, Anuj Chauhan, Alexander Hanbo Li, Lin Pan, Jun Wang, Chung-Wei Hang, Sheng Zhang, Jiang Guo, Mingwen Dong, Joseph Lilien, Patrick Ng, Zhiguo Wang, Vittorio Castelli, Bing Xiang

English as Defense Proxy: Mitigating Multilingual Jailbreak via Eliciting English Safety Knowledge (2025.findings-emnlp)

Challenge:	Existing text-to-SQL parsers lack the data to perform well with augmented synthetic data.
Approach:	They propose a framework that imposes strong typing constraints and incorporates key relationships from schema.
Outcome:	The proposed framework improves on the high-quality synthesized SQL and natural language question (NLQ) models have significant accuracy boosts and achieve new state-of-the-art performance on spider.

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Zekai Zhang, Yiduo Guo, Jiuheng Lin, Shanghaoran Quan, Huishuai Zhang, Dongyan Zhao

TAVT: Towards Transferable Audio-Visual Text Generation (2023.acl-long)

Challenge:	Large language models excel in many tasks, but their safety guarantees vary by language.
Approach:	They propose a unified approach that leverages English as a universal safety anchor.
Outcome:	The proposed approach leverages English as defense proxy (E-Proxy) to transfer safety knowledge across languages.

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Wang Lin, Tao Jin, Wenwen Pan, Linjun Li, Xize Cheng, Ye Wang, Zhou Zhao

MAVEN-ARG: Completing the Puzzle of All-in-One Event Understanding Dataset with Event Argument Annotation (2024.acl-long)

Challenge:	Existing transfer learning techniques focus on uni-modal analysis and lack consideration of multi-modal content and cross-modal relation.
Approach:	They propose a transferable audio-visual text generation framework that incorporates two components: Audio-Visual Meta-Mapper and Dual Counterfactual Contrastive Learning.
Outcome:	The proposed framework outperforms the state-of-the-art methods across multiple domains and modal settings.

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Xiaozhi Wang, Hao Peng, Yong Guan, Kaisheng Zeng, Jianhui Chen, Lei Hou, Xu Han, Yankai Lin, Zhiyuan Liu, Ruobing Xie, Jie Zhou, Juanzi Li

Fine-grained Pluggable Gradient Ascent for Knowledge Unlearning in Language Models (2024.emnlp-main)

Challenge:	Existing datasets for event understanding have limited coverage due to complexity of tasks.
Approach:	They propose a dataset that augments MAVEN datasets with event argument annotations . they propose 98,591 events and 290,613 arguments obtained with laborious human annotation .
Outcome:	The proposed dataset is the first all-in-one dataset supporting event detection, event argument extraction, and event relation extraction.

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XiaoHua Feng, Chaochao Chen, Yuyuan Li, Zibin Lin

ToolRM: Towards Agentic Tool-Use Reward Modeling (2026.findings-acl)

Challenge:	Existing studies on knowledge unlearning focus on computer vision but extend their exploration to other fields.
Approach:	They propose an adaptive objective that calculates gradients with fine-grained control specifically targeting sensitive tokens.
Outcome:	The proposed method improves the general ability of language models while achieving knowledge unlearning.

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Renhao Li, Jianhong Tu, Yang Su, Yantao Liu, Fei Huang, Hamid Alinejad-Rokny, Derek F. Wong, Junyang Lin, Min Yang

Challenge:	lack of reliable reward models for tool-use tasks has limited progress toward agentic AI . recent advances in agentic artificial intelligence are driven by tool-using capabilities of large language models.
Approach:	They propose a pipeline that constructs pairwise preference data using rule-based scoring and multidimensional sampling to build lightweight reward models.
Outcome:	The proposed model outperforms existing models on tool calling tasks with higher accuracy.

ToolBeHonest: A Multi-level Hallucination Diagnostic Benchmark for Tool-Augmented Large Language Models (2024.emnlp-main)

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Yuxiang Zhang, Jing Chen, Junjie Wang, Yaxin Liu, Cheng Yang, Chufan Shi, Xinyu Zhu, Zihao Lin, Hanwen Wan, Yujiu Yang, Tetsuya Sakai, Tian Feng, Hayato Yamana

Youling: an AI-assisted Lyrics Creation System (2020.emnlp-demos)

Challenge:	Currently, tool-augmented large language models (LLMs) only achieve total scores of 45.3 and 37.0, respectively, on a scale of 100.
Approach:	They propose a multi-level diagnostic process to assess the LLM's hallucinations through two perspectives: depth and breadth.
Outcome:	The proposed diagnostic process assesses the hallucinations of large language models through two perspectives: depth and breadth.

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Rongsheng Zhang, Xiaoxi Mao, Le Li, Lin Jiang, Lin Chen, Zhiwei Hu, Yadong Xi, Changjie Fan, Minlie Huang

Challenge:	Recent studies have focused on a single pass of lyrics generation with little human intervention.
Approach:	They propose an AI-assisted lyrics creation system that supports one pass full-text generation and interactive generation modes.
Outcome:	The proposed system supports full-text generation and interactive generation modes . it also provides a revision module which enables users to revise undesired lyrics repeatedly.

A Multimodal In-Context Tuning Approach for E-Commerce Product Description Generation (2024.lrec-main)

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Yunxin Li, Baotian Hu, Wenhan Luo, Lin Ma, Yuxin Ding, Min Zhang

Weak2Wise: An Automated, Lightweight Framework for Weak-LLM-Friendly Reasoning Synthesis (2025.findings-emnlp)

Challenge:	Existing methods for generating product descriptions from images are inaccurate and generic . e-commerce product descriptions are important for content marketing and increasing engagement .
Approach:	They propose a new setting for generating product descriptions from images, augmented by marketing keywords.
Outcome:	The proposed approach improves the accuracy and diversity of product descriptions by up to 3.3% on Rouge-L and 9.4% on D-5.

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Jianing Lin, Yuanfang Guo, Shunning Liu, Zeming Liu, Yunhong Wang

ContextBLIP: Doubly Contextual Alignment for Contrastive Image Retrieval from Linguistically Complex Descriptions (2024.findings-acl)

Challenge:	Existing approaches to finetuning large language models rely on expensive manual annotations or auxiliary models and fail to address the unique constraints of smaller "weak" LLMs.
Approach:	Weak2Wise is a fully automated framework for synthesizing highquality, weak-LLM-friendly reasoning traces.
Outcome:	Weak2Wise is a fully automated, lightweight framework for synthesizing highquality, weak-LLM-friendly reasoning traces.

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Honglin Lin, Siyu Li, Guoshun Nan, Chaoyue Tang, Xueting Wang, Jingxin Xu, Rong Yankai, Zhouzhili Zhouzhili, Yutong Gao, Qimei Cui, Xiaofeng Tao

Prompt-R1: Collaborative Automatic Prompting Framework via End-to-end Reinforcement Learning (2026.findings-acl)

Challenge:	Existing approaches to image retrieval from contextual descriptions (IRCD) lag behind human performance in IRCD.
Approach:	They propose a method that relies on a doubly contextual alignment scheme for challenging IRCD.
Outcome:	The proposed method can yield comparable results with GPT-4V, despite fewer parameters.

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Wenjin Liu, Haoran Luo, Xueyuan Lin, Haoming Liu, Tiesunlong Shen, Jiapu Wang, Rui Mao, Erik Cambria

CofiPara: A Coarse-to-fine Paradigm for Multimodal Sarcasm Target Identification with Large Multimodal Models (2024.acl-long)

Challenge:	Existing large language models are limited in understanding, reasoning, calculation, and generation, limiting their performance in complex reasoning and dynamic tasks.
Approach:	They propose a plug-and-play framework that integrates a small-scale LLM (as agent) with large-scale large-level LLMs (a as environment) they propose generating prompts that are used to interact with LLM, and a double constraint reward that optimizes correctness and quality of generation.
Outcome:	The proposed framework significantly outperforms baseline large-scale large-language models across various tasks.

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Zixin Chen, Hongzhan Lin, Ziyang Luo, Mingfei Cheng, Jing Ma, Guang Chen

On the Robustness of Reading Comprehension Models to Entity Renaming (2022.naacl-main)

Challenge:	Current methods for multimodal sarcasm target identification focus on superficial indicators in an end-to-end manner, overlooking the nuanced understanding of multimodal content.
Approach:	They propose a multimodal sarcasm target identification framework with a coarse-to-fine paradigm by augmenting sarcasm explainability with reasoning and pre-training knowledge.
Outcome:	The proposed framework outperforms state-of-the-art methods and exhibits explainability in deciphering sarcasm as well.

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Jun Yan, Yang Xiao, Sagnik Mukherjee, Bill Yuchen Lin, Robin Jia, Xiang Ren

Cognitive Reframing of Negative Thoughts through Human-Language Model Interaction (2023.acl-long)

Challenge:	SpanBERT model is more robust than RoBERTa, despite having similar accuracy on unperturbed test data.
Approach:	They propose a pipeline to replace entity names with names from a variety of sources.
Outcome:	The proposed model performs worse when entities are renamed, the authors show . SpanBERT, which is pretrained with span-level masking, is more robust than RoBERTa .

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Ashish Sharma, Kevin Rushton, Inna Lin, David Wadden, Khendra Lucas, Adam Miner, Theresa Nguyen, Tim Althoff

Leveraging Dual Process Theory in Language Agent Framework for Real-time Simultaneous Human-AI Collaboration (2025.acl-long)

Challenge:	Psychotherapy can help people overcome negative thoughts by replacing them with a more hopeful "reframed thought" but clinician shortages and mental health stigma often limit access to therapy.
Approach:	They propose a framework of seven linguistic attributes that can be used to reframe a thought . they use a retrieval-enhanced in-context learning model to generate reframed thoughts .
Outcome:	The proposed model is based on a human-centered study of 600 situations, thoughts and reframes on 2,000 mental health websites.

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Shao Zhang, Xihuai Wang, Wenhao Zhang, Chaoran Li, Junru Song, Tingyu Li, Lin Qiu, Xuezhi Cao, Xunliang Cai, Wen Yao, Weinan Zhang, Xinbing Wang, Ying Wen

Linguistic Theories

Challenge:	Large language models (LLMs) excel in turn-by-turn human-AI collaboration but struggle with simultaneous tasks requiring real-time interaction.
Approach:	They propose a language agent framework that integrates System 1 and System 2 for efficient real-time simultaneous human-AI collaboration.
Outcome:	The proposed framework improves on existing LLM-based agents and human collaborators by integrating Theory of Mind and asynchronous reflection to infer human intentions and perform reasoning-based autonomous decisions.

Chinese Discourse Parsing: Model and Evaluation (2020.lrec-1)

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Lin Chuan-An, Shyh-Shiun Hung, Hen-Hsen Huang, Hsin-Hsi Chen

Correctable-DST: Mitigating Historical Context Mismatch between Training and Inference for Improved Dialogue State Tracking (2022.emnlp-main)

Challenge:	Chinese discourse parsing has not yet a consistent evaluation metric . micro vs. macro F1 scores, binary v. multiway ground truth, and left-heavy v . right-heaviness binarization are important for Chinese discourses .
Approach:	They propose a neural network model that unifies a pre-trained transformer and a CKY-like algorithm and compare it with previous models with different evaluation scenarios.
Outcome:	The proposed model outperforms the previous models with different evaluation scenarios.

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Hongyan Xie, Haoxiang Su, Shuangyong Song, Hao Huang, Bo Zou, Kun Deng, Jianghua Lin, Zhihui Zhang, Xiaodong He

SpeechNet: Weakly Supervised, End-to-End Speech Recognition at Industrial Scale (2022.emnlp-industry)

Challenge:	Existing dialogue state tracking approaches predict the dialogue state of a target turn sequentially based on the ground-truth previous dialogue state.
Approach:	They propose a method that predicts dialogue state sequentially based on previous dialogue state . they propose generating a previously “predicted” dialogue state using ground-truth previous dialogue states .
Outcome:	The proposed method achieves 67.51%, 68.24%, 70.30%, 71.38%, and 81.27% joint goal accuracy on MultiWOZ 2.0-2.4 datasets.

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Raphael Tang, Karun Kumar, Gefei Yang, Akshat Pandey, Yajie Mao, Vladislav Belyaev, Madhuri Emmadi, Craig Murray, Ferhan Ture, Jimmy Lin

DARL: Encouraging Diverse Answers for General Reasoning without Verifiers (2026.findings-acl)

Challenge:	End-to-end automatic speech recognition systems require thousands of hours of manual annotation and heavyweight computation to perform inference.
Approach:	They propose to use a third-party ASR system as a weak supervision source and labeling functions derived from implicit user feedback to reduce human labor.
Outcome:	The proposed system improves word-error rate and speed up 600% over third-party ASR.

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Chongxuan Huang, Lei Lin, Xiaodong Shi, Wenping Hu, Ruiming Tang

A Survey on LLM-based Conversational User Simulation (2026.eacl-long)

Challenge:	Recent efforts such as RLPR have extended RLVR to general domains, enabling training on broader datasets and achieving improvements over RL PR.
Approach:	They propose a framework that encourages the generation of diverse answers within a controlled deviation range from the reference while preserving alignment with it.
Outcome:	Extensive experiments on 13 benchmarks show that DARL surpasses RLPR in both reasoning accuracy and output diversity.

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Bo Ni, Yu Wang, Leyao Wang, Branislav Kveton, Franck Dernoncourt, Yu Xia, Hongjie Chen, Reuben Luera, Samyadeep Basu, Subhojyoti Mukherjee, Puneet Mathur, Nesreen K. Ahmed, Junda Wu, Li Li, Huixin Zhang, Ruiyi Zhang, Tong Yu, Sungchul Kim, Jiuxiang Gu, Zhengzhong Tu, Alexa Siu, Zichao Wang, Seunghyun Yoon, Nedim Lipka, Namyong Park, Zihao Lin, Trung Bui, Yue Zhao, Tyler Derr, Ryan A. Rossi

Dynamic Sampling that Adapts: Self-Aware Iterative Data Persistent Optimization for Mathematical Reasoning (2026.findings-acl)

Challenge:	Recent advances in large language models (LLMs) have enabled high-fidelity generation of synthetic user conversation.
Approach:	They propose a taxonomy covering user granularity and simulation objectives . they analyze core techniques and evaluation methodologies to help them understand the latest developments .
Outcome:	The proposed model enables high-fidelity generation of synthetic user conversation.

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Jun Rao, Xuebo Liu, Hexuan Deng, Zepeng Lin, Zixiong Yu, Jiansheng Wei, Xiaojun Meng, Min Zhang

DualNER: A Dual-Teaching framework for Zero-shot Cross-lingual Named Entity Recognition (2022.findings-emnlp)

Challenge:	Current data selection paradigms rely on static, externally defined metrics, which fail to adapt to the evolving capabilities of models during training.
Approach:	They propose a dynamic sampling framework that aligns training data with the model's intrinsic competence by iterating on real-time feedback.
Outcome:	Extensive experiments on eight benchmarks show that SAI-DPO outperforms static baselines at most nearly 6 points, achieving state-of-the-art efficiency with significantly less data.

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Jiali Zeng, Yufan Jiang, Yongjing Yin, Xu Wang, Binghuai Lin, Yunbo Cao

Data Interpreter: An LLM Agent for Data Science (2025.findings-acl)

Challenge:	Existing approaches to named entity recognition (NER) are limited to high-resource languages like English and Chinese.
Approach:	They propose a framework to make full use of annotated source and unlabeled target language text for zero-shot cross-lingual named entity recognition.
Outcome:	The proposed framework makes full use of both annotated source and unlabeled target language text for zero-shot cross-lingual named entity recognition (NER).

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Sirui Hong, Yizhang Lin, Bang Liu, Bangbang Liu, Binhao Wu, Ceyao Zhang, Danyang Li, Jiaqi Chen, Jiayi Zhang, Jinlin Wang, Li Zhang, Lingyao Zhang, Min Yang, Mingchen Zhuge, Taicheng Guo, Tuo Zhou, Wei Tao, Robert Tang, Xiangtao Lu, Xiawu Zheng, Xinbing Liang, Yaying Fei, Yuheng Cheng, Yongxin Ni, Zhibin Gou, Zongze Xu, Yuyu Luo, Chenglin Wu

GRAVL-BERT: Graphical Visual-Linguistic Representations for Multimodal Coreference Resolution (2022.coling-1)

Challenge:	Large Language Models (LLMs) excel in various domains but face challenges when applied to data science workflows due to their complex, multi-stage nature.
Approach:	They propose a hierarchical graph-based agent that represents complexity and a progressive strategy for step-by-step verification, refinement, and consistent context management.
Outcome:	The proposed agent surpasses state-of-the-art baselines on the MATH dataset and performs better on InfiAgent-DABench.

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Danfeng Guo, Arpit Gupta, Sanchit Agarwal, Jiun-Yu Kao, Shuyang Gao, Arijit Biswas, Chien-Wei Lin, Tagyoung Chung, Mohit Bansal

PclGPT: A Large Language Model for Patronizing and Condescending Language Detection (2024.findings-emnlp)

Challenge:	Multimodal coreference resolution (MCR) is a crucial capability for building next-generation conversational agents.
Approach:	They propose a multimodal coreference resolution model that resolves coreferences made in multi-turn dialogues with scene images.
Outcome:	The proposed model resolves coreferences made in multi-turn dialogues with scene images.

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Hongbo Wang, LiMingDa LiMingDa, Junyu Lu, Hebin Xia, Liang Yang, Bo Xu, Ruizhu Liu, Hongfei Lin

Bit-Flip Error Resilience in LLMs: A Comprehensive Analysis and Defense Framework (2025.emnlp-main)

Challenge:	Patronizing and condescending language is an essential branch of toxic language . pre-trained language models perform poorly in detecting PCL due to its implicit toxicity traits .
Approach:	They propose a novel LLM benchmark for patronizing and condescending language . they use a dataset to analyze the toxicity of patronizing condescending languages .
Outcome:	The proposed model can detect patronizing and condescending language (PCL) the model can be used to analyze the toxicity of the language and to improve the detection.

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Yuhang Chen, Zhen Tan, Ajay Kumar Jaiswal, Huaizhi Qu, Xinyu Zhao, Qi Lin, Yu Cheng, Andrew Kwong, Zhichao Cao, Tianlong Chen

Unified Structure Generation for Universal Information Extraction (2022.acl-long)

Challenge:	Bit-flip errors (BFEs) are hardware faults where individual bits in memory or processing units are unintentionally flipped.
Approach:	They propose a novel defense strategy to mitigate bit-flip errors (BFEs) they propose bfe protection and a self-correction mechanism to minimize performance degradation .
Outcome:	The proposed defense strategy minimizes performance degradation while significantly improving robustness against BFEs.

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Yaojie Lu, Qing Liu, Dai Dai, Xinyan Xiao, Hongyu Lin, Xianpei Han, Le Sun, Hua Wu

AudioBench: A Universal Benchmark for Audio Large Language Models (2025.naacl-long)

Challenge:	Information extraction suffers from its varying targets, heterogeneous structures, and demand-specific schemas.
Approach:	They propose a unified text-to-structure generation framework, namely UIE, which can universally model different IE tasks, adaptively generate targeted structures, and collaboratively learn general IE abilities from different knowledge sources.
Outcome:	The proposed framework can model different IE tasks, generate targeted structures, and learn general IE abilities from different knowledge sources.

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Bin Wang, Xunlong Zou, Geyu Lin, Shuo Sun, Zhuohan Liu, Wenyu Zhang, Zhengyuan Liu, AiTi Aw, Nancy F. Chen

Full-Duplex-Bench-v2: A Multi-Turn Evaluation Framework for Duplex Dialogue Systems with an Automated Examiner (2026.acl-short)

Challenge:	Existing evaluation regimes for audio large language models do not cover the breadth of their possible use cases.
Approach:	They propose to use AudioBench to evaluate audio large language models . they found that no single model excels consistently across all tasks .
Outcome:	The proposed evaluation targets speech understanding, audio scene understanding, and voice understanding (paralinguistic) . no single model excels consistently across all tasks, the paper found .

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Guan-Ting Lin, Shih-Yun Shan Kuan, Jiatong Shi, Kai-Wei Chang, Siddhant Arora, Shinji Watanabe, Hung-yi Lee

Beyond Facts- Benchmarking Distributional Reading Comprehension in Large Language Models (2026.findings-acl)

Challenge:	Full-duplex speech agents are often half-duplice, alternating turns between user and system.
Approach:	They propose a streaming framework that integrates with an examiner that enforces staged goals under two pacing setups.
Outcome:	The framework reports fluency, multi-turn instruction following, and task-specific competence.

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Pei-Fu Guo, Ya An Tsai, Chun-Chia Hsu, Kai-Xin Chen, Yun-Da Tsai, Kai-Wei Chang, Nanyun Peng, Mi-Yen Yeh, Shou-De Lin

DeepPresenter: Environment-Grounded Reflection for Agentic Presentation Generation (2026.findings-acl)

Challenge:	Existing reading comprehension benchmarks focus on factual information, but many real-world tasks require distributional knowledge expressed across text.
Approach:	They propose a reading comprehension benchmark for LLMs to evaluate their ability to infer distributional knowledge from natural language.
Outcome:	Experiments with multiple LLMs show that the model outperforms baselines, but performance varies widely across distribution types and characteristics.

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Hao Zheng, Guozhao Mo, Xinru Yan, Qianhao Yuan, Wenkai Zhang, Xuanang Chen, Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun

Challenge:	Existing presentation agents rely on predefined workflows and fixed templates to generate presentations.
Approach:	They propose an agentic framework that adapts to diverse user intents and iterative refinement based on observation.
Outcome:	The proposed framework can be used to generate presentations with environmental observations.

UFO: A UI-Focused Agent for Windows OS Interaction (2025.naacl-long)

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Chaoyun Zhang, Liqun Li, Shilin He, Xu Zhang, Bo Qiao, Si Qin, Minghua Ma, Yu Kang, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang, Qi Zhang

Applying BERT to Document Retrieval with Birch (D19-3)

Challenge:	UFO is a UI-Fcused agent designed to fulfill user requests tailored to Windows OS applications . it decomposes user requests using divide-and-conquer approach, enabling seamless navigation and addressing sub-tasks across multiple applications.
Approach:	They propose a UI-Fcused Windows OS agent that decomposes user requests using a divide-and-conquer approach and incorporates a control interaction module tailored for Windows OS.
Outcome:	The proposed agent decomposes user requests using divide-and-conquer approach, enabling seamless navigation and addressing sub-tasks across multiple applications.

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Zeynep Akkalyoncu Yilmaz, Shengjin Wang, Wei Yang, Haotian Zhang, Jimmy Lin

Beta Distribution Guided Aspect-aware Graph for Aspect Category Sentiment Analysis with Affective Knowledge (2021.emnlp-main)

Challenge:	Birch is an open-source document retrieval system that integrates with the Anserini information retrieval toolkit to demonstrate end-to-end search over large document collections.
Approach:	They propose to integrate Anserini with a BERT-based document ranking model that provides an end-to-end open-source search engine.
Outcome:	The proposed system outperforms existing approaches to document retrieval and question answering on standard newswire and social media test collections.

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Bin Liang, Hang Su, Rongdi Yin, Lin Gui, Min Yang, Qin Zhao, Xiaoqi Yu, Ruifeng Xu

Training LLMs for Divide-and-Conquer Reasoning Elevates Test-Time Scalability (2026.acl-long)

Challenge:	Existing methods for aspect category sentiment analysis do not necessarily occur in a sentence.
Approach:	They propose a Beta Distribution-guided aspect-aware graph construction based on external knowledge . they use aspect-related words as the pivots to derive aspect-relevant weights .
Outcome:	The proposed approach outperforms the state-of-the-art methods on 6 benchmark datasets.

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Xiao Liang, Zhong-Zhi Li, Zhenghao Lin, Eric Hanchen Jiang, Hengyuan Zhang, Yelong Shen, Kai-Wei Chang, Ying Nian Wu, Yeyun Gong, Weizhu Chen

ChartM3: A Multi-Stage Code-Driven Pipeline for Constructing Multi-Dimensional and Multi-Step Visual Reasoning Data in Chart Comprehension (2025.findings-emnlp)

Challenge:	Large language models have demonstrated strong reasoning capabilities through step-by-step chain-of-thought (CoT) reasoning, but their strictly sequential nature constrains test-time scalability.
Approach:	They propose an end-to-end reinforcement learning framework to enhance LLMs' DAC-style reasoning capacity by decomposing a problem into subproblems and solving them sequentially.
Outcome:	The proposed model surpasses CoT by 8.6% and 6.3% on competition-level benchmarks and is available at the [github.com/MasterVito/DAC-RL].

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Duo Xu, Hao Cheng, Xin Lin, Zhen Xie, Hao Henry Wang

LiveCLKTBench: Towards Reliable Evaluation of Cross-Lingual Knowledge Transfer in Multilingual LLMs (2026.acl-long)

Challenge:	Currently, research on complex chart understanding tasks is limited . a pipeline for visual reasoning datasets addresses these limitations .
Approach:	They propose a code-driven pipeline for generating visual reasoning datasets . pipeline integrates retrieval-augmented generation to retrieve professional chart templates .
Outcome:	The proposed pipeline enhances chart diversity and data quality through model-based evaluation.

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Pei-Fu Guo, Yun-Da Tsai, Chun-Chia Hsu, Kai-Xin Chen, Ya An Tsai, Kai-Wei Chang, Nanyun Peng, Mi-Yen Yeh, Shou-De Lin

Knowledge-Augmented Multimodal Clinical Rationale Generation for Disease Diagnosis with Small Language Models (2025.acl-long)

Challenge:	Evaluating cross-lingual knowledge transfer in large language models is challenging, as correct answers in a target language may arise either from genuine transfer or from prior exposure during pre-training.
Approach:	They propose a pipeline to isolate and measure cross-lingual knowledge transfer by identifying self-contained, time-sensitive knowledge entities from real-world domains and generating factual questions.
Outcome:	The proposed pipeline analyzes multiple LLMs across five languages and shows that cross-lingual transfer is strongly influenced by linguistic distance and often asymmetric across language directions.

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Shuai Niu, Jing Ma, Hongzhan Lin, Liang Bai, Zhihua Wang, Richard Yi Da Xu, Yunya Song, Xian Yang

DI-BENCH: Benchmarking Large Language Models on Dependency Inference with Testable Repositories at Scale (2025.findings-acl)

Challenge:	Existing models struggle to balance predictive accuracy with human-understandable rationales.
Approach:	They propose to enhance LLMs by leveraging rationale distillation and domain knowledge injection for trustworthy multimodal rationale generation.
Outcome:	Experiments on real-world medical datasets show that ClinRaGen achieves state-of-the-art performance in disease diagnosis and rationale generation.

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Linghao Zhang, Junhao Wang, Shilin He, Chaoyun Zhang, Yu Kang, Bowen Li, Jiaheng Wen, Chengxing Xie, Maoquan Wang, Yufan Huang, Elsie Nallipogu, Qingwei Lin, Yingnong Dang, Saravan Rajmohan, Dongmei Zhang, Qi Zhang

AMO-Bench: Large Language Models Still Struggle in High School Math Competitions (2026.findings-acl)

Challenge:	Existing studies highlight that dependency-related issues cause over 40% of observed runtime errors on the generated repository.
Approach:	They propose a large-scale benchmark and evaluation framework specifically designed to assess LLMs’ capability on dependency inference.
Outcome:	The proposed model achieves only a 48% execution pass rate on Python, indicating room for improvement.

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Junlin Liu, Shengnan An, Shuang Zhou, Dan Ma, Yehao Lin, Xinxuan Lv, Xuanlin Wang, Xiaoyu Li, Ziwen Wang, Xuezhi Cao, Xunliang Cai

CodeJudge-Eval: Can Large Language Models be Good Judges in Code Understanding? (2025.coling-main)

Challenge:	Existing benchmarks for mathematical reasoning are becoming less effective due to performance saturation.
Approach:	They propose to use a mathematical reasoning benchmark with Olympiad difficulty to evaluate top-tier LLMs.
Outcome:	The proposed benchmarks are cross-validated by experts to meet IMO difficulty standards and entirely original problems to prevent performance leakages from data memorization.

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Yuwei Zhao, Ziyang Luo, Yuchen Tian, Hongzhan Lin, Weixiang Yan, Annan Li, Jing Ma

ReCreate: Reasoning and Creating Domain Agents Driven by Experience (2026.acl-long)

Challenge:	Recent advances in large language models (LLMs) have showcased impressive code generation capabilities, primarily evaluated through language-to-code benchmarks.
Approach:	They propose a benchmark to assess LLMs’ code understanding abilities from the perspective of code judging rather than code generation.
Outcome:	The proposed benchmark evaluates 12 well-known large language models to determine the correctness of provided code solutions.

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Zhezheng Hao, Hong Wang, Jian Luo, Jianqing Zhang, Yuyan Zhou, Qiang Lin, Can Wang, Hande Dong, Jiawei Chen

Temporal Scaling Law for Large Language Models (2025.emnlp-main)

Challenge:	Large Language Model (LLM) agents are reshaping the industrial landscape, but tasks differ widely, making them labor-intensive to build.
Approach:	They propose an experience-driven framework for the automatic creation of domain agents . they leverage agent interaction histories to provide rich concrete signals on success or failure .
Outcome:	The proposed framework outperforms human-designed agents and existing methods in experiments across diverse domains.

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Yizhe Xiong, Xiansheng Chen, Xin Ye, Hui Chen, Zijia Lin, Haoran Lian, Zhenpeng Su, Wei Huang, Jianwei Niu, Jungong Han, Guiguang Ding

DocMMIR: A Framework for Document Multi-modal Information Retrieval (2025.findings-emnlp)

Challenge:	Existing studies have found that the test loss of LLMs scales as power-laws with model size, computational budget, and dataset size.
Approach:	They propose a concept of Temporal Scaling Law to study test loss of LLMs . they break down test loss into fine-grained token positions and develop a dynamic hyperbolic-law .
Outcome:	The proposed model predicts the test loss of LLMs as the training steps scale up.

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Zirui Li, Siwei Wu, Yizhi Li, Xingyu Wang, Yi Zhou, Chenghua Lin

Modeling Multi-Dimensional Cognitive States in Large Language Models under Cognitive Crowding (2026.findings-acl)

Challenge:	Existing multi-modal information retrieval models lack a comprehensive exploration of document-level retrieval . existing models suffer from the absence of cross-domain datasets at this granularity.
Approach:	They propose a multi-modal document retrieval framework to unify diverse document formats and domains with a comprehensive retrieval scenario.
Outcome:	The proposed framework improves document retrieval performance on a large multimodal dataset.

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Lin Zhong, Siyu Zhu, Zizhen Yuan, Jinhao Cui, Xinyang Zhao, Lingzhi Wang, Hao Chen, Qing Liao

Preserving Zero-shot Capability in Supervised Fine-tuning for Multi-label Text Classification (2025.findings-naacl)

Challenge:	Existing Large Language Models (LLMs) mainly address isolated tasks such as emotion analysis or stance detection.
Approach:	They propose a large-scale model that combines large-level annotations with hyperbolic space to model human cognitive states.
Outcome:	The proposed model outperforms baseline models on cognitive dimensions on single dimension tasks while retaining strong hierarchical structure.

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Si-An Chen, Hsuan-Tien Lin, Chih-Jen Lin

Distributed Marker Representation for Ambiguous Discourse Markers and Entangled Relations (2023.acl-long)

Challenge:	Existing methods that assume label descriptions ensure zero-shot capability lose their zero-shot capability during training.
Approach:	They propose a method that preserves the zero-shot capabilities of powerful dual encoders and label-wise attention networks by freezing the label encoder.
Outcome:	The proposed methods preserve the zero-shot capabilities of powerful dual encoder and label-wise attention network architectures by freezing the label encoder.

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Dongyu Ru, Lin Qiu, Xipeng Qiu, Yue Zhang, Zheng Zhang

MATO: A Model-Agnostic Training Optimization for Aspect Sentiment Triplet Extraction (2025.naacl-long)

Challenge:	Discourse markers are natural representations of discourse in our daily language.
Approach:	They propose to use unlimited discourse marker data to learn a Distributed Marker Representation by bridging markers with sentence pairs.
Outcome:	The proposed model outperforms existing models on the implicit discourse relation recognition task and provides strong interpretability.

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Shaopeng Tang, Lin Li, Xiaohui Tao, Leqi Zhong, Qing Xie

GEMS: Generation-Based Event Argument Extraction via Multi-perspective Prompts and Ontology Steering (2025.findings-acl)

Challenge:	Existing models with strong in-house performance may struggle to generalize to diverse expressions.
Approach:	They propose a model-agnostic t**raining method to improve ASTE model inference . they propose to compute the violation rate (VR) on each element of one triplet .
Outcome:	The proposed method can improve aspect sentiment triplet extraction models consistent with expected results facing triplet element diversity.

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Run Lin, Yao Liu, Yanglei Gan, Yuxiang Cai, Tian Lan, Qiao Liu

TeamLoRA: Boosting Low-Rank Adaptation with Expert Collaboration and Competition (2025.acl-long)

Challenge:	Existing methods for event argument extraction rely on a single prompt . existing methods ignore complex structural and dynamic interdependencies between event arguments .
Approach:	They propose a multi-prompt learning framework that generates event arguments via multi-perspective prompts and ontology steering.
Outcome:	The proposed framework captures interrelationships between arguments and ontology steering . it uses multiple unfilled prompts for each sentence to generate event arguments .

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Tianwei Lin, Jiang Liu, Wenqiao Zhang, Yang Dai, Haoyuan Li, Zhelun Yu, Wanggui He, Juncheng Li, Jiannan Guo, Hao Jiang, Siliang Tang, Yueting Zhuang

Mirror-Consistency: Harnessing Inconsistency in Majority Voting (2024.findings-emnlp)

Challenge:	Existing methods for fine-tuning are resource-efficient, but performance often falls short . a new approach, TeamLoRA, integrates collaborative and competitive modules to improve performance.
Approach:	They propose to introduce task-specific LoRA as domain experts to improve learning efficiency . teamLoRA integrates collaborative and competition modules to improve model learning .
Outcome:	Experiments show that TeamLoRA improves performance in multi-task learning . teamLorea integrates collaborative and competitive modules to improve performance .

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Siyuan Huang, Zhiyuan Ma, Jintao Du, Changhua Meng, Weiqiang Wang, Zhouhan Lin

FPE2M2: Approaching Lossless and Efficient Quantization with Native Floating Point (2025.findings-acl)

Challenge:	Large Language Models (LLMs) are a widely-used decoding strategy that relies on the plurality voting rule, which focuses on the most frequent answer while overlooking all other minority responses.
Approach:	They propose to incorporate a ‘reflective mirror’ into the self-ensemble decoding process and enables LLMs to critically examine inconsistencies among multiple generations.
Outcome:	The proposed method incorporates a ‘reflective mirror’ into the self-ensemble decoding process and enables LLMs to critically examine inconsistencies among multiple generations.

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Ke Yi, Jianwei Zhang, Zhiying Xu, Xinlong Yang, Yang Zhou, Minmin Sun, Zengke Liu, Tong Zhang, Junyang Lin, Jingren Zhou

Neural-Symbolic Inference for Robust Autoregressive Graph Parsing via Compositional Uncertainty Quantification (2022.emnlp-main)

Challenge:	Auto-regressive decoding is a memory-bound job, meaning decoding performance is limited by the bandwidth rather than the computational capabilities of the GPU.
Approach:	They propose a framework that supports lossless weight-only quantization inference and validate it on Qwen and LLaMA Models.
Outcome:	The proposed framework achieves the highest efficiency with lossless accuracy on Qwen and LLaMA Models across various modalities.

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Zi Lin, Jeremiah Liu, Jingbo Shang

LEMMA: Learning from Errors for MatheMatical Advancement in LLMs (2025.findings-acl)

Challenge:	Pre-trained models excel at graph semantic parsing with rich annotated data, but generalize poorly to out-of-distribution and long-tail examples.
Approach:	They propose a compositionality-aware approach to neural-symbolic inference informed by model confidence to capture different aspects of the graph prediction.
Outcome:	The proposed method outperforms state-of-the-art models on an English resource grammar parsing problem on standard in-domain and seven OOD corpora.

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Zhuoshi Pan, Yu Li, Honglin Lin, Qizhi Pei, Zinan Tang, Wei Wu, Chenlin Ming, H. Vicky Zhao, Conghui He, Lijun Wu

More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction (2020.aacl-main)

Challenge:	Existing approaches focus on improving the quality of correct training data, neglecting the value contained in error data, thereby hindering the model’s reflective ability.
Approach:	They propose to improve LLM's reasoning ability by learning from error data and a grounded mistake augmentation method to collect representative errors.
Outcome:	The proposed model achieves significant performance improvements over other strong models with less than 90k data.

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Xu Han, Tianyu Gao, Yankai Lin, Hao Peng, Yaoliang Yang, Chaojun Xiao, Zhiyuan Liu, Peng Li, Jie Zhou, Maosong Sun

Task-Oriented Conversation Generation Using Heterogeneous Memory Networks (D19-1)

Challenge:	Existing methods for extracting relational facts from text have been successful . but with explosion of Web text, human knowledge is increasing drastically .
Approach:	They propose to improve relation extraction methods to extract relational facts from text . they analyze existing methods and show promising directions towards more powerful RE .
Outcome:	The proposed methods can extract relational facts from text, but they are still lacking in the current field.

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Zehao Lin, Xinjing Huang, Feng Ji, Haiqing Chen, Yin Zhang

PreP-OCR: A Complete Pipeline for Document Image Restoration and Enhanced OCR Accuracy (2025.acl-long)

Challenge:	Existing memory networks do not perform well when leveraging heterogeneous information from different sources.
Approach:	They propose to use user utterances, dialogue history and background knowledge tuples to integrate external knowledge into a neural dialogue model.
Outcome:	The proposed model outperforms the state-of-the-art data-driven task-oriented dialogue models on real-world datasets.

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Shuhao Guan, Moule Lin, Cheng Xu, Xinyi Liu, Jinman Zhao, Jiexin Fan, Qi Xu, Derek Greene

Topic-Driven and Knowledge-Aware Transformer for Dialogue Emotion Detection (2021.acl-long)

Challenge:	Existing pipelines that combine document image restoration with semantic-aware post-OCR correction can improve text extraction from degraded images.
Approach:	They propose a two-stage pipeline that combines document image restoration with semantic-aware post-OCR correction to enhance both visual clarity and textual consistency.
Outcome:	The proposed pipeline reduces character error rates by 63.9-70.3% on 13,831 pages of real historical documents in English, French, and Spanish compared to OCR on raw images.

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Lixing Zhu, Gabriele Pergola, Lin Gui, Deyu Zhou, Yulan He

Tell Me More! Towards Implicit User Intention Understanding of Language Model Driven Agents (2024.acl-long)

Challenge:	Emotion detection in dialogues requires the identification of thematic topics underlying a conversation, commonsense knowledge, and the intricate transition patterns between affective states.
Approach:	They propose a Topic-Driven Knowledge-Aware Transformer model that integrates topic representation and commonsense knowledge from ATOMIC for dialogue emotion detection.
Outcome:	The proposed model outperforms state-of-the-art models on four dialogue datasets . it can detect topics which help distinguish emotion categories, the authors show .

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Cheng Qian, Bingxiang He, Zhong Zhuang, Jia Deng, Yujia Qin, Xin Cong, Zhong Zhang, Jie Zhou, Yankai Lin, Zhiyuan Liu, Maosong Sun

Beyond Static Testbeds: An Interaction-Centric Agent Simulation Platform for Dynamic Recommender Systems (2025.emnlp-main)

Challenge:	Current language model-driven agents lack mechanisms for effective user participation, which is crucial given the vagueness commonly found in user instructions.
Approach:	They propose a benchmark to inspect users’ implicit intentions through explicit queries and a model expert as the upstream in agent design to enhance user-agent interaction.
Outcome:	The proposed approach excels at identifying vague user tasks, recovering and summarizing critical missing information, setting precise and necessary agent execution goals, and minimizing redundant tool usage, thus boosting overall efficiency.

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Song Jin, Juntian Zhang, Yuhan Liu, Xun Zhang, Yufei Zhang, Guojun Yin, Fei Jiang, Wei Lin, Rui Yan

Challenge:	Existing platforms lack a mechanism for user actions to dynamically reshape the environment.
Approach:	They propose a novel agent-based simulation platform for recommender systems with a robust interaction mechanism.
Outcome:	The proposed platform improves the credibility of the simulation and replicates the Matthew Effect and Brand Loyalty.

Flaming-hot Initiation with Regular Execution Sampling for Large Language Models (2025.findings-naacl)

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Weizhe Chen, Zhicheng Zhang, Guanlin Liu, Renjie Zheng, Wenlei Shi, Chen Dun, Zheng Wu, Xing Jin, Lin Yan

Prompt Space Optimizing Few-shot Reasoning Success with Large Language Models (2024.findings-naacl)

Challenge:	Large language models (LLMs) have demonstrated remarkable capabilities across various domains since the release of ChatGPT . a key challenge in developing these general capabilities is efficiently sourcing diverse, high-quality data.
Approach:	They introduce Flaming-hot Initiation with Regular Execution (FIRE) sampling to efficiently find good responses by promoting diversity.
Outcome:	The proposed method enhances inference-time generation quality and benefits training in the alignment stage.

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Fobo Shi, Peijun Qing, Dong Yang, Nan Wang, Youbo Lei, Haonan Lu, Xiaodong Lin, Duantengchuan Li

NOVA-63: Native Omni-lingual Versatile Assessments of 63 Disciplines (2025.emnlp-main)

Challenge:	Prompt engineering is an essential technique for enhancing the abilities of large language models (LLMs) by providing explicit and specific instructions.
Approach:	They propose a new approach that uses text embeddings to obtain basis vectors by matrix decomposition and constructs a space for representing all prompts.
Outcome:	The proposed approach significantly outperforms state-of-the-art prompt paradigms on ten public reasoning benchmarks.

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Jinyang Zhang, Kexin Yang, Yu Wan, Muyang Ye, Baosong Yang, Fei Huang, Junyang Lin, Dayiheng Liu

Knowledge-Aware Graph-Enhanced GPT-2 for Dialogue State Tracking (2021.emnlp-main)

Challenge:	Existing multilingual benchmarks show severe drawbacks, such as overly translated content, the absence of difficulty control, and disciplinary imbalance, making the benchmarking process unreliable and showing low convincingness.
Approach:	They propose a multilingual benchmark that integrates LLM-assisted formatting, expert quality verification, and multi-level difficulty screening to provide a comprehensive, difficult multilingual assessment.
Outcome:	The proposed benchmark features 93,536 questions sourced from native speakers across 14 languages and 63 academic disciplines.

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Weizhe Lin, Bo-Hsiang Tseng, Bill Byrne

CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning (2020.findings-emnlp)

Challenge:	Existing models for dialogue state tracking are based on Graph Attention Networks . if the relationship between slots and values is modelled explicitly, this can be improved .
Approach:	They propose a model architecture that augments GPT-2 with Graph Attention Networks to allow sequential prediction of slot values.
Outcome:	The proposed architecture improves performance against a strong GPT-2 baseline and with sparsely supervised training.

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Bill Yuchen Lin, Wangchunshu Zhou, Ming Shen, Pei Zhou, Chandra Bhagavatula, Yejin Choi, Xiang Ren

PV2TEA: Patching Visual Modality to Textual-Established Information Extraction (2023.findings-acl)

Challenge:	Recent studies show that pre-trained language models perform well on commonsense-reasoning benchmark datasets, but building machines with commonsence to compose plausible sentences remains challenging.
Approach:	They propose a constrained text generation task for generative commonsense reasoning that generates a coherent sentence using common concepts.
Outcome:	The proposed task generates a coherent sentence describing an everyday scenario using common concepts over 35k concept-sets.

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Hejie Cui, Rongmei Lin, Nasser Zalmout, Chenwei Zhang, Jingbo Shang, Carl Yang, Xian Li

MMedAgent: Learning to Use Medical Tools with Multi-modal Agent (2024.findings-emnlp)

Challenge:	Empirical results show up to 11.74% absolute (20.97% relative) increase over unimodal baselines.
Approach:	They propose to patch the visual modality to the textual-established attribute in- formation extractor.
Outcome:	Empirical results show up to 11.74% absolute (29.9% relative) increase over unimodal baselines.

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Binxu Li, Tiankai Yan, Yuanting Pan, Jie Luo, Ruiyang Ji, Jiayuan Ding, Zhe Xu, Shilong Liu, Haoyu Dong, Zihao Lin, Yixin Wang

A Shared-Private Representation Model with Coarse-to-Fine Extraction for Target Sentiment Analysis (2020.findings-emnlp)

Challenge:	Multi-modal Large Language Models (MLLMs) exhibit limited generality and often fall short when compared to specialized models.
Approach:	They propose a multi-modal medical agent that picks the most suitable medical tools based on user inputs.
Outcome:	The proposed agent performs better than open-source models and the closed-source model, GPT-4o.

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Peiqin Lin, Meng Yang

KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning (D19-1)

Challenge:	Existing models with span-based labeling have achieved promising results in sentiment analysis.
Approach:	They propose a shared-private representation model with a coarse-to-fine extraction algorithm to solve this problem.
Outcome:	The proposed model achieves state-of-the-art on target phrases and extraction tasks.

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Bill Yuchen Lin, Xinyue Chen, Jamin Chen, Xiang Ren

SH2: Self-Highlighted Hesitation Helps You Decode More Truthfully (2024.findings-emnlp)

Challenge:	empowering machines with the ability to perform commonsense reasoning has been seen as the bottleneck of artificial general intelligence .
Approach:	They propose a textual inference framework that uses external commonsense knowledge graphs to answer commonsensical questions.
Outcome:	The proposed framework is based on graph convolutional networks and LSTMs with a hierarchical path-based attention mechanism.

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Jushi Kai, Tianhang Zhang, Hai Hu, Zhouhan Lin

The Instinctive Bias: Spurious Images lead to Illusion in MLLMs (2024.emnlp-main)

Challenge:	Large language models (LLMs) have made great progress in text generation but suffer from hallucinations during reasoning and generation.
Approach:	They propose an inference-time method to help LLMs decode truthfully by selecting tokens with the lowest probabilities and concatenating them to the original context.
Outcome:	The proposed method improves LLaMA-7b, LLama2-7b and Mistral-7b on hallucination tasks.

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Tianyang Han, Qing Lian, Rui Pan, Renjie Pi, Jipeng Zhang, Shizhe Diao, Yong Lin, Tong Zhang

GRAIT: Gradient-Driven Refusal-Aware Instruction Tuning for Effective Hallucination Mitigation (2025.findings-naacl)

Challenge:	Existing multi-modal large language models (MLLMs) are able to process visual inputs by converting them into visual tokens that share the same latent space as language tokens in LLMs.
Approach:	They propose a benchmark that assesses the visual illusion level given spurious images and a pipeline that converts visual inputs into visual tokens.
Outcome:	The proposed benchmark shows that MLLMs suffer from an instinctive bias to varying degrees when presented with spurious images.

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Runchuan Zhu, Xinke Jiang, Jiang Wu, Zhipeng Ma, Jiahe Song, Fengshuo Bai, Dahua Lin, Lijun Wu, Conghui He

Weakly-Supervised Spoken Video Grounding via Semantic Interaction Learning (2023.acl-long)

Challenge:	Experimental evaluations on open-ended and multiple-choice questions demonstrate GRAIT significantly outperforms existing RAIT methods in the overall performance.
Approach:	They propose a framework to reduce the risk of over-refusal and reduce hallucinations by rejecting unknown questions to minimize hallucinism and ensuring correct answers are not rejected.
Outcome:	The proposed framework outperforms existing methods on open-ended and multiple-choice questions.

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Ye Wang, Wang Lin, Shengyu Zhang, Tao Jin, Linjun Li, Xize Cheng, Zhou Zhao

Intrinsic Mutual Information as a Modulator for Preference Optimization (2026.findings-acl)

Challenge:	Recent work on spoken video grounding challenges extracting semantic information from speech . previous studies focused on textual queries, but recent work focuses on spoken queries .
Approach:	They propose a framework for weakly-supervised spoken video grounding to represent cross-modal semantics without expensive temporal annotations.
Outcome:	The proposed framework is more efficient than existing methods.

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Peng Liao, Peijia Zheng, Lingbo Li, Shangsong Liang, Lin Chen

Challenge:	Existing methods for offline preference optimization involve additional hyperparameter tuning, resulting in substantial time overhead.
Approach:	They propose a lightweight framework for offline preference optimization that leverages hyperparameter modulation to decouple preference contributions.
Outcome:	The proposed framework achieves superior performance over existing methods while reducing training overhead by more than 15%.

VOYAGER: A Training Free Approach for Generating Diverse Datasets using LLMs (2026.acl-long)

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Avinash Amballa, Yashas Malur Saidutta, Chi-Heng Lin, Vivek Kulkarni, Srinivas Chappidi

Challenge:	Large language models (LLMs) are used to generate synthetic datasets but lack diversity . prior work has noted that such generated data lacks diversity - a problem that requires domain expertise.
Approach:	They propose a principled approach that optimizes a mathematical quantity that optimize the diversity of the dataset using determinantal point processes.
Outcome:	The proposed method improves diversity by 1.5-3 times compared to baseline approaches.

Inflating Topic Relevance with Ideology: A Case Study of Political Ideology Bias in Social Topic Detection Models (2020.coling-main)

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Meiqi Guo, Rebecca Hwa, Yu-Ru Lin, Wen-Ting Chung

Multi-Path Transformer is Better: A Case Study on Neural Machine Translation (2022.findings-emnlp)

Challenge:	a study examines the impact of political ideology biases in training data . topic detection methods may contain or propagate certain biase resulting in a skewed data collection .
Approach:	They propose to learn a text representation that is invariant to political ideology while still judging topic relevance.
Outcome:	The proposed model can be invariant to political ideology while still judging topic relevance.

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Ye Lin, Shuhan Zhou, Yanyang Li, Anxiang Ma, Tong Xiao, Jingbo Zhu

Joint Entity and Relation Extraction for Legal Documents with Legal Feature Enhancement (2020.coling-main)

Challenge:	Extensive experiments on 12 WMT tasks show that shallower multi-path models can achieve similar or even better performance than the deeper model.
Approach:	They propose to use a parameter-efficient multi-path structure to fuse features extracted from different paths to achieve better performance.
Outcome:	The proposed model can achieve better performance with the same number of parameters than the deeper model.

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Yanguang Chen, Yuanyuan Sun, Zhihao Yang, Hongfei Lin

Tailoring Diagnostic Modeling to Individual Learners: Personalized Distractor Generation via MCTS-Guided Reasoning Reconstruction (2026.acl-long)

Challenge:	Existing methods for information extraction are based on pipelining to extract entities from unstructured judgment documents . a large number of judgment documents are released on China Judgments Online .
Approach:	They propose a legal triplet extraction system for drug-related criminal judgment documents . they annotate a dataset for Named Entity Recognition and Relation Extraction in Chinese legal domain .
Outcome:	The proposed system extracts entities and semantic relations jointly and benefits from the proposed legal lexicon feature and multi-task learning framework.

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Tao Wu, Jingyuan Chen, Wang Lin, Jian Zhan, Mengze Li, Fangzhou Jin, Min Zhang, Kun Kuang, Fei Wu

Pluralistic Alignment for Healthcare: A Role-Driven Framework (2025.emnlp-main)

Challenge:	Current distractor generation methods produce shared distractors for all students, ignoring individual variations in reasoning, which limits their diagnostic effectiveness.
Approach:	They propose a method which tailors distractors to each student’s specific cognitive flaws, inferred from their past question-answering (QA) history.
Outcome:	The proposed framework outperforms existing methods in generating plausible distractors and adapts to group-level settings.

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Jiayou Zhong, Anudeex Shetty, Chao Jia, Xuanrui Lin, Usman Naseem

Challenge:	Existing approaches to align large language models fail to reflect diversity in sensitive domains like healthcare, where personal, cultural, and situational factors shape pluralism.
Approach:	They propose a lightweight, generalizable, pluralistic alignment approach to model diverse perspectives and values in open and closed models.
Outcome:	The proposed approach advances the pluralistic alignment for all three modes across seven varying-sized open and closed models.

Towards building a Robust Industry-scale Question Answering System (2020.coling-industry)

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Rishav Chakravarti, Anthony Ferritto, Bhavani Iyer, Lin Pan, Radu Florian, Salim Roukos, Avi Sil

A Neural-Symbolic Approach to Natural Language Understanding (2022.findings-emnlp)

Challenge:	Existing systems that use “zero-shot transfer learning” (ZSTL) are difficult to train and have observation biases.
Approach:	They propose a production model called GAAMA which has two characteristics . it is robust and efficient, and trains on the recently introduced Natural Questions dataset .
Outcome:	The proposed model performs on two benchmarks: BioASQ and CovidQA.

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Zhixuan Liu, Zihao Wang, Yuan Lin, Hang Li

Grounding Partially-Defined Events in Multimodal Data (2024.findings-emnlp)

Challenge:	Pre-trained language models have enabled deep neural networks to perform natural language understanding tasks, but their performance can drastically deteriorate when logical reasoning is needed.
Approach:	They propose a framework for NLU based on analogical reasoning based upon neural processing and logical reasoning using both neural and symbolic processing.
Outcome:	The proposed framework outperforms state-of-the-art methods on two NLU tasks, question answering (QA) and natural language inference (NLI).

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Kate Sanders, Reno Kriz, David Etter, Hannah Recknor, Alexander Martin, Cameron Carpenter, Jingyang Lin, Benjamin Van Durme

Visual Spatial Description: Controlled Spatial-Oriented Image-to-Text Generation (2022.emnlp-main)

Challenge:	Evidence suggests prelinguistic infants are capable of recognizing discrete events in real-world stimuli.
Approach:	They propose a multimodal formulation for partially-defined events and cast the extraction of these events as a three-stage span retrieval task.
Outcome:	The proposed approach can extract events from 14.5 hours of annotated current event videos and 1,168 text documents, containing 22.8K labeled event-centric entities.

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Yu Zhao, Jianguo Wei, ZhiChao Lin, Yueheng Sun, Meishan Zhang, Min Zhang

Global Structure Knowledge-Guided Relation Extraction Method for Visually-Rich Document (2023.findings-emnlp)

Challenge:	Image-to-text tasks such as captioning and controllable image descriptions have received extensive attention for decades.
Approach:	They propose a new perspective for image-to-text to generate spatial descriptions by combining two objects in an image.
Outcome:	The proposed model is awe-inspiring and human-like, and the proposed end-to-end architecture is the better choice for their integration.

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Xiangnan Chen, Qian Xiao, Juncheng Li, Duo Dong, Jun Lin, Xiaozhong Liu, Siliang Tang

MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing (2026.acl-industry)

Challenge:	Existing methods focus on manipulating entity features to find pairwise relations, yet neglect the more fundamental structural information that links disparate entity pairs together.
Approach:	They propose a Visual Relation Extraction framework that generates relation predictions on entity pairs extracted from scanned images and incorporates global structural knowledge into the representations of the entities.
Outcome:	The proposed framework outperforms existing methods in fine-tuning setting and yields stronger data-efficient performance in the low-resource setting.

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Junbo Niu, Zheng Liu, Zhuangcheng Gu, Bin Wang, Linke Ouyang, Zhiyuan Zhao, Tao Chu, Tianyao He, Fan Wu, Qintong Zhang, Zhenjiang Jin, Guang Liang, Rui Zhang, Wenzheng Zhang, Yuan Qu, Zhifei Ren, Yuefeng Sun, Zirui Tang, Boyu Niu, Yuanhong Zheng, Dongsheng Ma, Ziyang Miao, Hejun Dong, Siyi Qian, Junyuan Zhang, Fangdong Wang, Jingzhou Chen, Xiaomeng Zhao, Liqun Wei, Wei Li, Shasha Wang, RuiLiang Xu, Yuanyuan Cao, Lu Chen, Qianqian Wu, Huaiyu Gu, Lindong Lu, Dechen Lin, null Shenguanlin, Xuanhe Zhou, Linfeng Zhang, Yuhang Zang, Xiaoyi Dong, Jiaqi Wang, Bo Zhang, Lei Bai, Pei Chu, Weijia Li, Jiang Wu, Lijun Wu, Zhenxiang Li, Guangyu Wang, Zhongying Tu, Chao Xu, Kai Chen, Bowen Zhou, Dahua Lin, Wentao Zhang, Conghui He

LLM×MapReduce-V3: Enabling Interactive In-Depth Survey Generation through a MCP-Driven Hierarchically Modular Agent System (2025.emnlp-demos)

Challenge:	Document images are characterized by higher resolutions, denser content, and more complex structural layouts.
Approach:	They propose a 1.2B-parameter document parsing vision-language model that decouples layout analysis from local content recognition.
Outcome:	The proposed model surpasses general-purpose and domain-specific models on multiple benchmarks while maintaining significantly lower computational overhead.

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Yu Chao, Siyu Lin, Xiaorong Wang, Zhu Zhang, Zihan Zhou, Haoyu Wang, Shuo Wang, Jie Zhou, Zhiyuan Liu, Maosong Sun

Challenge:	Generating high-quality long-form survey articles poses significant challenges to AI Agent systems.
Approach:	They propose a hierarchically modular agent system for long-form survey generation . they use atomic models to implement skeleton initialization, digest construction, and skelet refinement . human evaluations demonstrate system surpasses representative baselines .
Outcome:	The proposed system surpasses representative baselines in both content depth and length, highlighting the strength of MCP-based modular planning.

Light-PEFT: Lightening Parameter-Efficient Fine-Tuning via Early Pruning (2024.findings-acl)

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Naibin Gu, Peng Fu, Xiyu Liu, Bowen Shen, Zheng Lin, Weiping Wang

Lightweight Spatial Modeling for Combinatorial Information Extraction From Documents (2023.findings-eacl)

Challenge:	Existing methods for fine-tuning large language models are inefficient and redundant . a light-PEFT framework can be used to prune redundant parameters during training .
Approach:	They propose a parameter-efficient fine-tuning framework that freezes most parameters of the foundation model and finetuns only a small number of parameters.
Outcome:	The proposed framework achieves training and inference speedup, reduces memory usage, and maintains comparable performance and plug-and-play feature of PEFT.

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Yanfei Dong, Lambert Deng, Jiazheng Zhang, Xiaodong Yu, Ting Lin, Francesco Gelli, Soujanya Poria, Wee Sun Lee

Universal NER: A Gold-Standard Multilingual Named Entity Recognition Benchmark (2024.naacl-long)

Challenge:	Existing datasets do not cover documents with complex spatial structures and a lack of spatial information for document entity classification.
Approach:	They propose a new spatial bias in attention calculation based on the K-nearest-neighbor graph of document entities that limits entities’ attention to their local radius.
Outcome:	The proposed model outperforms baselines in most entity types and is highly parameter-efficient compared to existing methods.

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Stephen Mayhew, Terra Blevins, Shuheng Liu, Marek Suppa, Hila Gonen, Joseph Marvin Imperial, Börje Karlsson, Peiqin Lin, Nikola Ljubešić, Lester James Miranda, Barbara Plank, Arij Riabi, Yuval Pinter

GeoQA: A Geometric Question Answering Benchmark Towards Multimodal Numerical Reasoning (2021.findings-acl)

Challenge:	In named entity recognition, the majority of annotation efforts are centered on English, and cross-lingual transfer performance remains brittle.
Approach:	They propose to develop gold-standard named entity recognition benchmarks in many languages using a cross-lingual consistent schema.
Outcome:	The proposed benchmarks will be released to the public in 2022 . they will provide baselines on in-language and cross-lingual learning settings.

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Jiaqi Chen, Jianheng Tang, Jinghui Qin, Xiaodan Liang, Lingbo Liu, Eric Xing, Liang Lin

CorefDiffs: Co-referential and Differential Knowledge Flow in Document Grounded Conversations (2022.coling-1)

Challenge:	Existing methods to solve geometric problems are dependent on handcraft rules and limited on small-scale datasets.
Approach:	They propose a Geometric Question Answering dataset with 5,010 geometric problems with corresponding annotated programs to illustrate the solving process.
Outcome:	The proposed method is significantly lower than human performance on the proposed dataset than on a publicly available dataset.

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Lin Xu, Qixian Zhou, Jinlan Fu, Min-Yen Kan, See-Kiong Ng

SongComposer: A Large Language Model for Lyric and Melody Generation in Song Composition (2025.acl-long)

Challenge:	Document-grounded dialogs need smooth transitions between knowledge selected for generating responses.
Approach:	They propose a multi-document co-referential graph to capture inter- and intra-document relationships . they propose 'Coref-MDG' method to linearize static Coref-mDG into conversational sequence logic.
Outcome:	The proposed method outperforms the state-of-the-art by 9.5%, 7.4% and 8.2% on three public benchmarks.

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Shuangrui Ding, Zihan Liu, Xiaoyi Dong, Pan Zhang, Rui Qian, Junhao Huang, Conghui He, Dahua Lin, Jiaqi Wang

Enhancing Uncertainty-Based Hallucination Detection with Stronger Focus (2023.emnlp-main)

Challenge:	Creating lyrics and melodies in symbolic format requires expert knowledge of melody and an advanced understanding of lyrics.
Approach:	They introduce SongComposer, a music-specialized large language model that can create symbolic lyrics and melodies following instructions.
Outcome:	The proposed model outperforms existing models in symbolic song composition tasks.

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Tianhang Zhang, Lin Qiu, Qipeng Guo, Cheng Deng, Yue Zhang, Zheng Zhang, Chenghu Zhou, Xinbing Wang, Luoyi Fu

Surprisal from Larger Transformer-based Language Models Predicts fMRI Data More Poorly (2026.eacl-short)

Challenge:	Existing methods for detecting hallucinations in LLMs rely on external knowledge for reference retrieval or require sampling multiple responses for consistency verification.
Approach:	They propose a reference-free, uncertainty-based method for detecting hallucinations in Large Language Models that imitates human focus in factuality checking from three aspects: focus on the most informative keywords; focus on unreliable tokens in historical context; focus of token properties such as token type and token frequency.
Outcome:	The proposed method achieves state-of-the-art performance across all evaluation metrics and eliminates the need for additional information.

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Yi-Chien Lin, William Schuler

Query-as-context Pre-training for Dense Passage Retrieval (2023.emnlp-main)

Challenge:	Recent work has observed an inverse scaling relationship between Transformers’ per-word estimated probability and the predictive power of their surprisal estimates on reading times.
Approach:	They conducted a more comprehensive evaluation using surprisal estimates from 17 pre-trained LMs on two functional magnetic resonance imaging datasets.
Outcome:	Recent work shows that surprisal from larger Transformer-based models is less predictive of reading times, resolving the inconclusive results and indicating that this trend is not specific to latency-based measures.

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Xing W, Guangyuan Ma, Wanhui Qian, Zijia Lin, Songlin Hu

Few-shot Named Entity Recognition via Superposition Concept Discrimination (2024.lrec-main)

Challenge:	Existing methods to improve passage retrieval performance by using context-supervised pre-training are weakly correlated.
Approach:	They propose to use query-as-context pre-training to train passage-query pairs . they evaluate the pre-trained models on large-scale passage retrieval benchmarks .
Outcome:	The proposed technique improves performance on large-scale passage retrieval benchmarks and out-of-domain zero-shot benchmarks.

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Jiawei Chen, Hongyu Lin, Xianpei Han, Yaojie Lu, Shanshan Jiang, Bin Dong, Le Sun

Incorporating EDS Graph for AMR Parsing (2021.starsem-1)

Challenge:	Few-shot named entity recognition (NER) aims to identify entities of target types with limited number of illustrative instances.
Approach:	They propose a superposition concept discriminator which solves the intrinsic generalization problem by an active learning paradigm.
Outcome:	The proposed model significantly improves few-shot named entity recognition (FS-NER) with minimal additional efforts.

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Ziyi Shou, Fangzhen Lin

Emphasising Structured Information: Integrating Abstract Meaning Representation into LLMs for Enhanced Open-Domain Dialogue Evaluation (2025.findings-emnlp)

Challenge:	AMR is abstract and conceptual, while EDS is low level, closer to the lexical structures of the given sentences.
Approach:	They propose to add EDS graphs as additional semantic features to AMR parsers by adding transition-based parser to add LSTM layer and GCN layer.
Outcome:	The proposed parser adds EDS graphs as additional semantic features to boost performance . Currently the parsing accuracies for AMR are in low 80s, while they can be improved by adding more information from EDS.

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Bohao Yang, Kun Zhao, Dong Liu, Chen Tang, Liang Zhan, Chenghua Lin

LLM-MC-Affect: LLM-Based Monte Carlo Modeling of Affective Trajectories and Latent Ambiguity for Interpersonal Dynamic Insight (2026.acl-long)

Challenge:	Existing evaluation metrics struggle to evaluate adversarial negative examples . existing metrics struggle in handling adversarials, resulting in low correlations with human judgments.
Approach:	They propose a framework that integrates AMR and domain-specific language models for automatic open-domain dialogue evaluation.
Outcome:	The proposed evaluation framework achieves strong correlations with human judgments across multiple datasets.

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Yu-Zheng Lin, Bono Po-Jen Shih, John Paul Martin Encinas, Elizabeth Victoria Achom, Karan Patel, Jesus Horacio Pacheco, Sicong Shao, Jyotikrishna Dass, Soheil Salehi, Pratik Satam

Exploring the Effectiveness and Consistency of Task Selection in Intermediate-Task Transfer Learning (2024.acl-srw)

Challenge:	Emotional coordination is a core property of human interaction that shapes relational meaning . prior approaches treat sentiment as a deterministic point estimate for individual speakers . scalable and deployable approach extends beyond education to broader social and behavioral research .
Approach:	They propose a probabilistic framework that characterizes emotion as a latent probability distribution defined over an affective space.
Outcome:	The proposed framework characterizes emotion as a latent probability distribution defined over affective space.

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Pin-Jie Lin, Miaoran Zhang, Marius Mosbach, Dietrich Klakow

LLM Self-Correction with DeCRIM: Decompose, Critique, and Refine for Enhanced Following of Instructions with Multiple Constraints (2024.findings-emnlp)

Challenge:	Identifying beneficial tasks to transfer from is a critical step toward successful intermediate-task transfer learning.
Approach:	They propose a method that measures pairwise token similarity using maximum inner product search to improve task prediction.
Outcome:	The proposed method improves task prediction scores from 2.59% to 3.96% for tasks requiring reasoning abilities, but not for reasoning abilities.

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Thomas Palmeira Ferraz, Kartik Mehta, Yu-Hsiang Lin, Haw-Shiuan Chang, Shereen Oraby, Sijia Liu, Vivek Subramanian, Tagyoung Chung, Mohit Bansal, Nanyun Peng

TMFN: A Target-oriented Multi-grained Fusion Network for End-to-end Aspect-based Multimodal Sentiment Analysis (2024.lrec-main)

Challenge:	Recent studies have shown that LLMs struggle with instructions containing multiple constraints.
Approach:	They propose a self-correction pipeline that decomposes the original instruction into a list of constraints and uses a Critic model to decide when and where the LLM’s response needs refinement.
Outcome:	The proposed model outperforms GPT-4 on RealInstruct and IFEval even with weak feedback.

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Di Wang, Yuzheng He, Xiao Liang, Yumin Tian, Shaofeng Li, Lin Zhao

CartesianMoE: Boosting Knowledge Sharing among Experts via Cartesian Product Routing in Mixture-of-Experts (2025.naacl-long)

Challenge:	Existing methods for multimodal aspect-based sentiment analysis focus on fusing image regional information and textual words.
Approach:	They propose a multimodal aspect-based sentiment analysis method that integrates regional and global image information with global image data.
Outcome:	Experiments show that the proposed method outperforms state-of-the-art methods on two benchmark datasets.

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Zhenpeng Su, Xing W, Zijia Lin, Yizhe Xiong, Minxuan Lv, Guangyuan Ma, Hui Chen, Songlin Hu, Guiguang Ding

CogToM: A Comprehensive Theory of Mind Benchmark inspired by Human Cognition for Large Language Models (2026.acl-long)

Challenge:	Large language models (LLMs) have been attracting much attention due to their impressive performance in all kinds of downstream tasks.
Approach:	They propose a mix-of-experts model that allows the model size to grow without raising training costs.
Outcome:	The proposed model outperforms existing models in perplexity and robustness tests.

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Haibo Tong, Zeyang Yue, Feifei Zhao, Erliang Lin, Lu Jia, Ruolin Chen, Yinqian Sun, Qian Zhang, Yi Zeng

Linguistics & Cognitive NLP

Challenge:	Existing benchmarks for Large Language Models (LLMs) are limited to false belief tasks, highlighting bottlenecks in specific dimensions.
Approach:	They propose a benchmark to evaluate Large Language Models' Theory of Mind capabilities . they evaluate 8000 bilingual instances across 46 paradigms and validated by 49 human annotators .
Outcome:	The proposed benchmark reveals performance heterogeneities and bottlenecks in 22 representative models.

CAGK: Collaborative Aspect Graph Enhanced Knowledge-based Recommendation (2024.lrec-main)

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Xiaotong Song, Huiping Lin, Jiatao Zhu, Xinyi Gong

Revisiting Distant Supervision for Relation Extraction (L18-1)

Challenge:	Existing KG-based recommendations have low link rates, redundant knowledge in KG, and low ratings and negative aspect sentiment.
Approach:	They propose a model that integrates auxiliary information such as social networks, user or item attributes, images, contextual data, etc.
Outcome:	The proposed model improves on two widely used benchmark datasets, Amazon-book and Yelp2018.

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Tingsong Jiang, Jing Liu, Chin-Yew Lin, Zhifang Sui

Linguistic Neuron Overlap Patterns to Facilitate Cross-lingual Transfer on Low-resource Languages (2025.emnlp-main)

Challenge:	Existing approaches for relation extraction (RE) use supervised learning on relation-specific training data, which is expensive to acquire.
Approach:	They propose to use a new testing dataset to re-examine distant supervision approaches . they aim to draw new conclusions based on the new testing data .
Outcome:	The proposed method can generate training data without noise and bias issues . the proposed method is annotated by the researchers on Amzaon Mechanical Turk .

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Yuemei Xu, Kexin Xu, Jian Zhou, Ling Hu, Lin Gui

Just Like a Human Would, Direct Access to Sarcasm Augmented with Potential Result and Reaction (2023.acl-long)

Challenge:	Recent studies have focused on data-efficient methods, particularly Cross-lingual In-Context Learning (X-ICL)
Approach:	They propose a method to improve cross-lingual in-context learning for low-resource languages by using language-specific neurons.
Outcome:	The proposed method improves cross-lingual performance on low-resource languages by ensuring full activation of language overlap neurons.

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Changrong Min, Ximing Li, Liang Yang, Zhilin Wang, Bo Xu, Hongfei Lin

GEM: A General Evaluation Benchmark for Multimodal Tasks (2021.findings-acl)

Challenge:	sarcasm is a form of irony conveying mockery and contempt . social media has become increasingly popular for identifying sarcasm .
Approach:	They develop a method to detect sarcasm from social media using augmented potentials.
Outcome:	The proposed method outperforms baselines on benchmark datasets.

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Lin Su, Nan Duan, Edward Cui, Lei Ji, Chenfei Wu, Huaishao Luo, Yongfei Liu, Ming Zhong, Taroon Bharti, Arun Sacheti

Weakly-Supervised Temporal Article Grounding (2022.emnlp-main)

Challenge:	Existing datasets that focus on natural language tasks are not considered as a general evaluation benchmark for multimodal tasks.
Approach:	They present a general evaluation benchmark for multimodal tasks, GEM 1 . they compare it with existing multimodal vision-language datasets .
Outcome:	The proposed model is compared with existing vision-language datasets focusing on natural language tasks . it is the largest vision-linguistic dataset covering image-language tasks and video-language task at the same time .

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Long Chen, Yulei Niu, Brian Chen, Xudong Lin, Guangxing Han, Christopher Thomas, Hammad Ayyubi, Heng Ji, Shih-Fu Chang

Don’t Change Me! User-Controllable Selective Paraphrase Generation (2021.eacl-main)

Challenge:	Existing VG models make unrealistic assumptions about how to ground video segments . a recent study has shown that video grounding can be useful for downstream applications .
Approach:	They propose a new task: Weakly-Supervised temporal Article Grounding (WSAG) given an article and a relevant video, WSAG aims to localize all "groundable" sentences to the video.
Outcome:	The proposed method is simple but effective, and it can be used in real-world applications.

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Mohan Zhang, Luchen Tan, Zihang Fu, Kun Xiong, Jimmy Lin, Ming Li, Zhengkai Tu

Do VLMs Have a Moral Backbone? A Study on the Fragile Morality of Vision-Language Models (2026.findings-acl)

Challenge:	a new technique allows paraphrase generation to be user-controlled . a user looking for cheap hotels in New York would not find the other answer helpful .
Approach:	They propose a method that provides a user with explicit tags that can be placed around any arbitrary segment of text to mean "don't change me!" they propose allowing user-controllable paraphrase generation by fine-tuning model that exhibits this behavior .
Outcome:	The proposed technique is language agnostic and tested in English and Chinese.

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Zhining Liu, Tianyi Wang, Xiao Lin, Penghao Ouyang, Gaotang Li, Ze Yang, Hui Liu, Sumit Keswani, Vishwa Pardeshi, Huijun Zhao, Wei Fan, Hanghang Tong

Beyond Memorization: The Challenge of Random Memory Access in Language Models (2024.acl-long)

Challenge:	Vision-Language Models (VLMs) have advanced multimodal learning, driving progress in cross-modal reasoning.
Approach:	They propose to examine moral robustness of vision-language models by analyzing their moral stances under multimodal perturbations.
Outcome:	The proposed model-agnostic multimodal perturbations expose VLMs to a variety of moral vulnerabilities, including a sycophancy trade-off where stronger instruction-following models are more susceptible to persuasion.

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Tongyao Zhu, Qian Liu, Liang Pang, Zhengbao Jiang, Min-Yen Kan, Min Lin

The Mystery of In-Context Learning: A Comprehensive Survey on Interpretation and Analysis (2024.emnlp-main)

Challenge:	Recent advances in Language Models (LMs) have shown their effectiveness in knowledge-intensive tasks.
Approach:	They investigate whether a generative language model is able to access its memory sequentially or randomly.
Outcome:	The proposed LMs are able to access memory sequentially or randomly.

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Yuxiang Zhou, Jiazheng Li, Yanzheng Xiang, Hanqi Yan, Lin Gui, Yulan He

Denoising Bottleneck with Mutual Information Maximization for Video Multimodal Fusion (2023.acl-long)

Challenge:	In-context learning (ICL) is a capability that enables large language models to excel in proficiency through demonstration examples.
Approach:	They present a survey on the interpretation and analysis of in-context learning . they focus on theoretical and empirical perspectives on the concept .
Outcome:	The proposed model can perform tasks with minimal examples without re-training and has demonstrated proficiency across various tasks with a minimal set of task-oriented examples.

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Shaoxiang Wu, Damai Dai, Ziwei Qin, Tianyu Liu, Binghuai Lin, Yunbo Cao, Zhifang Sui

Bridging the Gap between Relevance Matching and Semantic Matching for Short Text Similarity Modeling (D19-1)

Challenge:	Prior denoising methods suppress redundant and noisy information at risk of losing critical information.
Approach:	They propose a denoising bottleneck fusion model for fine-grained video multimodal fusion . they employ a bottleneck mechanism to filter out noise and redundancy with a restrained receptive field .
Outcome:	The proposed model improves on state-of-the-art video multimodal fusion benchmarks.

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Jinfeng Rao, Linqing Liu, Yi Tay, Wei Yang, Peng Shi, Jimmy Lin

Robust Adaptation of Large Multimodal Models for Retrieval Augmented Hateful Meme Detection (2025.emnlp-main)

Challenge:	Existing techniques for relevance and semantic matching cannot be easily adapted to the other.
Approach:	They propose a model that incorporates a hybrid encoder module, a relevance matching module and co-attention mechanisms that capture context-aware semantic relatedness.
Outcome:	The proposed model incorporates a hybrid encoder module, a relevance matching module and co-attention mechanisms that capture context-aware semantic relatedness.

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Jingbiao Mei, Jinghong Chen, Guangyu Yang, Weizhe Lin, Bill Byrne

PaperRegister: Boosting Flexible-grained Paper Search via Hierarchical Register Indexing (2026.acl-long)

Challenge:	Large Multimodal Models (LMMs) have shown promise in hateful meme detection, but they face limitations like sub-optimal performance and limited out-of-domain generalization capabilities.
Approach:	They propose a robust adaptation framework for hateful meme detection that enhances in-domain accuracy and cross-domain generalization while preserving the general vision-language capabilities of LMMs.
Outcome:	The proposed framework outperforms larger agentic systems in detecting hateful memes under adversarial attacks while maintaining the general vision-language capabilities of LMMs.

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Zhuoqun Li, Xuanang Chen, Hongyu Lin, Yaojie Lu, Xianpei Han, Shanshan Jiang, Bin Dong, Le Sun

A Co-Attentive Cross-Lingual Neural Model for Dialogue Breakdown Detection (2020.coling-main)

Challenge:	Existing paper search systems lack detailed information to support finer-grained queries.
Approach:	They propose a paper-based index that transforms abstract-based corpus index into hierarchical index tree and offline can support paper search queries.
Outcome:	The proposed system achieves the SOTA performance and excels in fine-grained scenarios.

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Qian Lin, Souvik Kundu, Hwee Tou Ng

DiVE: Decoupling Intra-layer Visual Evidence for Mitigating Hallucinations in Large Vision-Language Models (2026.acl-long)

Challenge:	Existing models for dialogue breakdown detection do not focus on preventing dialogue breakdowns.
Approach:	They propose a model that integrates a pretrained cross-lingual language model and a co-attention network for dialogue breakdown detection.
Outcome:	The proposed model outperforms all previous approaches on evaluation metrics in Japanese and English tracks in Dialogue Breakdown Detection Challenge 4 .

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Xinwei Li, Li Lin, Hui Jiao, Li Yao, Tien-Tsin Wong, Hanqian Wu

Sparse Activation Editing for Reliable Instruction Following in Narratives (2025.emnlp-main)

Challenge:	Existing decoding-based approaches do not explicitly decouple visual evidence from mixed vision–language representations.
Approach:	They propose to decouple visual evidence from mixed vision–language representations by dynamically identifying layers enriched with visual information and performing intra-layer decoupling to extract aggregated visual evidence.
Outcome:	Experiments show that DiVE achieves state-of-the-art performance on multiple benchmarks.

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Runcong Zhao, Chengyu Cao, Qinglin Zhu, Xiucheng Ly, Shun Shao, Lin Gui, Ruifeng Xu, Yulan He

Challenge:	Existing benchmarks fail to capture the challenges of instruction following in complex narrative contexts.
Approach:	They propose a training-free framework that identifies and edits instruction-relevant neurons using only natural language instructions without requiring labelled data.
Outcome:	The proposed framework improves instruction following by identifying and editing instruction-relevant neurons using only natural language instructions, without requiring labelled data.

Resilience of Large Language Models for Noisy Instructions (2024.findings-emnlp)

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Bin Wang, Chengwei Wei, Zhengyuan Liu, Geyu Lin, Nancy Chen

AdaptThink: Reasoning Models Can Learn When to Think (2025.emnlp-main)

Challenge:	Large language models (LLMs) are powerful tools for interpreting human commands and generating text.
Approach:	They examine the resilience of large language models against five common types of disruptions including ASR, OCR, grammatical errors, typographical errors and distractive content.
Outcome:	The models show resistance to noise, but their performance suffers . authors evaluated the models against five common types of disruptions based on their results .

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Jiajie Zhang, Nianyi Lin, Lei Hou, Ling Feng, Juanzi Li

NAMER: A Node-Based Multitasking Framework for Multi-Hop Knowledge Base Question Answering (2021.naacl-demos)

Challenge:	Recent advances in large reasoning models have demonstrated remarkable capabilities in tackling complex tasks.
Approach:	They propose an algorithm to teach reasoning models to choose the optimal thinking mode based on problem difficulty.
Outcome:	The proposed algorithm reduces the average response length and improves accuracy on three math datasets.

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Minhao Zhang, Ruoyu Zhang, Lei Zou, Yinnian Lin, Sen Hu

Virtual Context Enhancing Jailbreak Attacks with Special Token Injection (2024.findings-emnlp)

Challenge:	Using a node-based framework, knowledge base question answering systems can grasp structural mappings between questions and KB queries.
Approach:	They propose a node-based framework that better grasps the structural mapping between questions and KB queries by aligning the nodes in a query with their corresponding mentions in question.
Outcome:	The proposed framework outperforms the previous SoTA on CCKS CKBQA dataset.

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Yuqi Zhou, Lin Lu, Ryan Sun, Pan Zhou, Lichao Sun

Interactive Learning for LLM Reasoning (2026.findings-acl)

Challenge:	Existing jailbreak attacks target the two phases of user interaction: prompt input and model computation.
Approach:	They propose a new tool that leverages special tokens to improve jailbreak attacks . they found that the tool can increase success rates of existing jailbreak methods by 40% .
Outcome:	The proposed solution can improve success rates of four widely used jailbreak methods by approximately 40% across various LLMs.

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Hehai Lin, Shilei Cao, Sudong Wang, Haotian Wu, Minzhi Li, Linyi Yang, Juepeng Zheng, Chengwei Qin

Is the Brain Mechanism for Hierarchical Structure Building Universal Across Languages? An fMRI Study of Chinese and English (2022.emnlp-main)

Challenge:	Existing multi-agent learning approaches foster collaboration among Large Language Models (LLMs) yet they still rely on re-executing the MAS during inference.
Approach:	They propose a co-learning framework that integrates Dynamic Interaction and Perception Calibration to enhance LLMs' independent problem-solving ability.
Outcome:	The proposed framework integrates Dynamic Interaction and Perception Calibration to improve LLMs' independent problem-solving ability.

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Xiaohan Zhang, Shaonan Wang, Nan Lin, Chengqing Zong

Guiding the Growth: Difficulty-Controllable Question Generation through Step-by-Step Rewriting (2021.acl-long)

Challenge:	Existing studies have shown that the brain builds hierarchical syntactic structures, but it is unknown whether they are universal across languages.
Approach:	They analyze the working memory requirements when applying parsing strategies to two languages: Chinese and English.
Outcome:	The proposed method shows that the brain adopts parsing strategies with less memory load according to different language structures.

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Yi Cheng, Siyao Li, Bang Liu, Ruihui Zhao, Sujian Li, Chenghua Lin, Yefeng Zheng

SciAssess: Benchmarking LLM Proficiency in Scientific Literature Analysis (2025.findings-naacl)

Challenge:	Existing QG systems perform substantially worse in answering multi-hop questions than single-hop ones.
Approach:	They propose a framework that progressively increases question difficulty through step-by-step rewriting under the guidance of an extracted reasoning chain.
Outcome:	The proposed framework increases question difficulty through step-by-step rewriting under the guidance of an extracted reasoning chain.

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Hengxing Cai, Xiaochen Cai, Junhan Chang, Sihang Li, Lin Yao, Wang Changxin, Zhifeng Gao, Hongshuai Wang, Li Yongge, Mujie Lin, Shuwen Yang, Jiankun Wang, Mingjun Xu, Jin Huang, Xi Fang, Jiaxi Zhuang, Yuqi Yin, Yaqi Li, Changhong Chen, Zheng Cheng, Zifeng Zhao, Linfeng Zhang, Guolin Ke

Challenge:	Existing benchmarks fail to adequately evaluate the proficiency of Large Language Models (LLMs) Existing standards do not cover the skills needed to evaluate LLMs in scientific literature analysis.
Approach:	They propose a benchmark to evaluate the proficiency of large language models in scientific literature analysis.
Outcome:	SciAssess evaluates 11 LLMs on multiple tasks across scientific fields.

Instruct Once, Chat Consistently in Multiple Rounds: An Efficient Tuning Framework for Dialogue (2024.acl-long)

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Jian Wang, Chak Tou Leong, Jiashuo Wang, Dongding Lin, Wenjie Li, Xiaoyong Wei

Predicting Turn-Taking and Backchannel in Human-Machine Conversations Using Linguistic, Acoustic, and Visual Signals (2025.acl-long)

Challenge:	Tuning language models for dialogue generation has been a prevalent paradigm for building capable dialogue agents.
Approach:	They propose a multi-round interactive dialogue tuning framework that models the speaker roles of agent and user separately.
Outcome:	The proposed framework performs superior to fine-tuning and improves dialogue consistency.

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Yuxin Lin, Yinglin Zheng, Ming Zeng, Wangzheng Shi

LTRAG: Enhancing Autoformalization and Self-refinement for Logical Reasoning with Thought-Guided RAG (2025.findings-acl)

Challenge:	Existing systems for human-machine conversations are limited in predicting turn-taking and backchannel actions.
Approach:	They propose a multi-modal face-to-face (MM-F2F) human conversation dataset . they collect and annotate over 210 hours of human conversation videos .
Outcome:	The proposed model achieves state-of-the-art on turn-taking and backchannel prediction tasks.

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Ruikang Hu, Shaoyu Lin, Yeliang Xiu, Yongmei Liu

MMEvol: Empowering Multimodal Large Language Models with Evol-Instruct (2025.findings-acl)

Challenge:	Large language models (LLMs) have shown promise in natural language reasoning, especially with techniques like chain-of-thought prompting.
Approach:	They propose a framework to enhance autoformalization and self-refinement for logical reasoning with Retrieval-Augmented Generation (RAG) by building knowledge bases of thought-guided examples.
Outcome:	The proposed framework outperforms Logic-LM and LINC on FOLIO and AR-LSAT, and achieves an accuracy gain of 13% over Logic LM and the proposed methods on GPT-4 and AR LSAT.

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Run Luo, Haonan Zhang, Longze Chen, Ting-En Lin, Xiong Liu, Yuchuan Wu, Min Yang, Yongbin Li, Minzheng Wang, Pengpeng Zeng, Lianli Gao, Heng Tao Shen, Yunshui Li, Hamid Alinejad-Rokny, Xiaobo Xia, Jingkuan Song, Fei Huang

Understanding and Mitigating Spurious Correlations in Text Classification with Neighborhood Analysis (2024.findings-eacl)

Challenge:	a new framework for image-text instruction data evolution improves MLLM performance . lack of high-quality instruction data remains a major bottleneck in ML modeling .
Approach:	They propose a multimodal instruction data evolution framework that iteratively enhances data quality through fine-grained perception, cognitive reasoning, and interaction evolution.
Outcome:	The proposed approach improves MLLM performance in nine vision-language tasks while using significantly less data.

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Oscar Chew, Hsuan-Tien Lin, Kai-Wei Chang, Kuan-Hao Huang

DiVISe: Direct Visual-Input Speech Synthesis Preserving Speaker Characteristics And Intelligibility (2025.findings-naacl)

Challenge:	Recent research has revealed that machine learning models have a tendency to leverage spurious correlations that exist in the training set but may not hold true in general circumstances.
Approach:	They propose a metric to detect spurious tokens and a family of regularization methods to mitigate spurious correlations in text classification.
Outcome:	The proposed method prevents spurious clusters and significantly improves the robustness of classifiers without auxiliary data.

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Yifan Liu, Yu Fang, Zhouhan Lin

Designing Multilingual Interactive Agents using Small Dialogue Corpora (2020.lrec-1)

Challenge:	Video-to-speech (V2S) synthesis requires acoustic hints to accurately reconstruct both speech content and speaker characteristics from video clips alone.
Approach:	They propose a video-to-speech (V2S) model that predicts Mel-spectrograms directly from video frames.
Outcome:	The proposed model outperforms existing models in acoustic intelligibility and preserves speaker-specific characteristics.

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Donghui Lin, Masayuki Otani, Ryosuke Okuno, Toru Ishida

ReAct Meets Industrial IoT: Language Agents for Data Access (2025.emnlp-industry)

Challenge:	a new study aims to develop a design framework for multilingual interactive agents . large amounts of data and language resources are needed to develop most key components .
Approach:	They propose a general design framework for multilingual interactive agents in specialized domains with small or non-existent dialogue corpora.
Outcome:	The proposed framework integrates external language services for supporting multilingual functions and realizes context-aware dialogue generation under the situation of small corpora.

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James T Rayfield, Shuxin Lin, Nianjun Zhou, Dhaval C Patel

Multimodal Table Understanding (2024.acl-long)

Challenge:	a framework for domain-specific language agents is being developed for industrial automation . a novel approach to adapting these systems to domain-based applications poses new challenges .
Approach:	They propose a framework for deploying domain-specific language agents that can query industrial sensor data using natural language.
Outcome:	The proposed framework outperforms standard prompting baselines across multiple LLMs including smaller models.

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Mingyu Zheng, Xinwei Feng, Qingyi Si, Qiaoqiao She, Zheng Lin, Wenbin Jiang, Weiping Wang

ExAnte: A Benchmark for Ex-Ante Inference in Large Language Models (2026.eacl-long)

Challenge:	Existing approaches to understanding tables rely on textual inputs and table images are difficult to access in real-world scenarios.
Approach:	They propose a multimodal table understanding problem where the model needs to generate correct responses to various table-related requests based on the given table image.
Outcome:	The proposed model outperforms open-source MLLMs on 23 benchmarks under held-in and held-out settings.

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Yachuan Liu, Xiaochun Wei, Lin Shi, Xinnuo Li, Bohan Zhang, Paramveer Dhillon, Qiaozhu Mei

Efficient Large Scale Language Modeling with Mixtures of Experts (2022.emnlp-main)

Challenge:	Large language models (LLMs) struggle with ex-ante reasoning—making inferences or predictions without access to future information.
Approach:	They propose a benchmark that assesses LLMs’ ex-ante inference ability across four tasks: stock prediction, question answering, Wikipedia event generation, and scientific publication generation.
Outcome:	The proposed benchmark assesses LLMs’ ex-ante inference ability across four tasks.

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Mikel Artetxe, Shruti Bhosale, Naman Goyal, Todor Mihaylov, Myle Ott, Sam Shleifer, Xi Victoria Lin, Jingfei Du, Srinivasan Iyer, Ramakanth Pasunuru, Giridharan Anantharaman, Xian Li, Shuohui Chen, Halil Akin, Mandeep Baines, Louis Martin, Xing Zhou, Punit Singh Koura, Brian O’Horo, Jeffrey Wang, Luke Zettlemoyer, Mona Diab, Zornitsa Kozareva, Veselin Stoyanov

Adaptive Scaling for Sparse Detection in Information Extraction (P18-1)

Challenge:	Mixture of Experts layers (MoEs) enable efficient scaling of language models . large autoregressive language models such as GPT-3 can be adapted to a wide range of tasks .
Approach:	They propose to use Mixture of Experts layers to enable efficient scaling of language models . they find that MoEs are substantially more compute efficient than dense models compared to MoE models - but only when they are more modestly trained .
Outcome:	The proposed model outperforms dense models in a wide range of tasks and domains.

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Hongyu Lin, Yaojie Lu, Xianpei Han, Le Sun

Prometheus 2: An Open Source Language Model Specialized in Evaluating Other Language Models (2024.emnlp-main)

Challenge:	Detection problems involving positive instances are often deficient in information extraction tasks . a number of researches have employed neural network models to solve detection problems .
Approach:	They propose an algorithm which can handle positive sparsity problem and directly optimize over F-measure . they borrow the idea of marginal utility from economics and propose a theoretical framework for instance importance measuring .
Outcome:	The proposed algorithm improves on positive sparsity problem and over F-measure . it leads to more effective and stable training of neural network based detection models.

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Seungone Kim, Juyoung Suk, Shayne Longpre, Bill Yuchen Lin, Jamin Shin, Sean Welleck, Graham Neubig, Moontae Lee, Kyungjae Lee, Minjoon Seo

AnnaAgent: Dynamic Evolution Agent System with Multi-Session Memory for Realistic Seeker Simulation (2025.findings-acl)

Challenge:	Existing open-source evaluation paradigms lack flexibility and performance . language model-based evaluation is cheap and scalable, but it is difficult to evaluate .
Approach:	They propose a language model-based evaluation paradigm that uses a scalar indicator of quality to assess LM outputs.
Outcome:	The proposed language model-based evaluation model is more powerful than its predecessor.

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Ming Wang, Peidong Wang, Lin Wu, Xiaocui Yang, Daling Wang, Shi Feng, Yuxin Chen, Bixuan Wang, Yifei Zhang

Zero-Shot Dialogue State Tracking via Cross-Task Transfer (2021.emnlp-main)

Challenge:	Existing models of seeker simulations are limited by the cost and ethical concerns of involving real seekers in mental health research.
Approach:	They propose an emotional and cognitive dynamic agent system equipped with tertiary memory to enable dynamic control of the simulator's configurations.
Outcome:	The proposed system achieves more realistic seeker simulation compared to baselines.

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Zhaojiang Lin, Bing Liu, Andrea Madotto, Seungwhan Moon, Zhenpeng Zhou, Paul Crook, Zhiguang Wang, Zhou Yu, Eunjoon Cho, Rajen Subba, Pascale Fung

Adversarial Learning of Poisson Factorisation Model for Gauging Brand Sentiment in User Reviews (2021.eacl-main)

Challenge:	Existing approaches to training a dialogue state tracking model require extensive annotated dialogue data.
Approach:	They propose to transfer cross-task knowledge from general question answering corpora to QA model that can handle zero-shot DST.
Outcome:	The proposed model improves existing zero-shot and few-shot results on MultiWoz and shows better generalization ability in unseen domains.

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Runcong Zhao, Lin Gui, Gabriele Pergola, Yulan He

How Many Answers Should I Give? An Empirical Study of Multi-Answer Reading Comprehension (2023.findings-acl)

Challenge:	Existing models for sentiment-topic extraction assume topics are grouped under discrete sentiment categories such as ‘positive’, ‘negative’ and ‘neural’.
Approach:	They propose a Brand-Topic Model which aims to detect brand-associated polarity-bearing topics from product reviews.
Outcome:	The proposed model outperforms existing models on Amazon reviews and shows that it is more coherent and unique than existing models.

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Chen Zhang, Jiuheng Lin, Xiao Liu, Yuxuan Lai, Yansong Feng, Dongyan Zhao

Meta-Information Guided Meta-Learning for Few-Shot Relation Classification (2020.coling-main)

Challenge:	Despite recent progress in multi-answer MRC, there is no systematic analysis of how this phenomenon arises and how to better address it.
Approach:	They develop a taxonomy to categorize commonly-seen multi-answer MRC instances and examine how well different paradigms deal with different types of multi-announced questions.
Outcome:	The proposed taxonomy categorizes commonly-seen multi-answer instances and analyzes how well different paradigms deal with different types of multi-announced instances.

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Bowen Dong, Yuan Yao, Ruobing Xie, Tianyu Gao, Xu Han, Zhiyuan Liu, Fen Lin, Leyu Lin, Maosong Sun

Few-shot Learning with Multilingual Generative Language Models (2022.emnlp-main)

Challenge:	Existing meta-learning models rely on implicit instance statistics and are unreliability and weak interpretability.
Approach:	They propose a meta-information guided meta-learning framework that uses semantics to guide meta- learning . experimental results demonstrate the effectiveness of the proposed framework .
Outcome:	The proposed framework can establish connections between instance-based information and semantic-based data, enabling faster initialization and adaptation.

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Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O’Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, Xian Li

VDebugger: Harnessing Execution Feedback for Debugging Visual Programs (2024.findings-emnlp)

Challenge:	Large-scale generative language models such as GPT-3 are competitive few-shot learners.
Approach:	They train multilingual generative language models on a corpus covering a diverse set of languages and study their few- and zero-shot learning capabilities.
Outcome:	The proposed model outperforms GPT-3 on 171 out of 182 directions with 32 training examples and surpasses the official supervised baseline in 45 directions.

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Xueqing Wu, Zongyu Lin, Songyan Zhao, Te-Lin Wu, Pan Lu, Nanyun Peng, Kai-Wei Chang

Making Language Models Better Reasoners with Step-Aware Verifier (2023.acl-long)

Challenge:	Visual programs are executable code generated by large language models to address visual reasoning problems.
Approach:	They propose a critic-refiner framework that localizes and debugs visual programs by tracking execution step by step.
Outcome:	The proposed framework detects and corrects program errors leveraging detailed execution feedback, improving interpretability and accuracy.

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Yifei Li, Zeqi Lin, Shizhuo Zhang, Qiang Fu, Bei Chen, Jian-Guang Lou, Weizhu Chen

Sentiment-Aware Word and Sentence Level Pre-training for Sentiment Analysis (2022.emnlp-main)

Challenge:	Large language models have made impressive progress in few-shot learning but still face difficulties in reasoning tasks such as GSM8K.
Approach:	They propose a new approach that uses a verifier to filter out incorrect answers based on a weighted voting scheme to improve reasoning ability of language models.
Outcome:	The proposed approach improves GSM8K reasoning rate by 17.9% to 58.1%.

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Shuai Fan, Chen Lin, Haonan Li, Zhenghao Lin, Jinsong Su, Hang Zhang, Yeyun Gong, JIan Guo, Nan Duan

Mitigating the Alignment Tax of RLHF (2024.emnlp-main)

Challenge:	Existing pre-trained language representation models (PLMs) capture sentiment information from word-level while under-considering sentence-level information.
Approach:	They propose a Sentiment-aware pre-trained language model with combined Word-level and Sentence-level Pre-training tasks that enhance the PLM’s knowledge about sentiment words.
Outcome:	The proposed model achieves state-of-the-art on various sentence-level and aspect-level sentiment classification benchmarks.

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Yong Lin, Hangyu Lin, Wei Xiong, Shizhe Diao, Jianmeng Liu, Jipeng Zhang, Rui Pan, Haoxiang Wang, Wenbin Hu, Hanning Zhang, Hanze Dong, Renjie Pi, Han Zhao, Nan Jiang, Heng Ji, Yuan Yao, Tong Zhang

CFO: A Framework for Building Production NLP Systems (D19-3)

Challenge:	Large Language Models (LLMs) acquire a wide range of abilities during pre-training, but aligning LLMs under Reinforcement Learning with Human Feedback (RLHF) can lead to forgetting pretrained abilities, which is also known as the alignment tax.
Approach:	They propose to use a model averaging technique to find the most powerful alignment-forging Pareto front among RLHF algorithms.
Outcome:	The proposed method achieves the strongest alignment-forging Pareto front among competing methods.

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Rishav Chakravarti, Cezar Pendus, Andrzej Sakrajda, Anthony Ferritto, Lin Pan, Michael Glass, Vittorio Castelli, J. William Murdock, Radu Florian, Salim Roukos, Avi Sil

KCAT: A Knowledge-Constraint Typing Annotation Tool (P19-3)

Challenge:	Using a new orchestration framework, we build, test, and deploy interactive NLP and IR systems to production environments.
Approach:	They introduce a new orchestration framework for building, experimenting with, and deploying interactive NLP and IR systems to production environments.
Outcome:	The proposed framework is well suited to a variety of use cases but is not suitable for academic benchmarking or industry specific use cases.

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Sheng Lin, Luye Zheng, Bo Chen, Siliang Tang, Zhigang Chen, Guoping Hu, Yueting Zhuang, Fei Wu, Xiang Ren

Named Entity Recognition

Challenge:	Recent years Natural Language Processing community has seen a surge of interest in fine-grained entity typing (FET) given an entity mention (i.e. a sequence of token spans representing an entity), FET aims at uncovering its contextdependent type.
Approach:	They propose an efficient Knowledge Constraint Fine-grained Entity Typing Annotation Tool which further improves the entity typing process through entity linking together with some practical functions.
Outcome:	The proposed tool improves the entity typing process by linking the candidate types with some practical functions.

WebNovelBench: Placing LLM Novelists on the Web Novel Distribution (2026.findings-eacl)

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Liangtao Lin, Jun Zheng, Haidong Wang

CTCC: A Robust and Stealthy Fingerprinting Framework for Large Language Models via Cross-Turn Contextual Correlation Backdoor (2025.emnlp-main)

Challenge:	Existing benchmarks for long-form novel generation lack scale, diversity, or objective measures.
Approach:	They propose a framework that assesses long-form novel generation using an LLM-as-Judge approach.
Outcome:	The proposed framework differentiates between human-written masterpieces, popular web novels, and LLM-generated content.

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Zhenhua Xu, Xixiang Zhao, Xubin Yue, Shengwei Tian, Changting Lin, Meng Han

FLatS: Principled Out-of-Distribution Detection with Feature-Based Likelihood Ratio Score (2023.emnlp-main)

Challenge:	Existing methods for fingerprinting model ownership traces are vulnerable to illegal plagiarism and are not reliable.
Approach:	They propose a rule-driven fingerprinting framework that encodes contextual correlations across multiple dialogue turns.
Outcome:	The proposed framework achieves stronger stealth and robustness than previous work.

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Haowei Lin, Yuntian Gu

UltraEval-Audio: A Unified Framework for Comprehensive Evaluation of Audio Foundation Models (2026.acl-demo)

Challenge:	Existing methods for detecting out-of-distribution instances are empirical . state-of the-art methods for OOD detection are suboptimal since they only estimate in-distance density pout(x).
Approach:	They propose a method that measures the “OOD-ness” of a test case x through the likelihood ratio between out-distribution mathcal Pout and in-division mathcal Pin.
Outcome:	The proposed method improves existing methods on popular benchmarks and establishes a new SOTA on popular NLP benchmarks.

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Qundong Shi, Jie Zhou, Biyuan Lin, Junbo Cui, Guoyang Zeng, Yixuan Zhou, Ziyang Wang, Xin Liu, Zhen Luo, Yudong Wang, Zhiyuan Liu

DUCK: Rumour Detection on Social Media by Modelling User and Comment Propagation Networks (2022.naacl-main)

Challenge:	Existing evaluation frameworks for audio foundation models are heavily reliant on English, making it difficult to objectively assess models’ performance on Chinese.
Approach:	They propose a unified framework that supports 10 languages, 14 task categories, 24 models, and 36 benchmarks with one-command evaluation and real-time leaderboards.
Outcome:	The proposed framework supports 10 languages, 14 task categories, 24 models, and 36 benchmarks with one-command evaluation and real-time leaderboards.

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Lin Tian, Xiuzhen Zhang, Jey Han Lau

Challenge:	Social media rumours can cause significant economic and social disruption.
Approach:	They propose a rumour detection algorithm that leverages transformers and graph attention networks to jointly model social media conversations and the network of users who engaged in them.
Outcome:	The proposed algorithm produces superior performance over four widely used benchmark rumour datasets in English and Chinese.

MTVQA: Benchmarking Multilingual Text-Centric Visual Question Answering (2025.findings-acl)

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Jingqun Tang, Qi Liu, Yongjie Ye, Jinghui Lu, Shu Wei, An-Lan Wang, Chunhui Lin, Hao Feng, Zhen Zhao, Yanjie Wang, Yuliang Liu, Hao Liu, Xiang Bai, Can Huang

Temporal Precision Matters: Brain-Tuning Speech Language Models with Millisecond-Resolution Neural Signals (2026.acl-long)

Challenge:	Text-Centric Visual Question Answering (TEC-VQA) is a text-centric visual task understanding tool.
Approach:	They introduce a benchmark that features human expert annotations across 9 languages . they prioritize the text in question-answer pairs while disregarding visual text in images .
Outcome:	The proposed benchmarks prioritize the text in question-answer pairs while disregarding visual text in images.

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Zhejun Zhang, Wenqing Zhou, Haozhe Xu, Lin Zhang, Lei Li

Transition-based Directed Graph Construction for Emotion-Cause Pair Extraction (2020.acl-main)

Challenge:	Language models have emerged as powerful tools for predicting human brain activity during language comprehension.
Approach:	They propose a technique that leverages electrocorticography’s millisecond precision to train speech language models.
Outcome:	The proposed technique improves brain alignment over pretrained and distillation models and produces higher gains in higher-order language regions.

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Chuang Fan, Chaofa Yuan, Jiachen Du, Lin Gui, Min Yang, Ruifeng Xu

MoEL: Mixture of Empathetic Listeners (D19-1)

Challenge:	Existing methods to extract emotions and causes from unannotated text are pipelined, causing error propagation.
Approach:	They propose to transform a task into a procedure of parsing-like directed graph construction . they propose to generate a directed graph with labeled edges based on a sequence of actions .
Outcome:	The proposed method outperforms the state-of-the-art methods by 6.71% (p0.01) in F1 measure.

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Zhaojiang Lin, Andrea Madotto, Jamin Shin, Peng Xu, Pascale Fung

ChildMandarin: A Comprehensive Mandarin Speech Dataset for Young Children Aged 3-5 (2025.acl-long)

Challenge:	Neural network approaches for conversation models have shown to be successful in generating fluent and relevant responses.
Approach:	They propose a novel end-to-end approach for modeling empathy in dialogue systems by using Mixture of Empathetic Listeners (MoEL).
Outcome:	The proposed model outperforms multitask training baseline in terms of empathy, relevance, and fluency.

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Jiaming Zhou, Shiyao Wang, Shiwan Zhao, Jiabei He, Haoqin Sun, Hui Wang, Cheng Liu, Aobo Kong, Yujie Guo, Xi Yang, Yequan Wang, Yonghua Lin, Yong Qin

Structure-Infused Copy Mechanisms for Abstractive Summarization (C18-1)

Challenge:	Automatic speech recognition systems have advanced significantly with models like Whisper, Conformer, and self-supervised frameworks such as Wav2vec 2.0.
Approach:	They propose to use Mandarin speech datasets to analyze pronunciation and tone of children aged 3 to 5 and evaluate their models on speaker verification (SV) They find that the datasets are more robust than those used by adult speech recognition systems and are open-source and available for all academic purposes.
Outcome:	The proposed dataset includes 41.25 hours of speech with carefully crafted manual transcriptions, collected from 397 speakers across various provinces in China, with balanced gender representation.

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Kaiqiang Song, Lin Zhao, Fei Liu

Semi-Supervised Learning for Video Captioning (2020.findings-emnlp)

Challenge:	Experimental results show that system summaries struggle to preserve syntactic meaning of source texts.
Approach:	They propose to incorporate syntactic information from source sentences into abstractive summaries by structure-infused copy mechanisms.
Outcome:	The proposed approach compares favorably to state-of-the-art methods.

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Ke Lin, Zhuoxin Gan, Liwei Wang

TACLR: A Scalable and Efficient Retrieval-based Method for Industrial Product Attribute Value Identification (2025.acl-long)

Challenge:	Existing video captioning algorithms are heavily dependent on supervised training data.
Approach:	They propose to train the video captioning model on labeled and unlabeled data jointly in a semi-supervised learning manner.
Outcome:	The proposed model outperforms state-of-the-art semi-supervised learning approaches on VATEX, MSR-VTT and MSVD datasets.

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Yindu Su, Huike Zou, Lin Sun, Ting Zhang, Haiyang Yang, Chen Li Yu, David Lo, Qingheng Zhang, Shuguang Han, Jufeng Chen

Low-Hallucination and Efficient Coreference Resolution with LLMs (2025.findings-emnlp)

Challenge:	Existing methods for product attribute value identification face critical challenges . seller-provided attribute values are often incomplete or inaccurate .
Approach:	They propose a retrieval-based method that uses taxonomy-aware contrastive learning . they use product profiles and candidate values to encode and retrieve attributes based on similarity .
Outcome:	The proposed method is based on a taxonomy-aware, hard negative sampling and adaptive inference with dynamic thresholds.

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Yujian Gan, Yuan Liang, Jinxia Xie, Yanni Lin, Juntao Yu, Massimo Poesio

2INER: Instructive and In-Context Learning on Few-Shot Named Entity Recognition (2023.findings-emnlp)

Challenge:	Large Language Models have shown promising results in coreference resolution, but they face a critical issue: hallucinations.
Approach:	They propose a low-hallucination and efficient solution to the problem of hallucinations . they propose efficient constrained decoding for coreference resolution .
Outcome:	The proposed approach achieves better performance on the English OntoNotes development set.

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Jiasheng Zhang, Xikai Liu, Xinyi Lai, Yan Gao, Shusen Wang, Yao Hu, Yiqing Lin

Selecting Demonstrations for Many-Shot In-Context Learning via Gradient Matching (2025.findings-acl)

Challenge:	Named Entity Recognition (NER) tasks are a fundamental task of natural language processing (NLP).
Approach:	They propose a text-to-text framework for Few-Shot Named Entity Recognition (NER) that employs instruction finetuning and auxiliary tasks to enhance the model's understanding of entity types in the overall semantic context of a sentence.
Outcome:	The proposed framework outperforms existing Few-Shot NER methods and remains competitive with state-of-the-art NER algorithms.

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Jianfei Zhang, Bei Li, Jun Bai, Rumei Li, Yanmeng Wang, Chenghua Lin, Wenge Rong

LLM Reasoning as Trajectories: Step-Specific Representation Geometry and Correctness Signals (2026.acl-long)

Challenge:	In-Context Learning (ICL) empowers Large Language Models for rapid task adaptation without fine-tuning.
Approach:	They propose a method that aligns fine-tuning gradients between entire training set and selected examples to enable in-context learning and fine-uning.
Outcome:	The proposed method outperforms random selection on large LLMs from 4-shot to 128-shot scenarios across 9 datasets.

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Lihao Sun, Hang Dong, Bo Qiao, Qingwei Lin, Dongmei Zhang, Saravan Rajmohan

VideoMind: Thinking in Steps for Long Video Understanding (2026.eacl-industry)

Challenge:	Existing models generate tokens by updating high-dimensional representations and decoding from them at each timestep.
Approach:	They propose a framework that allows reasoning correction and length control based on derived ideal trajectories.
Outcome:	The proposed model can predict correctness and length control based on ideal trajectories.

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Shubhang Bhatnagar, Renxiong Wang, Kapil Krishnakumar, Adel Ahmadyan, Zhaojiang Lin, Lambert Mathias, Xin Luna Dong, Babak Damavandi, Narendra Ahuja, Seungwhan Moon

KPatch: Knowledge Patch to Pre-trained Language Model for Zero-Shot Stance Detection on Social Media (2024.lrec-main)

Challenge:	Multimodal Large Language Models struggle with Long Video Understanding due to their limited context window and the distributed nature of salient information across many redundant frames.
Approach:	They propose a training framework that mimics a human reasoning process to train Long Video Understanding models.
Outcome:	The proposed framework achieves 77.6% performance on Video MME, LongVideo, and MLVU benchmarks while yielding 5% improvement on Llama 4 Scout.

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Shuohao Lin, Wei Chen, Yunpeng Gao, Zhishu Jiang, Mengqi Liao, Zhiyu Zhang, Shuyuan Zhao, Huaiyu Wan

Selective “Selective Prediction”: Reducing Unnecessary Abstention in Vision-Language Reasoning (2024.findings-acl)

Challenge:	Existing knowledge injection methods fail to understand the semantics of tweets .
Approach:	They propose a method to flexibly inject knowledge into a pre-trained language model and adaptively expand tweets context.
Outcome:	The proposed method is based on two training stages to flexibly inject knowledge into the pre-trained language model and adaptively expand tweets context.

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Tejas Srinivasan, Jack Hessel, Tanmay Gupta, Bill Yuchen Lin, Yejin Choi, Jesse Thomason, Khyathi Chandu

CoMoL: Efficient Mixture of LoRA Experts via Dynamic Core Space Merging (2026.findings-acl)

Challenge:	ReCoVERR reduces the over-abstention of a vision-language system with low tolerance for inaccurate predictions without increasing the error rate of the system’s predictions.
Approach:	They propose an inference-time algorithm to reduce the over-abstention of a selective vision-language system without increasing the error rate of the system’s predictions.
Outcome:	ReCoVERR reduces the over-abstention of a vision-language system without increasing the error rate of the system’s predictions.

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Jie Cao, Zhenxuan Fan, Zhuonan Wang, Tianwei Lin, Ziyuan Zhao, Rolan Yan, Wenqiao Zhang, Feifei Shao, Hongwei Wang, Jun Xiao, Siliang Tang

Align-SLM: Textless Spoken Language Models with Reinforcement Learning from AI Feedback (2025.acl-long)

Challenge:	Existing PEFT methods suffer from limited parameter efficiency and coarse-grained adaptation due to proliferation of LoRA experts and instance-level routing.
Approach:	They propose a new MoE-LoRA framework that incorporates expert diversity, parameter efficiency, and fine-grained adaptation.
Outcome:	The proposed framework outperforms existing methods on multiple tasks while maintaining parameter efficiency.

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Guan-Ting Lin, Prashanth Gurunath Shivakumar, Aditya Gourav, Yile Gu, Ankur Gandhe, Hung-yi Lee, Ivan Bulyko

Call Me When Necessary: LLMs can Efficiently and Faithfully Reason over Structured Environments (2024.findings-acl)

Challenge:	Textless Spoken Language Models lag behind text-based Large Language Model (LLM) in semantic coherence and relevance.
Approach:	They propose a framework that leverages preference optimization inspired by Reinforcement Learning with Human Feedback to enhance the semantic understanding of SLMs.
Outcome:	The proposed framework achieves state-of-the-art performance of SLMs for most benchmarks . it leverages preference optimization inspired by Reinforcement Learning with Human Feedback .

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Sitao Cheng, Ziyuan Zhuang, Yong Xu, Fangkai Yang, Chaoyun Zhang, Xiaoting Qin, Xiang Huang, Ling Chen, Qingwei Lin, Dongmei Zhang, Saravan Rajmohan, Qi Zhang

VISA: Retrieval Augmented Generation with Visual Source Attribution (2025.acl-long)

Challenge:	Large Language Models (LLMs) have shown potential in reasoning over structured environments, e.g., knowledge graphs and tables.
Approach:	They propose a framework that allows LLMs to efficiently and faithfully reason over structured environments.
Outcome:	The proposed framework surpasses state-of-the-art fine-tuned methods on three KGQA and two TableQA datasets and surpasse CWQ and WTQ methods.

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Xueguang Ma, Shengyao Zhuang, Bevan Koopman, Guido Zuccon, Wenhu Chen, Jimmy Lin

Can Large Language Models Always Solve Easy Problems if They Can Solve Harder Ones? (2024.emnlp-main)

Challenge:	Existing approaches to retrieval-augmented generation primarily link generated content to document-level references, making it difficult for users to locate evidence among multiple content-rich retrieved documents.
Approach:	They propose a novel approach that combines answer generation with visual source attribution by leveraging large vision-language models to identify evidence and highlight exact regions that support the generated answers with bounding boxes in the retrieved document screenshots.
Outcome:	The proposed approach identifies evidence and highlights exact regions that support the generated answers with bounding boxes in the retrieved document screenshots.

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Zhe Yang, Yichang Zhang, Tianyu Liu, Jian Yang, Junyang Lin, Chang Zhou, Zhifang Sui

An Auto-Encoder Matching Model for Learning Utterance-Level Semantic Dependency in Dialogue Generation (D18-1)

Challenge:	Large language models (LLMs) have impressive capabilities, but still suffer from inconsistency issues.
Approach:	They develop a ConsisEval benchmark to evaluate LLMs' inconsistency . they find that LLM models can paradoxically fail at easier problems .
Outcome:	The proposed model achieves highest consistency score but inconsistent to specific questions due to distraction by redundant information, misinterpretation of questions, etc.

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Liangchen Luo, Jingjing Xu, Junyang Lin, Qi Zeng, Xu Sun

Recurrent Alignment with Hard Attention for Hierarchical Text Rating (2024.emnlp-main)

Challenge:	Experimental results show that our model can generate semantically coherent responses compared to baseline models.
Approach:	They propose an Auto-Encoder Matching model to learn utterance-level semantic dependency . their model contains two auto-encoders and one mapping module .
Outcome:	Experimental results show that the proposed model can generate high coherence and fluency compared to baseline models.

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Chenxi Lin, Ren Jiayu, Guoxiu He, Zhuoren Jiang, Haiyan Yu, Xiaomin Zhu

Navigating the Infinite Dynamic Web Space: Effective In-Context Exploration via Cognitive Multi-Agent Collaboration (2026.eacl-long)

Challenge:	Large language models excel at understanding and generating plain text, but they are not tailored to handle hierarchical text structures or directly predict task-specific properties such as text rating.
Approach:	They propose a framework that integrates Recurrent Alignment with Hard Attention to analyze hierarchically structured text.
Outcome:	The proposed framework outperforms existing state-of-the-art methods on three hierarchical text rating datasets.

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Guozhao Mo, Yanjiang Liu, Yafei Shi, Jiawei Chen, Yang Li, Yaojie Lu, Hongyu Lin, Ben He, Le Sun, Bo Zheng, Xianpei Han

Linguistics & Cognitive NLP

Challenge:	Existing methods for dynamic web navigation rely on greedy strategies or value estimation, struggle to achieve effective backtracking and are heavily dependent on proprietary models.
Approach:	They propose a cognitive multi-agent collaboration framework that enhances cyberspace exploration capability through In-Context Exploration.
Outcome:	The proposed framework surpasses the proprietary model Claude-3.5 Sonnet on the WebArena benchmark.

Instance-level Randomization: Toward More Stable LLM Evaluations (2025.findings-emnlp)

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Yiyang Li, Yonghuang Wu, Ying Luo, Liangtai Sun, Zishu Qin, Lin Qiu, Xuezhi Cao, Xunliang Cai

FACT-AUDIT: An Adaptive Multi-Agent Framework for Dynamic Fact-Checking Evaluation of Large Language Models (2025.acl-long)

Challenge:	Evaluations of large language models suffer from instability, where small changes of random factors can lead to drastic fluctuations of scores and even model rankings.
Approach:	They propose an instance-level randomization method to reduce variance and improve fairness in evaluations by randomizing all factors that affect evaluation scores for every single instance.
Outcome:	The proposed method reduces variance and improves fairness in model comparisons by using instance-level randomization.

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Hongzhan Lin, Yang Deng, Yuxuan Gu, Wenxuan Zhang, Jing Ma, See-Kiong Ng, Tat-Seng Chua

Large Language Models Fall Short: Understanding Complex Relationships in Detective Narratives (2024.findings-acl)

Challenge:	Existing fact-checking evaluation methods rely on static datasets and classification metrics, which fail to evaluate justification production and uncover the nuanced limitations of LLMs.
Approach:	They propose a framework that adaptively and dynamically assesses LLMs’ fact-checking capabilities by incorporating justification production alongside verdict prediction.
Outcome:	Experiments show that the framework differentiates among state-of-the-art LLMs, providing valuable insights into model strengths and limitations in model-centric fact-checking analysis.

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Runcong Zhao, Qinglin Zhu, Hainiu Xu, Jiazheng Li, Yuxiang Zhou, Yulan He, Lin Gui

PEMV: Improving Spatial Distribution for Emotion Recognition in Conversations Using Proximal Emotion Mean Vectors (2025.findings-naacl)

Challenge:	Existing datasets for narrative understanding fail to represent complexity and uncertainty of relationships in real-life social scenarios.
Approach:	They propose a benchmark for extracting and analysing intricate character relation graphs from detective narratives using large-scale large-language models.
Outcome:	The proposed dataset extracts and analyses character relation graphs from detective narratives using advanced Large Language Models like GPT-3.5, GPT-4, and Llama2 .

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Chen Lin, Fei Li, Donghong Ji, Chong Teng

s3: You Don’t Need That Much Data to Train a Search Agent via RL (2025.emnlp-main)

Challenge:	Existing research focuses on the analysis of contextual structure in dialogue and the interactions between different emotions.
Approach:	They propose a method that generates Proximal Emotion Mean Vectors (PEMVs) based on emotion feature queues to optimize the spatial representation of text features.
Outcome:	The proposed method achieves state-of-the-art performance on three widely used benchmark datasets.

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Pengcheng Jiang, Xueqiang Xu, Jiacheng Lin, Jinfeng Xiao, Zifeng Wang, Jimeng Sun, Jiawei Han

Code Needs Comments: Enhancing Code LLMs with Comment Augmentation (2024.findings-acl)

Challenge:	Existing approaches to optimize retrieval using search-only metrics ignore downstream utility and fine-tune entire LLM to jointly reason and retrieve limit retrieval utility and compatibility with frozen or proprietary models.
Approach:	They propose a lightweight, model-agnostic framework that decouples the searcher from the generator and trains the search user using a Gain Beyond RAG reward.
Outcome:	The proposed framework outperforms baselines trained on over 70 more data with 2.4k training samples.

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Demin Song, Honglin Guo, Yunhua Zhou, Shuhao Xing, Yudong Wang, Zifan Song, Wenwei Zhang, Qipeng Guo, Hang Yan, Xipeng Qiu, Dahua Lin

Why Multi-Interest Fairness Matters: Hypergraph Contrastive Multi-Interest Learning for Fair Conversational Recommender System (2025.findings-acl)

Challenge:	Large Language Models (LLMs) require a deep understanding of programming languages and their correlation with natural languages (NLs).
Approach:	They propose a data augmentation method that generates comments for existing code and a filtering strategy that filters out code data poorly correlated with natural language.
Outcome:	The proposed method outperforms the model trained on the augmented data and the model further trained on data without augmentation on two widely-used programming skill benchmarks.

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Yongsen Zheng, Zongxuan Xie, Guohua Wang, Ziyao Liu, Liang Lin, Kwok-Yan Lam

LLM Critics Help Catch Bugs in Mathematics: Towards a Better Mathematical Verifier with Natural Language Feedback (2025.findings-acl)

Challenge:	Unfairness is a well-known challenge in Recommender Systems (RSs) some approaches have started to improve fairness in offline or static contexts, but it often exacerbates over time, leading to significant problems like the Matthew effect, filter bubbles, and echo chambers.
Approach:	They propose a framework to promote multi-interest diversity fairness in RSs by establishing diverse hypergraphs through contrastive learning.
Outcome:	The proposed framework achieves state-of-the-art performance while effectively alleviating unfairness in two CRS-based datasets.

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Bofei Gao, Zefan Cai, Runxin Xu, Peiyi Wang, Ce Zheng, Runji Lin, Keming Lu, Dayiheng Liu, Chang Zhou, Wen Xiao, Tianyu Liu, Baobao Chang

Reasoning Like Program Executors (2022.emnlp-main)

Challenge:	Existing mathematical verifiers are trained with binary classification labels, which are not informative enough for the model to accurately assess the solutions.
Approach:	They propose a natural language feedback-enhanced verifier that can validate the correctness of response generated by policy models by constructing automatically generated training data and a two-stage training paradigm.
Outcome:	The proposed verifier significantly improves in verification and reinforcement learning and alleviates data-demanding problems of the reward model.

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Xinyu Pi, Qian Liu, Bei Chen, Morteza Ziyadi, Zeqi Lin, Qiang Fu, Yan Gao, Jian-Guang Lou, Weizhu Chen

RIGOURATE: Quantifying Scientific Exaggeration with Evidence-Aligned Claim Evaluation (2026.findings-acl)

Challenge:	Existing language models are inadequate in reasoning, according to studies . a new reasoning pre-training paradigm is based on pretraining language models with programs .
Approach:	They propose a reasoning pre-training paradigm that empowers language models to harvest reasoning knowledge possessed by program executors.
Outcome:	The proposed reasoning pre-training paradigm can boost models' reasoning skills . it can be instantiated by different kinds of program executors and run on a single database .

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Joseph James, Chenghao Xiao, Yucheng Li, Nafise Sadat Moosavi, Chenghua Lin

Can LLMs Learn From Mistakes? An Empirical Study on Reasoning Tasks (2024.findings-emnlp)

Challenge:	Scientific rigour tends to be sidelined in favour of bold statements, leading authors to overstate claims beyond what their results support.
Approach:	They propose a multimodal framework that retrieves supporting evidence from a paper and assigns each claim an overstatement score.
Outcome:	The proposed framework retrieves supporting evidence from ICLR and NeurIPS papers and assigns each claim an overstatement score.

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Shengnan An, Zexiong Ma, Siqi Cai, Zeqi Lin, Nanning Zheng, Jian-Guang Lou, Weizhu Chen

Pruning Large Language Models to Intra-module Low-rank Architecture with Transitional Activations (2024.findings-acl)

Challenge:	Existing work has shown that simple learning can enhance the chain-of-thought (CoT) reasoning of large language models.
Approach:	They construct mistake-correction datasets to identify and correct mistakes in CoTs . they conclude that LLMs can learn from mistakes to enhance their CoT reasoning .
Outcome:	The proposed datasets show that LLMs can learn from mistakes to enhance their CoT reasoning performance.

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Bowen Shen, Zheng Lin, Daren Zha, Wei Liu, Jian Luan, Bin Wang, Weiping Wang

Parameter Importance is Not Static: Evolving Parameter Isolation for Supervised Fine-Tuning (2026.acl-long)

Challenge:	Structured pruning is a feasible solution for end-side LLM deployment . however, achieving a high compression ratio for scaled-up LLMs remains a challenge .
Approach:	They propose a task-agnostic structured pruning approach coupled with a compact Transformer architecture to prune LLMs into an intra-module low-rank architecture.
Outcome:	The proposed approach reduces transitional activations inside multi-head attention (MHA) and multi-layer perceptron (MLP) modules while preserving inter-module activations sensitive to perturbations.

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Zekai Lin, Chao Xue, Di Liang, Xingsheng Han, Peiyang Liu, Xianjie Wu, Lei Jiang, Yu Lu, Bob Simons, Shuang Liang, Minlong Peng

A Recipe for Creating Multimodal Aligned Datasets for Sequential Tasks (2020.acl-main)

Challenge:	Recent approaches to fine-tuning of large language models suffer from task interference and catastrophic forgetting.
Approach:	They propose a fine-tuning framework that adapts isolation decisions based on online estimates of parameter importance.
Outcome:	The proposed framework reduces interference and forgetting while releasing outdated parameters to recover plasticity.

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Angela Lin, Sudha Rao, Asli Celikyilmaz, Elnaz Nouri, Chris Brockett, Debadeepta Dey, Bill Dolan

Counterfactual Augmentation for Multimodal Learning Under Presentation Bias (2023.findings-emnlp)

Challenge:	a web-based algorithm can be used to align instructions for different tasks . video instructions can be noisy and contain far more information than textual instructions.
Approach:	They propose an algorithm that learns pairwise alignments between different recipes . they then use a graph algorithm to derive a joint alignment between multiple video and text recipes based on the same recipe.
Outcome:	The proposed algorithm learns pairwise alignments between different recipes for the same dish.

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Victoria Lin, Louis-Philippe Morency, Dimitrios Dimitriadis, Srinagesh Sharma

PsyDT: Using LLMs to Construct the Digital Twin of Psychological Counselor with Personalized Counseling Style for Psychological Counseling (2025.acl-long)

Challenge:	In real-world machine learning systems, labels are often derived from user behaviors that the system wishes to encourage.
Approach:	They propose a method for correcting presentation bias using generated counterfactual labels by augmentation of the labels by the user.
Outcome:	The proposed method improves performance in an oracle setting compared to uncorrected models and existing bias-correction methods.

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Haojie Xie, Yirong Chen, Xiaofen Xing, Jingkai Lin, Xiangmin Xu

Memorizing is Not Enough: Deep Knowledge Injection Through Reasoning (2025.acl-long)

Challenge:	Existing mental health LLMs do not consider the fact that different psychological counselors exhibit different personal styles.
Approach:	They propose a framework that uses LLMs to construct the digital twin of psychological counselor with personalized counseling style.
Outcome:	The proposed framework can synthesize multi-turn dialogues that closely resemble real-world counseling cases and demonstrate better performance compared to baselines.

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Ruoxi Xu, Yunjie Ji, Boxi Cao, Yaojie Lu, Hongyu Lin, Xianpei Han, Ben He, Yingfei Sun, Xiangang Li, Le Sun

Character-level White-Box Adversarial Attacks against Transformers via Attachable Subwords Substitution (2022.emnlp-main)

Challenge:	Existing knowledge injection frameworks focus on knowledge memorization and retrieval, but static nature of large language models leads to outdated information as the real world evolves or when adapting to domain-specific knowledge.
Approach:	They propose a four-tier knowledge injection framework that defines the levels of knowledge injection: memorization, retrieval, reasoning, and association.
Outcome:	The proposed framework defines the levels of knowledge injection: memorization, retrieval, reasoning, and association.

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Aiwei Liu, Honghai Yu, Xuming Hu, Shu’ang Li, Li Lin, Fukun Ma, Yawen Yang, Lijie Wen

Decomposing Complex Queries for Tip-of-the-tongue Retrieval (2023.findings-emnlp)

Challenge:	Existing methods to attack transformer models are not effective at character level, but they are a natural attack scenario.
Approach:	They propose a character-level adversarial attack method against transformer models . they use a gradient-based method to find the most vulnerable words in a sentence .
Outcome:	The proposed method outperforms previous methods on sentence-level and token-level tasks.

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Kevin Lin, Kyle Lo, Joseph Gonzalez, Dan Klein

A Rigorous Study on Named Entity Recognition: Can Fine-tuning Pretrained Model Lead to the Promised Land? (2020.emnlp-main)

Challenge:	Tip-of-the-tongue retrieval is a retrieval setting in which a user is unable to formulate a precise query that identifies a sought item . a framework that decomposes complex queries into subqueries can improve gold book recall .
Approach:	They propose a framework for handling tip-of-the-tongue queries by decomposing queries into individual clues routing them to specialized retrievers.
Outcome:	The proposed framework improves gold book recall up to 6% on a new query-book pair . it takes advantage of off-the-shelf retrievers or incorporates retriever-specific logic .

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Hongyu Lin, Yaojie Lu, Jialong Tang, Xianpei Han, Le Sun, Zhicheng Wei, Nicholas Jing Yuan

Learning Robust Representations for Continual Relation Extraction via Adversarial Class Augmentation (2022.emnlp-main)

Challenge:	Named entity recognition (NER) is a fundamental task of information extraction.
Approach:	They propose to perform randomization tests on standard NER benchmarks to examine name regularity, mention coverage and context diversity.
Outcome:	The proposed model performs better on standard NER benchmarks than other models on open datasets.

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Peiyi Wang, Yifan Song, Tianyu Liu, Binghuai Lin, Yunbo Cao, Sujian Li, Zhifang Sui

AgentCPM-GUI: Building Mobile-Use Agents with Reinforcement Fine-Tuning (2025.emnlp-demos)

Challenge:	Existing studies attribute catastrophic forgetting to the corruption of the learned representations as new relations come . Continual relation extraction models suffer from catastrophic forgetting when learning new relations .
Approach:	They propose to use adversarial class augmentation mechanism to learn more precise representations . they propose to train the model on a sequence of tasks where two new relations are discovered .
Outcome:	The proposed model improves on two popular benchmarks.

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Zhong Zhang, Yaxi Lu, Yikun Fu, Yupeng Huo, Shenzhi Yang, Yesai Wu, Han Si, Xin Cong, Haotian Chen, Yankai Lin, Xie Xie, Wei Zhou, Wang Xu, Zhou Su, Zhongwu Zhai, Xiaoming Liu, null Meiyudong, Jianming Xu, Hongyan Tian, Chongyi Wang, Chi Chen, Yuan Yao, Zhiyuan Liu, Maosong Sun

Boosting Scientific Concepts Understanding: Can Analogy from Teacher Models Empower Student Models? (2024.emnlp-main)

Challenge:	Large language model agents have enabled GUI-based automation, but their deployment is limited by noisy data, poor generalization, and lack of support for non-English GUIs.
Approach:	They propose an 8B-parameter GUI agent built for robust and efficient on-device GUI interaction.
Outcome:	The proposed GUI agent achieves promising performance on five public benchmarks and proposed Chinese benchmark CAGUI.

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Siyu Yuan, Cheng Jiayang, Lin Qiu, Deqing Yang

DeepMed: Building a Medical DeepResearch Agent via Multi-hop Med-Search Data and Turn-Controlled Agentic Training & Inference (2026.findings-acl)

Challenge:	Analogical reasoning plays a critical role in human cognition, enabling us to understand new concepts by associating them with familiar ones.
Approach:	They propose to use free-form analogies to aid students in understanding scientific concepts . they also show that analogies generated by student LMs can improve their own performance .
Outcome:	The proposed model can help students understand scientific concepts, the authors show .

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Zihan Wang, Hao Wang, Shi Feng, Xiaocui Yang, Daling Wang, Yiqun Zhang, Jinghao Lin, Xiaozhong Ji, Haihua Yang

OctoBench: Benchmarking Scaffold-Aware Instruction Following in Repository-Grounded Agentic Coding (2026.acl-long)

Challenge:	Medical reasoning models are constrained by parametric knowledge and can induce hallucinations and spurious attributions.
Approach:	They propose a model that uses a multi-hop med-search QA synthesis method to apply the DR paradigm in medical contexts.
Outcome:	The proposed model outperforms larger medical reasoning models on medical benchmarks.

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Deming Ding, Shichun Liu, Enhui Yang, Jiahang Lin, Ziying Chen, Shihan Dou, Honglin Guo, Weiyu Cheng, Pengyu Zhao, Chengjun Xiao, Qunhong Zeng, Qi Zhang, Xuanjing Huang, Qidi Xu, Tao Gui

Do LLMs Know Tool Irrelevance? Demystifying Structural Alignment Bias in Tool Invocations (2026.acl-long)

Challenge:	coding scaffolds that follow heterogeneous instructions remain under-examined in software engineering . coding models are capable software agents, but their ability to follow constraints remains under-explored .
Approach:	They introduce OctoBench, which benchmarks scaffold-aware instruction following in agentic coding.
Outcome:	The proposed benchmark aims to accelerate the development of more scaffold-aware agents.

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Yilong Liu, Xixun Lin, Pengfei Cao, Ge Zhang, Fang Fang, Yanan Cao

Challenge:	Large language models (LLMs) have impressive capabilities in utilizing external tools, but in practice, they are often exposed to tools that are irrelevant to the user’s query, in which case the desired behavior is to refrain from invocations.
Approach:	They propose a new dataset that decouples structural alignment from semantic relevance and propose rebalancing strategies that effectively mitigates structural alignment bias.
Outcome:	The proposed approach effectively mitigates structural alignment bias without degrading general tool-use capabilities.

Active Sentence Learning by Adversarial Uncertainty Sampling in Discrete Space (2020.findings-emnlp)

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Dongyu Ru, Jiangtao Feng, Lin Qiu, Hao Zhou, Mingxuan Wang, Weinan Zhang, Yong Yu, Lei Li

T2DR: A Two-Tier Deficiency-Resistant Framework for Incomplete Multimodal Learning (2025.findings-acl)

Challenge:	Existing uncertainty sampling methods are time-consuming and can't be executed frequently.
Approach:	They propose adversarial uncertainty sampling in discrete space to find informative unlabeled text samples for annotation using adversarials.
Outcome:	The proposed approach outperforms baselines on effectiveness on five datasets.

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Han Lin, Xiu Tang, Huan Li, Wenxue Cao, Sai Wu, Chang Yao, Lidan Shou, Gang Chen

Continual Dialogue State Tracking via Example-Guided Question Answering (2023.emnlp-main)

Challenge:	Existing incomplete multimodal learning frameworks are inadequate for integrating multimodal data.
Approach:	They propose a framework for incomplete multimodal learning that is deficiency-resistant and provides two modules to address fine-grained deficiencies.
Outcome:	The proposed framework outperforms the SOTA models on two well-known multimodal benchmarks.

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Hyundong Cho, Andrea Madotto, Zhaojiang Lin, Khyathi Chandu, Satwik Kottur, Jing Xu, Jonathan May, Chinnadhurai Sankar

Found in the Middle: Permutation Self-Consistency Improves Listwise Ranking in Large Language Models (2024.naacl-long)

Challenge:	Dialogue systems are frequently updated to accommodate new services, but naively updating them by continually training with data for new services causes catastrophic forgetting.
Approach:	They propose to reformulate dialogue state tracking (DST) as a bundle of example-guided question answering tasks to minimize the task shift between services.
Outcome:	The proposed model achieves state-of-the-art performance on DST continual learning metrics without relying on any complex regularization or parameter expansion methods.

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Raphael Tang, Crystina Zhang, Xueguang Ma, Jimmy Lin, Ferhan Ture

CTRLEval: An Unsupervised Reference-Free Metric for Evaluating Controlled Text Generation (2022.acl-long)

Challenge:	Large language models exhibit positional bias in how they use context, which affects listwise ranking.
Approach:	They propose a method to marginalize out different list orders in the prompt to produce an order-independent ranking with less positional bias.
Outcome:	The proposed method improves on five datasets in sorting and passage reranking by 34-52% . it marginalizes out different list orders in the prompt to produce an order-independent ranking .

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Pei Ke, Hao Zhou, Yankai Lin, Peng Li, Jie Zhou, Xiaoyan Zhu, Minlie Huang

Less Noise, More Voice: Reinforcement Learning for Reasoning via Instruction Purification (2026.findings-acl)

Challenge:	Existing reference-free metrics have obvious limitations for evaluating controlled text generation models.
Approach:	They propose an unsupervised reference-free metric which evaluates controlled text generation from different aspects by formulating each aspect into multiple text infilling tasks.
Outcome:	The proposed metric has higher correlations with human judgments while obtaining better generalization of evaluating generated texts from different models and with different qualities.

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Yiju Guo, Tianyi Hu, Zexu Sun, Yankai Lin

AgentSense: Benchmarking Social Intelligence of Language Agents through Interactive Scenarios (2025.naacl-long)

Challenge:	Experimental results show that LENS outperforms GRPO in delivering higher performance and faster convergence.
Approach:	They propose a framework that purifies prompts by identifying and removing interference tokens and then transfers successful rollouts to supervise policy optimization on original noisy prompts.
Outcome:	The proposed framework outperforms GRPO in the real-world, with a 3.88% gain and speedup.

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Xinyi Mou, Jingcong Liang, Jiayu Lin, Xinnong Zhang, Xiawei Liu, Shiyue Yang, Rong Ye, Lei Chen, Haoyu Kuang, Xuanjing Huang, Zhongyu Wei

Linguistics & Cognitive NLP

Challenge:	Large language models are increasingly employed to empower autonomous agents to simulate human behavior.
Approach:	They propose to evaluate LLM-driven agents through multi-turn interactions using a bottom-up approach to create diverse social scenarios constructed from extensive scripts.
Outcome:	The proposed model evaluates LLM-driven agents through multi-turn interactions emphasizing goal completion and implicit reasoning.

SParC: Cross-Domain Semantic Parsing in Context (P19-1)

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Tao Yu, Rui Zhang, Michihiro Yasunaga, Yi Chern Tan, Xi Victoria Lin, Suyi Li, Heyang Er, Irene Li, Bo Pang, Tao Chen, Emily Ji, Shreya Dixit, David Proctor, Sungrok Shim, Jonathan Kraft, Vincent Zhang, Caiming Xiong, Richard Socher, Dragomir Radev

Multi-Level Knowledge Distillation for Out-of-Distribution Detection in Text (2023.acl-long)

Challenge:	Xu et al., 2017): a dataset for cross-domain semantic parsing in context with 4,298 question sequences.
Approach:	They present a dataset for cross-domainSemanticParsing inContext that consists of 4,298 coherent question sequences.
Outcome:	The proposed dataset demonstrates that it has greater semantic diversity and can be generalized to unseen domains due to its cross-domain nature and the unseened databases at test time.

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Qianhui Wu, Huiqiang Jiang, Haonan Yin, Börje Karlsson, Chin-Yew Lin

Learning Latent Semantic Annotations for Grounding Natural Language to Structured Data (D18-1)

Challenge:	Self-supervised representation learning has proved to be a valuable component for out-of-distribution (OoD) detection with only the texts of in-difference (ID) examples.
Approach:	They propose a method that integrates strengths and weaknesses of both methods . they use a fine-tuned model as the teacher to teach a randomly initialized student model .
Outcome:	The proposed method outperforms human evaluators in the pair-expert task on the Human ChatGPT Comparison Corpus.

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Guanghui Qin, Jin-Ge Yao, Xuening Wang, Jinpeng Wang, Chin-Yew Lin

Leveraging Grammar Induction for Language Understanding and Generation (2024.findings-emnlp)

Challenge:	Existing work on grounded language learning does not capture the semantics of correspondences between structured world state representations and texts.
Approach:	They propose to learn explicit latent semantic annotations from paired structured tables and texts . they use an adapted semi-hidden Markov model to impose a soft constraint to further improve performance .
Outcome:	The proposed framework improves on a semi-hidden Markov model and extracts templates for language generation.

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Jushi Kai, Shengyuan Hou, Yusheng Huang, Zhouhan Lin

Thinking Before You Speak: A Proactive Test-time Scaling Approach (2025.findings-emnlp)

Challenge:	Existing grammar induction methods do not provide sufficient performance in downstream tasks.
Approach:	They propose an unsupervised grammar induction method for language understanding and generation using a grammar parser and a syntactic mask.
Outcome:	The proposed method performs better on from-scratch and pre-trained scenarios.

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Cong Liu, Wenchang Chai, Hejun Wu, Yan Pan, Pengxu Wei, Liang Lin

Enhancing Zero-shot and Few-shot Stance Detection with Commonsense Knowledge Graph (2021.findings-acl)

Challenge:	Large Language Models often exhibit deficiencies with complex reasoning tasks, such as maths, due to the discrepancy between human reasoning patterns and those presented in training data.
Approach:	They propose to insert insights between consecutive reasoning steps to bridge this gap by generating insights between the next reasoning steps.
Outcome:	Experiments on mathematical datasets confirm the effectiveness of the proposed reasoning framework on complex problems.

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Rui Liu, Zheng Lin, Yutong Tan, Weiping Wang

Few-shot Named Entity Recognition with Self-describing Networks (2022.acl-long)

Challenge:	Existing methods for stance detection are not applicable to zero-shot and few-shot scenarios.
Approach:	They propose a model that integrates commonsense knowledge into a stance detection model.
Outcome:	The proposed model outperforms state-of-the-art methods on zero-shot and few-shot stance detection tasks.

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Jiawei Chen, Qing Liu, Hongyu Lin, Xianpei Han, Le Sun

PToco: Prefix-based Token-level Collaboration Enhances Reasoning for Multi-LLMs (2025.coling-main)

Challenge:	Existing few-shot named entity recognition (NER) models capture information from limited instances while transferring useful knowledge from external resources.
Approach:	They propose a self-describing mechanism for few-shot NER which can universally describe mentions using concepts and automatically map novel entity types to concepts.
Outcome:	The proposed model can universally describe mentions using concepts and automatically map novel entity types to concepts and adaptively recognize entities on-demand.

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Yuang Bian, Yupian Lin, Jingping Liu, Tong Ruan

Continual Training of Language Models for Few-Shot Learning (2022.emnlp-main)

Challenge:	Existing approaches to collaboration between multiple Large Language Models (LLMs) rely on highly capable models with strong self-reflection abilities or are limited to models sharing the same tokenizer.
Approach:	They propose a mechanism that enables collaboration among less capable LLMs independent of tokenizer differences.
Outcome:	The proposed mechanism improves performance over individual models and generalizes well across different quantities and sizes of participating models.

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Zixuan Ke, Haowei Lin, Yijia Shao, Hu Xu, Lei Shu, Bing Liu

Learning to Refine: Self-Refinement of Parallel Reasoning in LLMs (2026.findings-acl)

Challenge:	Recent work on applying large language models (LMs) achieves impressive performance in many NLP applications.
Approach:	They propose to continuously post-train an LM with unlabeled domains to expand its knowledge without forgetting previous skills.
Outcome:	The proposed system improves few-shot end-task learning in these domains.

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Qibin Wang, Pu Zhao, Shaohan Huang, Fangkai Yang, Lu Wang, Furu Wei, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang

An Empirical Study of Instruction-tuning Large Language Models in Chinese (2023.findings-emnlp)

Challenge:	Existing approaches to test-time scaling are limited due to the quality of candidate responses.
Approach:	They propose a new metric to quantify the relative improvement of self-refinement beyond majority voting.
Outcome:	The proposed method achieves state-of-the-art performance across five benchmarks over other methods.

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Qingyi Si, Tong Wang, Zheng Lin, Xu Zhang, Yanan Cao, Weiping Wang

Turn Waste into Worth: Rectifying Top-k Router of MoE (2024.emnlp-main)

Challenge:	emergence of ChatGPT validates the potential of large language models (LLMs) in artificial general intelligence (AGI) however, the closed source of LLMs coupled with the requirement for massive computing resources has deterred researchers from reaching the LLM training stage.
Approach:	They propose to use Chinese instruction-tuning LLMs as a cookbook for customizing LLM models that can better respond to Chinese instructions.
Outcome:	The proposed LLM can be used to customize Chinese LLMs that can better respond to Chinese instructions.

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Zhiyuan Zeng, Qipeng Guo, Zhaoye Fei, Zhangyue Yin, Yunhua Zhou, Linyang Li, Tianxiang Sun, Hang Yan, Dahua Lin, Xipeng Qiu

Cross-Lingual Training of Neural Models for Document Ranking (2020.findings-emnlp)

Challenge:	Top-k router suffers from redundancy computation and memory costs due to unbalanced routing . some experts are overflow, where exceeding tokens are dropped, while others are empty, which are padded with zeros, negatively impacting model performance.
Approach:	They propose a top-k router that is unbalanced and uses a multi-gPU system to handle dropped tokens and padding.
Outcome:	The proposed model surpasses the top-1 router by 4.7% in terms of performance . the top-k router suffers from redundancy computation and memory costs .

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Peng Shi, He Bai, Jimmy Lin

FedGUI: Benchmarking Federated GUI Agents across Heterogeneous Platforms, Devices, and Operating Systems (2026.findings-acl)

Challenge:	a recent study shows that multi-lingual BERT models can be used for document ranking in non-English languages . a blog post by Google suggests that the company is exploring this approach to improve web search across a number of languages.
Approach:	They propose to leverage relevance judgments in English to train neural document ranking models for mono-lingual retrieval in multiple target languages.
Outcome:	The proposed approach improves search quality in non-English languages while requiring low resources.

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WenHao Wang, Haoting Shi, Mengying Yuan, Yiquan Lin, Panrong Tong, Hanzhang Zhou, Guangyi Liu, Pengxiang Zhao, Yue Wang, Siheng Chen

Two Birds, One Stone: A Simple, Unified Model for Text Generation from Structured and Unstructured Data (2020.acl-main)

Challenge:	a lack of benchmarks capture real-world, cross-platform heterogeneity in GUI training . traditional methods to train GUI agents rely on centralized data collection and manual labeling .
Approach:	They propose a benchmark for developing and evaluating federated GUI agents across mobile, web and desktop platforms.
Outcome:	The proposed benchmarks show that cross-platform collaboration improves performance and identify platform and OS as the most influential factors.

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Hamidreza Shahidi, Ming Li, Jimmy Lin

ConsistentChat: Building Skeleton-Guided Consistent Multi-Turn Dialogues for Large Language Models from Scratch (2025.emnlp-main)

Challenge:	Recent studies have shown that simpler, properly tuned models are at least competitive across NLP tasks.
Approach:	They propose to use a table-to-text and neural question generation tasks to generate text from structured and unstructured data.
Outcome:	The proposed task generates biographies based on Wikipedia infoboxes . the proposed model can achieve the state of the art in both tasks .

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Jiawei Chen, Xinyan Guan, Qianhao Yuan, Mo Guozhao, Weixiang Zhou, Yaojie Lu, Hongyu Lin, Ben He, Le Sun, Xianpei Han

MCS: An In-battle Commentary System for MOBA Games (2022.coling-1)

Challenge:	Existing instruction data synthesis methods focus on single-turn instructions and neglect cross-turn coherence, resulting in context drift and reduced task completion rates.
Approach:	They propose a framework that constrains multi-turn instruction synthesis by explicitly modeling human conversational intent.
Outcome:	The proposed framework outperforms existing models trained on single-turn and multi-turn instruction datasets.

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Xiaofeng Qi, Chao Li, Zhongping Liang, Jigang Liu, Cheng Zhang, Yuanxin Wei, Lin Yuan, Guang Yang, Lanxiao Huang, Min Li

Challenge:	In-battle commentary is an important component of live streaming of e-sports competitions and is applicable to a wide range of scenarios like combat information analysis and live streaming.
Approach:	They propose a generative system for in-battle real-time commentary in mobile MOBA games and propose 'transform' method to convert match statistics and utterances into consistent encoding space.
Outcome:	The proposed system is based on real-time match statistics and events and can be used for live streaming, e-sports commentary and combat information analysis.

Large Language Models as Zero-shot Dialogue State Tracker through Function Calling (2024.acl-long)

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Zekun Li, Zhiyu Chen, Mike Ross, Patrick Huber, Seungwhan Moon, Zhaojiang Lin, Xin Dong, Adithya Sagar, Xifeng Yan, Paul Crook

Mitigating Hallucinations of Large Language Models in Medical Information Extraction via Contrastive Decoding (2024.findings-emnlp)

Challenge:	Large language models (LLMs) are increasingly prevalent in conversational systems due to their advanced understanding and generative capabilities in general contexts.
Approach:	They propose a method for solving dialogue state tracking (DST) with large language models through function calling.
Outcome:	The proposed approach improves zero-shot DST, allowing adaptation to diverse domains without extensive data collection or model tuning.

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Derong Xu, Ziheng Zhang, Zhihong Zhu, Zhenxi Lin, Qidong Liu, Xian Wu, Tong Xu, Xiangyu Zhao, Yefeng Zheng, Enhong Chen

Jointly Learning Aspect-Focused and Inter-Aspect Relations with Graph Convolutional Networks for Aspect Sentiment Analysis (2020.coling-main)

Challenge:	Medical Information Extraction (MIE) tasks are a fundamental component of medical NLP.
Approach:	They propose an alternative adaptive constraint strategy to adjust the scale and scope of contrastive tokens.
Outcome:	The proposed approach selectively enhances the identification and classification capabilities while minimizing the influence of other inherent abilities in LLMs.

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Bin Liang, Rongdi Yin, Lin Gui, Jiachen Du, Ruifeng Xu

Diagnosing Failures in Large Language Models’ Answers: Integrating Error Attribution into Evaluation Framework (2025.findings-acl)

Challenge:	Existing methods for aspect sentiment analysis do not include explicit sentiment expressions.
Approach:	They propose to construct a heterogeneous graph by leveraging aspect-focused and inter-aspect contextual dependencies for the specific aspect.
Outcome:	The proposed model outperforms state-of-the-art methods on four benchmark datasets and significantly boosts performance in comparison with BERT.

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Zishan Xu, Shuyi Xie, Qingsong Lv, Shupei Xiao, Linlin Song, Sui Wenjuan, Fan Lin

HPT: Hierarchy-aware Prompt Tuning for Hierarchical Text Classification (2022.emnlp-main)

Challenge:	Existing evaluation models lack error attribution capability due to their proprietary nature.
Approach:	They propose a misattribution framework with 6 primary and 15 secondary categories to facilitate in-depth analysis.
Outcome:	The proposed framework is based on a dataset specifically designed for error attribution, along with the corresponding scores and feedback.

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Zihan Wang, Peiyi Wang, Tianyu Liu, Binghuai Lin, Yunbo Cao, Zhifang Sui, Houfeng Wang

LATENTLOGIC: Learning Logic Rules in Latent Space over Knowledge Graphs (2023.findings-emnlp)

Challenge:	Hierarchical text classification (HTC) is a multi-label classification problem with a complex label hierarchy.
Approach:	They propose a Hierarchy-aware Prompt Tuning method to handle HTC from a multi-label perspective using a dynamic virtual template and label words that take the form of soft prompts to fuse the label hierarchy knowledge.
Outcome:	The proposed method achieves state-of-the-art performance on 3 popular HTC datasets and is adept at handling imbalance and low resource situations.

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Junnan Liu, Qianren Mao, Chenghua Lin, Yangqiu Song, Jianxin Li

STK-Adapter: Incorporating Evolving Graph and Event Chain for Temporal Knowledge Graph Extrapolation (2026.acl-long)

Challenge:	Existing methods for learning logic rules for knowledge graph reasoning face limitations such as searching in vast search space and inefficient optimization.
Approach:	They propose a framework to efficiently mine logic rules by controllable generation in the latent space by a pre-trained VAE and a discriminator.
Outcome:	The proposed framework efficiently mines logic rules by controllable generation in the latent space.

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Shuyuan Zhao, Wei Chen, Weijie Zhang, Xinrui Hou, Junfeng Shen, Boyan Shi, Shengnan Guo, Youfang Lin, Huaiyu Wan

Leveraging Generative Large Language Models with Visual Instruction and Demonstration Retrieval for Multimodal Sarcasm Detection (2024.naacl-long)

Challenge:	Temporal Knowledge Graphs (TKGs) store dynamic facts in the real world.
Approach:	They propose a Spatial-Temporal Knowledge Adapter which integrates the evolving graph encoder and the LLM to facilitate TKG reasoning.
Outcome:	The proposed method outperforms state-of-the-art methods on benchmark datasets and exhibits strong generalization capabilities in cross-dataset task.

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Binghao Tang, Boda Lin, Haolong Yan, Si Li

Improving Multi-Criteria Chinese Word Segmentation through Learning Sentence Representation (2023.findings-emnlp)

Challenge:	Existing methods for multimodal sarcasm detection do not fully utilize cross-modal features, limiting their performance on in-domain datasets.
Approach:	They propose a multimodal sarcasm detection model with a designed instruction template and a demonstration retrieval module.
Outcome:	The proposed model outperforms existing methods on in-domain datasets and achieves state-of-the-art performance.

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Chun Lin, Ying-Jia Lin, Chia-Jen Yeh, Yi-Ting Li, Ching Yang, Hung-Yu Kao

Controllable Preference Optimization: Toward Controllable Multi-Objective Alignment (2024.emnlp-main)

Challenge:	Recent Chinese word segmentation models tend to learn the segmentation knowledge through in-vocabulary words rather than understanding the meaning of the entire context.
Approach:	They propose a context-aware approach that incorporates unsupervised sentence representation learning over different dropout masks into the multi-criteria training framework.
Outcome:	The proposed approach achieves state-of-the-art (SoTA) performance on six of the nine CWS benchmark datasets and out-of vocabulary (OOV) recalls for eight of nine.

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Yiju Guo, Ganqu Cui, Lifan Yuan, Ning Ding, Zexu Sun, Bowen Sun, Huimin Chen, Ruobing Xie, Jie Zhou, Yankai Lin, Zhiyuan Liu, Maosong Sun

Challenge:	Existing algorithms for achieving optimal alignment are mostly unidirectional . a recent study suggests that large language models can be ground with evident preferences .
Approach:	They propose to ground large language models with evident preferences . they propose to use controllable preference optimization to specify different objectives .
Outcome:	The proposed models can provide responses that match various preferences among the ”3H” desiderata.

DART: Open-Domain Structured Data Record to Text Generation (2021.naacl-main)

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Linyong Nan, Dragomir Radev, Rui Zhang, Amrit Rau, Abhinand Sivaprasad, Chiachun Hsieh, Xiangru Tang, Aadit Vyas, Neha Verma, Pranav Krishna, Yangxiaokang Liu, Nadia Irwanto, Jessica Pan, Faiaz Rahman, Ahmad Zaidi, Mutethia Mutuma, Yasin Tarabar, Ankit Gupta, Tao Yu, Yi Chern Tan, Xi Victoria Lin, Caiming Xiong, Richard Socher, Nazneen Fatema Rajani

PaD: Program-aided Distillation Can Teach Small Models Reasoning Better than Chain-of-thought Fine-tuning (2024.naacl-long)

Challenge:	Data-to-text annotations can be costly when dealing with tables with nontrivial structures.
Approach:	They propose a procedure for extracting semantic triples from tables that encodes their structures by exploiting table headers and table title.
Outcome:	The proposed method exploits the semantic dependencies between table headers and title to extract semantic triples from tables.

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Xuekai Zhu, Biqing Qi, Kaiyan Zhang, Xinwei Long, Zhouhan Lin, Bowen Zhou

Cacheback: Speculative Decoding With Nothing But Cache (2025.emnlp-main)

Challenge:	Large language models excel in various tasks, but their huge size and inaccessibility of parameters present challenges for practical deployment.
Approach:	They propose to use CoT data to distill task-specific ability from large language models to smaller models . they use reasoning programs to suppress errors in distilled data and improve distillation quality .
Outcome:	The proposed model outperforms LLMs on arithmetic reasoning, symbolic reasoning, and general ability.

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Zhiyao Ma, In Gim, Lin Zhong

Challenge:	a recent study shows that large language models are unable to model locality in language.
Approach:	They propose a training-free and model-agnostic speculative decoding method that exploits locality in language to accelerate Large Language Models.
Outcome:	The proposed method achieves state-of-the-art performance among comparable methods . it leverages only LRU cache tables of token n-grams to generate draft sequences .

Multi-Programming Language Sandbox for LLMs (2025.acl-demo)

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Shihan Dou, Jiazheng Zhang, Jianxiang Zang, Yunbo Tao, Weikang Zhou, Haoxiang Jia, Shichun Liu, Yuming Yang, Shenxi Wu, Zhiheng Xi, Muling Wu, Rui Zheng, Changze Lv, Limao Xiong, Shaoqing Zhang, Lin Zhang, Wenyu Zhan, Rongxiang Weng, Jingang Wang, Xunliang Cai, Yueming Wu, Ming Wen, Yixin Cao, Tao Gui, Xipeng Qiu, Qi Zhang, Xuanjing Huang

Empowering Healthcare Practitioners with Language Models: Structuring Speech Transcripts in Two Real-World Clinical Applications (2025.emnlp-industry)

Challenge:	MPLSandbox is an out-of-the-box multi-programming language sandbox designed to provide unified and comprehensive feedback from compiler and analysis tools for Large Language Models (LLMs).
Approach:	They propose a multi-programming language sandbox that provides unified feedback from compilers and analysis tools for Large Language Models.
Outcome:	The proposed multi-language sandbox can provide comprehensive feedback from compilers and analysis tools for large language models (LLMs).

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Jean-Philippe Corbeil, Asma Ben Abacha, George Michalopoulos, Phillip Swazinna, Miguel Del-Agua, Jerome Tremblay, Akila Jeeson Daniel, Cari Bader, Kevin Cho, Pooja Krishnan, Nathan Bodenstab, Thomas Lin, Wenxuan Teng, Francois Beaulieu, Paul Vozila

Why Do More Experts Fail? A Theoretical Analysis of Model Merging (2026.acl-long)

Challenge:	Large language models (LLMs) have demonstrated strong performance on clinical natural language processing tasks across multiple medical benchmarks.
Approach:	They propose an agentic pipeline for generating realistic, non-sensitive nurse dictations, enabling structured extraction of clinical observations.
Outcome:	The proposed pipeline generates realistic, non-sensitive nurse dictations, enabling structured extraction of clinical observations.

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Zijing Wang, Xingle Xu, YongKang Liu, Yiqun Zhang, Peiqin Lin, Shi Feng, Daling Wang, Xiaocui Yang, Hinrich Schuetze

Challenge:	Existing methods for model merging struggle to maintain performance gains as the number of merged models increases.
Approach:	They propose a Reparameterized Heavy-Tailed method to extend the merged model’s coverage and enhance performance.
Outcome:	The proposed method extends the merged model’s coverage and enhances performance on 19 benchmarks, including knowledge-intensive and general-purpose tasks.

RiddleSense: Reasoning about Riddle Questions Featuring Linguistic Creativity and Commonsense Knowledge (2021.findings-acl)

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Bill Yuchen Lin, Ziyi Wu, Yichi Yang, Dong-Ho Lee, Xiang Ren

VIGIL: Defending LLM Agents Against Tool-Stream Injection via Verify-Before-Commit (2026.acl-long)

Challenge:	a riddle-style commonsense questions require complex commonsensense reasoning and figurative language skills . there is currently no dataset aimed at testing these abilities . authors propose a new multiple-choice question answering task .
Approach:	They propose a new multiple-choice question answering task that uses a large dataset for riddlestyle commonsense questions.
Outcome:	The proposed task comes with the first large dataset for answering riddlestyle commonsense questions.

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Junda Lin, Zhaomeng Zhou, Zhi Zheng, Shuochen Liu, Tong Xu, Yong Chen, Enhong Chen

ELISA-EDL: A Cross-lingual Entity Extraction, Linking and Localization System (N18-5)

Challenge:	Existing defenses for indirect prompt injection are limited by static protection mechanisms . existing models prioritize injected rules due to strict alignment, whereas static protections sever the feedback loop required for adaptive reasoning.
Approach:	They propose a framework that shifts the paradigm from restrictive isolation to a verify-before-commit protocol.
Outcome:	The proposed framework outperforms state-of-the-art dynamic defenses by reducing the attack success rate by over 22% while more thandoubling utility under attack compared to static baselines.

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Boliang Zhang, Ying Lin, Xiaoman Pan, Di Lu, Jonathan May, Kevin Knight, Heng Ji

Compressing Context to Enhance Inference Efficiency of Large Language Models (2023.emnlp-main)

Challenge:	ELISA-EDL is a cross-lingual entity extraction, linking and localization system for Wikipedia languages.
Approach:	They propose a cross-lingual entity extraction, linking and localization system for English speakers . it extracts entities from unstructured text in any of 282 Wikipedia languages and links them to English knowledge bases .
Outcome:	The proposed system extracts entity mentions from Wikipedia and links them to English knowledge bases and visualizes locations related to disaster topics on a world heatmap.

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Yucheng Li, Bo Dong, Frank Guerin, Chenghua Lin

Critic-CoT: Boosting the Reasoning Abilities of Large Language Model via Chain-of-Thought Critic (2025.findings-acl)

Challenge:	Large language models (LLMs) have demonstrated remarkable power and impressive generalisation abilities across various tasks.
Approach:	They propose a method that prunes redundancies in the input context to make the input more compact.
Outcome:	The proposed method reduces memory and inference time while maintaining comparable performance compared to full context.

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Xin Zheng, Jie Lou, Boxi Cao, Xueru Wen, Yuqiu Ji, Hongyu Lin, Yaojie Lu, Xianpei Han, Debing Zhang, Le Sun

Divide, Conquer, and Combine: Mixture of Semantic-Independent Experts for Zero-Shot Dialogue State Tracking (2023.acl-long)

Challenge:	Existing approaches to improve the reasoning performance of large language models rely on intuitive instance-level feedback, which limits the reasoning capabilities.
Approach:	They propose a framework that pushes LLMs toward System-2-like critic capability by using a step-wise CoT reasoning paradigm and automatic construction of weak-supervision data without human annotation.
Outcome:	The proposed model significantly improves task-solving performance by filtering out invalid solutions or iterative refinement.

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Qingyue Wang, Liang Ding, Yanan Cao, Yibing Zhan, Zheng Lin, Shi Wang, Dacheng Tao, Li Guo

JoPA: Explaining Large Language Model’s Generation via Joint Prompt Attribution (2025.acl-long)

Challenge:	Existing methods to enhance the zeroshot generalization of DST fail to effectively decouple semantics of samples, limiting the zero-shot performance of the system.
Approach:	They propose a new learning schema that explicitly disentangles the semantics of seen data and leverages the performance and robustness with the mixture-of-experts mechanism.
Outcome:	The proposed model achieves state-of-the-art on multiWOZ2.1 with 10M trainable parameters and is robust to the mixture-of experts mechanism.

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Yurui Chang, Bochuan Cao, Yujia Wang, Jinghui Chen, Lu Lin

LEAN-LIFE: A Label-Efficient Annotation Framework Towards Learning from Explanation (2020.acl-demos)

Challenge:	Existing attempts to explain the entire language generation often treat input prompt texts independently, ignoring their combinatorial effects on the follow-up generation.
Approach:	They propose a framework for explaining how a few prompt texts collaboratively influences the LLM's complete generation.
Outcome:	The proposed explanations demonstrate faithfulness and efficiency of the proposed framework.

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Dong-Ho Lee, Rahul Khanna, Bill Yuchen Lin, Seyeon Lee, Qinyuan Ye, Elizabeth Boschee, Leonardo Neves, Xiang Ren

LLMs Caught in the Crossfire: Malware Requests and Jailbreak Challenges (2025.acl-long)

Challenge:	Existing frameworks for sequence labeling and classification require massive human effort and labeling data is limited.
Approach:	They propose a web-based, Label-Efficient AnnotatioN framework that allows an annotator to provide the needed labels for a task and can capture explanations for each labeling decision.
Outcome:	The proposed framework surpasses baseline F1 scores by 5-10 percentage points while using 2X times fewer labeled instances.

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Haoyang Li, Huan Gao, Zhiyuan Zhao, Zhiyu Lin, Junyu Gao, Xuelong Li

TexOCR: Advancing Document OCR Models for Compilable Page-to-LaTeX Reconstruction (2026.acl-long)

Challenge:	Large Language Models (LLMs) have a high vulnerability to jailbreak attacks that leverage crafted prompts to generate malicious outputs.
Approach:	They propose to use large language models to test their security against jailbreak attacks that leverage crafted prompts to generate malicious outputs.
Outcome:	The proposed model is based on 320 manually crafted malicious code generation requirements, covering 11 jailbreak methods and 29 code functionality categories.

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Chengye Wang, Lin Fu, Zexi Kuang, Yilun Zhao

AdamMeme: Adaptively Probe the Reasoning Capacity of Multimodal Large Language Models on Harmfulness (2025.acl-long)

Challenge:	Existing document OCR largely targets plain text or Markdown, discarding structural and executable properties that make LaTeX essential for scientific publishing.
Approach:	They propose a benchmark and a training corpus for document reconstruction . they train a 2B-parameter model using supervised fine-tuning and reinforcement learning .
Outcome:	The proposed model improves on existing models using supervised fine-tuning and reinforcement learning with verifiable rewards.

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Zixin Chen, Hongzhan Lin, Kaixin Li, Ziyang Luo, Zhen Ye, Guang Chen, Zhiyong Huang, Jing Ma

NovaCOMET: Open Commonsense Foundation Models with Symbolic Knowledge Distillation (2023.findings-emnlp)

Challenge:	Existing models that assess mLLMs on harmful meme understanding are inaccurate and lack accuracy.
Approach:	They propose a framework that adaptively probes the reasoning capabilities of mLLMs . their framework systematically reveals the varying performance of different target mllms a .
Outcome:	The proposed framework systematically reveals the performance of different target mLLMs.

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Peter West, Ronan Bras, Taylor Sorensen, Bill Lin, Liwei Jiang, Ximing Lu, Khyathi Chandu, Jack Hessel, Ashutosh Baheti, Chandra Bhagavatula, Yejin Choi

A Novel Matching Paradigm: Unified Generative and Discriminative LLM with Prompt Compression for Relevance Learning (2026.acl-industry)

Challenge:	a new commonsense knowledge model, NovaCOMET, combines knowledge and general task models.
Approach:	They propose an open commonsense knowledge model that combines knowledge and general task models.
Outcome:	The proposed model matches or exceeds existing knowledge models on commonsense reasoning tasks.

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Guoliang Zhao, Zixin Cui, Chao Ye, Dengwu He, Fei Huang, Yubo Liu, Shuanglong Li, Tzungren Kuo, Bin Ding, Shuang Zhang, null KunhongZhu, Zhi Guo, Liu Lin

Argument-Based Sentiment Analysis on Forward-Looking Statements (2024.findings-acl)

Challenge:	Existing approaches to matching use Large Language Models as feature extractors, underutilizing their full modeling capabilities.
Approach:	They propose a matching paradigm that integrates two-tower, single-towing, and generative tasks within a unified LLM framework via attention-mask partitioning.
Outcome:	The proposed model achieves superior performance and strong practical value in an industrial search engine.

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Chin-Yi Lin, Chung-Chi Chen, Hen-Hsen Huang, Hsin-Hsi Chen

Coarse-to-fine Few-shot Learning for Named Entity Recognition (2023.findings-acl)

Challenge:	Existing models for argument mining are limited in interpreting future-oriented arguments.
Approach:	They propose a categorization of argument units into claims, premises, and scenarios coupled with a unique sentiment analysis framework.
Outcome:	The proposed framework outperforms existing models in most tasks and is more efficient than existing methods.

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Ruotian Ma, Zhang Lin, Xuanting Chen, Xin Zhou, Junzhe Wang, Tao Gui, Qi Zhang, Xiang Gao, Yun Wen Chen

Cognitive-Level Adaptive Generation via Capability-Aware Retrieval and Style Adaptation (2025.findings-emnlp)

Challenge:	Existing few-shot NER solutions do not consider sub-class discrimination and various granularity of new classes during coarse training.
Approach:	They propose a method that uses a cluster-based prototype loss to learn group-wise discriminative representations of coarse-grained classes and a mixture prototype loss for learning the representations.
Outcome:	The proposed method shows superior performance over baseline methods on in-domain and cross-domain settings with various target granularity.

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Qingsong Wang, Tao Wu, Wang Lin, Yueying Feng, Gongsheng Yuan, Chang Yao, Jingyuan Chen

Enhancing Dialogue Symptom Diagnosis with Global Attention and Symptom Graph (D19-1)

Challenge:	Large Language Models struggle to adapt content to users with differing cognitive capacities, leading to cognitive misalignment.
Approach:	They propose a cognitive-level alignment framework that aligns both knowledge complexity and presentation style with user cognition.
Outcome:	The proposed framework aligns knowledge complexity and presentation style with user cognition.

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Xinzhu Lin, Xiahui He, Qin Chen, Huaixiao Tou, Zhongyu Wei, Ting Chen

Leveraging Social Context for Humor Recognition and Sense of Humor Evaluation in Social Media with a New Chinese Humor Corpus - HumorWB (2024.lrec-main)

Challenge:	Existing studies on symptom diagnosis based on EHRs focus on the standard electronic medical records, but the dialogues between doctors and patients that contain more rich information are not well studied.
Approach:	They propose to build a global attention mechanism to capture more symptom related information and build symptom graphs to model the associations between symptoms rather than treating each symptom independently.
Outcome:	The proposed model achieves the state-of-the-art on the constructed dataset.

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Zeyuan Zeng, Zefeng Li, Liang Yang, Hongfei Lin

Reasoning-Guided Exploration for Online DPO (2026.findings-acl)

Challenge:	Existing humor computing research focuses on content while neglecting interaction relationships in social media.
Approach:	They propose a dataset which introduces social context information from social media . they propose 'humor recognition' task and 'horror evaluation task'
Outcome:	The proposed model incorporates social context information from social media . it shows that it is efficient and can be used to evaluate humor in real life .

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Zetian Hu, Shunyu Liu, Ting-En Lin, Fei Huang, Yongbin Li, Dacheng Tao

SenteCon: Leveraging Lexicons to Learn Human-Interpretable Language Representations (2023.findings-acl)

Challenge:	Recent work has aimed to enhance reasoning capabilities of language models, but these methods are limited to domains with objectively verifiable answers.
Approach:	They propose a self-play framework to improve reasoning on general-domain data.
Outcome:	Experiments show that the proposed framework improves reasoning performance on general-domain data while maintaining competitive performance on verifiable academic benchmarks.

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Victoria Lin, Louis-Philippe Morency

Evaluating Readability and Faithfulness of Concept-based Explanations (2024.emnlp-main)

Challenge:	In many settings, it is important to understand a model’s decision-making process.
Approach:	They propose a method for introducing human interpretability in deep language representations by encoding a passage of text as a layer of interpretable categories.
Outcome:	The proposed method outperforms existing interpretable language representations on downstream tasks and on agreement with human characterizations of the text.

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Meng Li, Haoran Jin, Ruixuan Huang, Zhihao Xu, Defu Lian, Zijia Lin, Di Zhang, Xiting Wang

Deconvolution-Based Global Decoding for Neural Machine Translation (C18-1)

Challenge:	Existing methods for evaluating concepts from different perspectives lack a unified formalization.
Approach:	They propose a formal definition of concepts generalizing to diverse concept-based explanations’ settings and apply it to other types of explanations or tasks.
Outcome:	Extensive experimental analysis was carried out to determine the evaluation measures for explanation evaluation measures.

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Junyang Lin, Xu Sun, Xuancheng Ren, Shuming Ma, Jinsong Su, Qi Su

Is LLM an Overconfident Judge? Unveiling the Capabilities of LLMs in Detecting Offensive Language with Annotation Disagreement (2025.findings-acl)

Challenge:	Existing models for Neural Machine Translation (NMT) use Recurrent Neural Network (RNN) to generate translation word by word following a sequential order.
Approach:	They propose a Neural Machine Translation (NMT) model that decodes the sequence with the guidance of its structural prediction of the target-side context.
Outcome:	The proposed model is more competitive compared with the state-of-the-art methods and reduces repetition with the instruction from the target-side context for decoding.

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Junyu Lu, Kai Ma, Kaichun Wang, Kelaiti Xiao, Roy Ka-Wei Lee, Bo Xu, Liang Yang, Hongfei Lin

Silencing the Guardrails: Inference-Time Jailbreaking via Dynamic Contextual Representation Ablation (2026.findings-acl)

Challenge:	Existing studies focus on evaluating large language models' ability to handle disagreement cases.
Approach:	They evaluate the performance of large language models in detecting offensive language at varying levels of agreement.
Outcome:	The proposed model improves detection accuracy and model alignment with human judgment by using disagreement samples in training.

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Wenpeng Xing, Moran Fang, Guangtai Wang, Changting Lin, Meng Han

RepoGenesis: Benchmarking End-to-End Microservice Generation from Readme to Repository (2026.acl-long)

Challenge:	Existing strategies to circumvent safety constraints face significant trade-offs between effectiveness and efficiency.
Approach:	They propose a framework that allows to infer model refusal behaviors without expensive parameter updates or training.
Outcome:	The proposed framework outperforms baselines in multiple safety-aligned open-source LLMs.

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Zhiyuan Peng, Xin Yin, Pu Zhao, Fangkai Yang, Lu Wang, Ran Jia, Xu Chen, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang

Challenge:	Existing benchmarks focus on isolated function/class-level generation, neglecting complete microservice repository generation.
Approach:	They propose a multilingual benchmark for repository-level end-to-end web microservice generation that reflects real-world development workflows.
Outcome:	The benchmark compared 106 repositories across 18 domains and 11 frameworks and 1,258 API endpoints and 2,335 test cases.

Learn Continually, Generalize Rapidly: Lifelong Knowledge Accumulation for Few-shot Learning (2021.findings-emnlp)

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Xisen Jin, Bill Yuchen Lin, Mohammad Rostami, Xiang Ren

Cross-lingual Text-to-SQL Semantic Parsing with Representation Mixup (2022.findings-emnlp)

Challenge:	Existing models that pursue rapid generalization to new tasks are mostly trained in a single shot on fixed datasets, unable to dynamically expand their knowledge.
Approach:	They propose a new learning setup that assumes a model learns from a sequence of diverse NLP tasks arriving sequentially, accumulating knowledge for improved generalization to new tasks.
Outcome:	The proposed learning setup improves generalization ability while retaining performance on the tasks learned earlier.

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Peng Shi, Linfeng Song, Lifeng Jin, Haitao Mi, He Bai, Jimmy Lin, Dong Yu

Large Language Model-based Human-Agent Collaboration for Complex Task Solving (2024.findings-emnlp)

Challenge:	Experimental results show that Rex can benefit from cross-lingual training and improve the effectiveness of semantic parsers.
Approach:	They propose a Representation Mixup Framework for effectively exploiting translations in the cross-lingual Text-to-SQL task.
Outcome:	The proposed framework can benefit from cross-lingual training and improve the effectiveness of semantic parsers, achieving state-of-the-art performance.

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Xueyang Feng, Zhi-Yuan Chen, Yujia Qin, Yankai Lin, Xu Chen, Zhiyuan Liu, Ji-Rong Wen

Rerank Before You Reason: Analyzing Reranking Tradeoffs through Effective Token Cost in Deep Search Agents (2026.findings-acl)

Challenge:	Recent advances in large language models have led to the development of LLM-based autonomous agents.
Approach:	They propose a Reinforcement Learning-based Human-Agent Collaboration method which trains a policy model to determine the most opportune stages for human intervention within the task-solving process.
Outcome:	The proposed method improves human-agent collaboration significantly through well-planned, limited human intervention.

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Sahel Sharifymoghaddam, Jimmy Lin

Multi-level Alignment Pretraining for Multi-lingual Semantic Parsing (2020.coling-main)

Challenge:	Recent work emphasizes improving efficiency in LLM-based systems, especially for longcontext and multi-step reasoning.
Approach:	They analyze the role of listwise reranking in deep search pipelines and compare their results to a novel ETC metric to determine model scale and reasoning effort.
Outcome:	The proposed model scale, reasoning effort, reranking depth, and total token cost (ETC) metric improve retrieval and end-to-end accuracy and moderate reranked agents achieve comparable accuracy at substantially lower cost.

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Bo Shao, Yeyun Gong, Weizhen Qi, Nan Duan, Xiaola Lin

ChatGPT Is a Knowledgeable but Inexperienced Solver: An Investigation of Commonsense Problem in Large Language Models (2024.lrec-main)

Challenge:	Existing methods for multilingual semantic parsing only handle monolingual parsers, while in real world applications such as Chatbot and search engine, we generally need to handle multi-lingual semanticparsing.
Approach:	They propose a multi-level alignment pretraining method in a unified architecture for multi-lingual semantic parsing.
Outcome:	The proposed method outperforms state-of-the-art methods on a publicly avail-able multi-lingual semantic parsing dataset and a newly constructed dataset.

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Ning Bian, Xianpei Han, Le Sun, Hongyu Lin, Yaojie Lu, Ben He, Shanshan Jiang, Bin Dong

A Meaning-Based Statistical English Math Word Problem Solver (N18-1)

Challenge:	acquiring and representing commonsense in machines has posed a long-standing challenge (Li et al., 2021; Zhang e t al, 2022; Zhou e al. 2023) .
Approach:	They use a commonsense-based LLM to evaluate ChatGPT's commonsensing abilities by analyzing 11 datasets and generating knowledge descriptions.
Outcome:	The proposed model can achieve good QA accuracies while still struggling with certain domains of datasets.

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Chao-Chun Liang, Yu-Shiang Wong, Yi-Chung Lin, Keh-Yih Su

Bootstrapping LLM-based Task-Oriented Dialogue Agents via Self-Talk (2024.findings-acl)

Challenge:	Experimental results show that the proposed approach understands the meaning of each quantity in the text more.
Approach:	They propose a meaning-based approach for solving English math word problems . they analyze text, transform body and question parts into corresponding logic forms . Statistical models are proposed to select operator and operands .
Outcome:	The proposed approach outperforms existing systems on benchmark and noisy datasets.

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Dennis Ulmer, Elman Mansimov, Kaixiang Lin, Lijia Sun, Xibin Gao, Yi Zhang

Improved Unsupervised Chinese Word Segmentation Using Pre-trained Knowledge and Pseudo-labeling Transfer (2023.emnlp-main)

Challenge:	Large language models (LLMs) are powerful dialogue agents, but specializing them towards fulfilling a specific function can be prohibitive in terms of feasibility, time, and resources.
Approach:	They propose a method for training large language models by enabling "self-talk" they propose supervised fine-tuning of LLMs to improve quality of dialogues .
Outcome:	The proposed method generates training data via "self-talk" of LLMs that can be refined and utilized for supervised fine-tuning.

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Hsiu-Wen Li, Ying-Jia Lin, Yi-Ting Li, Chun Lin, Hung-Yu Kao

ProcessBench: Identifying Process Errors in Mathematical Reasoning (2025.acl-long)

Challenge:	Existing approaches to unsupervised Chinese word segmentation require multiple inferences to perform word segmenting.
Approach:	They propose a method that integrates the segmentation signal from an unsupervised language model to a pre-trained BERT classifier under a pseudo-labeling framework.
Outcome:	The proposed method achieves state-of-the-art performance on the eight UCWS tasks while significantly reducing training time compared to previous approaches.

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Chujie Zheng, Zhenru Zhang, Beichen Zhang, Runji Lin, Keming Lu, Bowen Yu, Dayiheng Liu, Jingren Zhou, Junyang Lin

MT-Video-Bench: A Holistic Video Understanding Benchmark for Evaluating Multimodal LLMs in Multi-Turn Dialogues (2026.findings-acl)

Challenge:	Existing models fail to generalize to more challenging math problems, authors say . existing benchmarks related to assessing language models' reasoning process are limited .
Approach:	They propose a tool to measure language models' ability to identify erroneous steps in reasoning . they use two types of models: process reward models and critic models .
Outcome:	The proposed model outperforms existing models in evaluating language models' reasoning process . the best open-source model has demonstrated the critique capability competitive with the proprietary model .

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Yaning Pan, Qianqian Xie, Guohui Zhang, Zekun Moore Wang, Yongqian Wen, Yuanxing Zhang, Haoxuan Hu, Zhiyu Pan, Yibing Huang, Zhidong Gan, Yonghong Lin, An Ping, Shihao Li, Yanghai Wang, Tianhao Peng, Jiaheng Liu

Evaluating Psychological Safety of Large Language Models (2024.emnlp-main)

Challenge:	Existing evaluation benchmarks for Multimodal Large Language Models (MLLMs) focus on single-turn question answering, overlooking the complexity of multi-turn dialogues in real-world scenarios.
Approach:	They propose a video understanding benchmark for MLLMs in multi-turn dialogues that assesses six core competencies that focus on perceptivity and interactivity.
Outcome:	The MT-Video-Bench evaluates 1,000 multi-turn dialogues from diverse domains and reveals significant performance discrepancies and limitations in handling multi-turned video dialogues.

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Xingxuan Li, Yutong Li, Lin Qiu, Shafiq Joty, Lidong Bing

Tree of Agents: Improving Long-Context Capabilities of Large Language Models through Multi-Perspective Reasoning (2025.findings-emnlp)

Challenge:	a recent study evaluated the psychological safety of large language models.
Approach:	They designed unbiased prompts to evaluate the psychological safety of large language models.
Outcome:	The proposed prompts showed that they were fine-tuned with behavioral metrics to reduce toxicity.

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Song Yu, Xiaofei Xu, Ke Deng, Li Li, Lin Tian

Adversarial Text Generation using Large Language Models for Dementia Detection (2024.emnlp-main)

Challenge:	Large language models face persistent challenges when handling long-context tasks . existing methods that reduce input have the risk of discarding key information .
Approach:	To address this issue, we propose a multi-agent reasoning framework called Tree of Agents . the framework segments input into chunks processed by independent agents .
Outcome:	The proposed model outperforms baseline models on long-context tasks.

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Youxiang Zhu, Nana Lin, Kiran Balivada, Daniel Haehn, Xiaohui Liang

M3-VQA: A Benchmark for Multimodal, Multi-Entity, Multi-Hop Visual Question Answering (2026.acl-long)

Challenge:	Large language models excel in text classification tasks, but they do not perform well with picture description.
Approach:	They propose an interpretable classification approach by Adversarial Text Generation (ATG) that could relate dementia detection with other tasks.
Outcome:	The proposed approach achieves 85% accuracy, >10% improvement over the previous methods.

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Jiatong Ma, Longteng Guo, Yuchen Liu, Zijia Zhao, Dongze Hao, Xuanxu Lin, Jing Liu

RMTBench: Benchmarking LLMs Through Multi-Turn User-Centric Role-Playing (2025.findings-emnlp)

Challenge:	Existing knowledge-based VQA benchmarks focus on coarse-grained categories and simple reasoning over single entities.
Approach:	They propose a knowledge-based Visual Question Answering benchmark to enhance multimodality evaluation.
Outcome:	The proposed benchmark improves evaluation of multimodal large language models in fine-grained multimodal entity understanding and complex multihop reasoning.

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Hao Xiang, Tianyi Tang, Yang Su, Bowen Yu, An Yang, Fei Huang, Yichang Zhang, Yaojie Lu, Hongyu Lin, Xianpei Han, Jingren Zhou, Junyang Lin, Le Sun

Differentiable Open-Ended Commonsense Reasoning (2021.naacl-main)

Challenge:	Existing benchmarks focus on character-centric approach and fail to reflect real-world applications.
Approach:	RMTBench is a user-centric bilingual role-playing benchmark featuring 80 diverse characters and over 8,000 dialogue rounds.
Outcome:	RMTBench features 80 diverse characters and over 8,000 dialogue rounds.

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Bill Yuchen Lin, Haitian Sun, Bhuwan Dhingra, Manzil Zaheer, Xiang Ren, William Cohen

Knowledgeable or Educated Guess? Revisiting Language Models as Knowledge Bases (2021.acl-long)

Challenge:	Existing commonsense reasoning models work by scoring a question-candidate pair, but new approaches are needed to answer multiple-choice questions.
Approach:	They propose to use a corpus of commonsense facts to answer a commonsensical question without any pre-defined choices as a resource.
Outcome:	The proposed model outperforms baseline methods by a large margin in the open-ended commonsense reasoning task.

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Boxi Cao, Hongyu Lin, Xianpei Han, Le Sun, Lingyong Yan, Meng Liao, Tong Xue, Jin Xu

Natural Language Generation for Effective Knowledge Distillation (D19-61)

Challenge:	Recent studies show that pre-trained masked language models can be factual knowledge bases.
Approach:	They conduct a rigorous study to explore the underlying predicting mechanisms of MLMs . they find that previous decent performance mainly owes to the biased prompts which overfit dataset artifacts a .
Outcome:	The proposed model improves on illustrative cases and external contexts . the results question the previous findings that MLMs can be reliable factual knowledge bases .

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Raphael Tang, Yao Lu, Jimmy Lin

CulturalBench: A Robust, Diverse and Challenging Benchmark for Measuring LMs’ Cultural Knowledge Through Human-AI Red-Teaming (2025.acl-long)

Challenge:	Knowledge distillation can transfer knowledge from deep language representation models to shallow word embedding-based neural networks.
Approach:	They propose to build an unlabeled transfer dataset to enable effective knowledge transfer . they hypothesize that this principled, general approach outperforms rule-based techniques .
Outcome:	The proposed method outperforms OpenAI GPT on four datasets in sentiment classification, sentence similarity, and linguistic acceptability.

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Yu Ying Chiu, Liwei Jiang, Bill Yuchen Lin, Chan Young Park, Shuyue Stella Li, Sahithya Ravi, Mehar Bhatia, Maria Antoniak, Yulia Tsvetkov, Vered Shwartz, Yejin Choi

Merger-as-a-Stealer: Stealing Targeted PII from Aligned LLMs with Model Merging (2025.emnlp-main)

Challenge:	CulturalBench is a set of 1,696 human-written and human-verified questions to assess LMs’ cultural knowledge covering 45 global regions including underrepresented ones like Bangladesh, Zimbabwe, and Peru.
Approach:	They construct a set of 1,696 human-written and human-verified questions to assess LMs' cultural knowledge, covering 45 global regions including underrepresented ones like Bangladesh, Zimbabwe, and Peru.
Outcome:	The proposed model outperforms other models across cultures, while underperforming on questions related to North Africa, South America and Middle East.

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Lin Lu, Zhigang Zuo, Ziji Sheng, Pan Zhou

QA-MoE: Towards a Continuous Reliability Spectrum with Quality-Aware Mixture of Experts for Robust Multimodal Sentiment Analysis (2026.acl-long)

Challenge:	Model merging is a promising approach for updating large language models . but unmonitored mergers can introduce significant security vulnerabilities .
Approach:	They propose a model merging attack surface where a malicious merger can extract PII from an aligned model with model merg.
Outcome:	The proposed framework can extract PII from an aligned model with model merging.

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Yitong Zhu, Yuxuan Jiang, Guanxuan Jiang, Bojing Hou, Peng Yuan Zhou, Ge Lin, Yuyang Wang

AgentAsk: Multi-Agent Systems Need to Ask (2026.acl-long)

Challenge:	Existing models that use multimodal inputs are often noisy or incomplete.
Approach:	They propose a Quality-Aware Mixture-of-Experts framework that quantifies modality reliability via aleatoric uncertainty.
Outcome:	The proposed framework is competitive or state-of-the-art across diverse degradation scenarios and exhibits a promising One-Checkpoint-for-all property in practice.

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Bohan Lin, Kuo Yang, Zelin Tan, Yingchuan Lai, Chen Zhang, Guibin Zhang, Xinlei Yu, Miao Yu, Xu Wang, Yudong Zhang, Yang Wang

Fast and Scalable Dialogue State Tracking with Explicit Modular Decomposition (2021.naacl-main)

Challenge:	Multi-agent systems fail to consistently outperform strong single-a agent baselines due to error propagation at inter-aggent message handoffs.
Approach:	They propose an edge-level error taxonomy that identifies four main errors in multi-agent interactions as data gaps, signal corruption, referential drift and capacity gaps as primary sources of failure.
Outcome:	The proposed module outperforms existing systems on five benchmarks and is architecture-agnostic.

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Dingmin Wang, Chenghua Lin, Qi Liu, Kam-Fai Wong

Multimodal Sarcasm Target Identification in Tweets (2022.acl-long)

Challenge:	Existing approaches for dialogue state tracking are mainly based on classification-based and extraction-based methods.
Approach:	They propose a model which incorporates both classification-based and extraction-based methods and integrates four modules to jointly extract dialogue states.
Outcome:	The proposed model outperforms the state-of-the-art models in multi-domain dialogues with many turns of utterances.

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Jiquan Wang, Lin Sun, Yi Liu, Meizhi Shao, Zengwei Zheng

Language Scaling for Universal Suggested Replies Model (2021.naacl-industry)

Challenge:	Existing methods to detect sarcasm target with text lacking context are not sufficient and complete.
Approach:	They propose a multi-modal sarcasm target identification task that performs both textual and visual detection.
Outcome:	The proposed model can perform textual target labeling and visual target detection.

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Qianlan Ying, Payal Bajaj, Budhaditya Deb, Yu Yang, Wei Wang, Bojia Lin, Milad Shokouhi, Xia Song, Yang Yang, Daxin Jiang

SciVQR: A Multidisciplinary Multimodal Benchmark for Advanced Scientific Reasoning Evaluation (2026.findings-acl)

Challenge:	We consider scaling automated suggested replies (SR) to multiple languages for a commercial email application.
Approach:	They propose a multi-lingual multi-task continual learning framework with auxiliary tasks and language adapters to train universal language representation across regions.
Outcome:	The proposed model reduces catastrophic forgetting and improves cross-lingual transfer across languages while reducing training costs.

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Longteng Guo, Xuanxu Lin, Dongze Hao, Tongtian Yue, Pengkang Huo, Jiatong Ma, Yuchen Liu, Jing Liu

Confidence v.s. Critique: A Decomposition of Self-Correction Capability for LLMs (2025.acl-long)

Challenge:	Existing benchmarks for multimodal large language models fail to capture complexity and traceability of reasoning processes . SciVQR includes domain-specific visuals and challenges models to combine visual comprehension with reasoning.
Approach:	They propose a multimodal benchmark for scientific reasoning covering 54 subfields . SciVQR includes domain-specific visuals and challenges models to combine visual comprehension with reasoning .
Outcome:	SciVQR evaluates 54 subfields in mathematics, physics, chemistry, geography, astronomy, and biology . the results highlight the need for improved multi-step reasoning and integration of interdisciplinary knowledge .

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Zhe Yang, Yichang Zhang, Yudong Wang, Ziyao Xu, Junyang Lin, Zhifang Sui

Challenge:	Existing approaches to improve self-correction performance of Large Language Models are based on intrinsic selfcorrectione, which allows the model to check and revise its selfgenerated answers without external feedback.
Approach:	They propose to decompose the self-correction capability into confidence and critique capabilities and a metric for overall self-corretion capability evaluation.
Outcome:	The proposed method outperforms vanilla SFT and achieves much higher accuracy after self-correction.

IndiVec: An Exploration of Leveraging Large Language Models for Media Bias Detection with Fine-Grained Bias Indicators (2024.findings-eacl)

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Luyang Lin, Lingzhi Wang, Xiaoyan Zhao, Jing Li, Kam-Fai Wong

Prompt Tuning for Discriminative Pre-trained Language Models (2022.findings-acl)

Challenge:	Existing studies on social media bias detection focus on fine-tuning models specific to particular datasets and testing them on corresponding test sets.
Approach:	They propose a general bias detection framework, IndiVec, built upon large language models and vector databases.
Outcome:	The proposed framework outperforms baseline methods on four political bias datasets and provides explicit top-k indicators to interpret bias predictions.

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Yuan Yao, Bowen Dong, Ao Zhang, Zhengyan Zhang, Ruobing Xie, Zhiyuan Liu, Leyu Lin, Maosong Sun, Jianyong Wang

Tracking Brand-Associated Polarity-Bearing Topics in User Reviews (2023.tacl-1)

Challenge:	Recent studies have shown promising results of prompt tuning in stimulating pre-trained language models (PLMs) for natural language processing tasks.
Approach:	They propose a prompt tuning framework that reformulates NLP tasks into a discriminative language modeling problem.
Outcome:	The proposed framework improves on text classification and question answering tasks and prevents unstable tuning problems in low-resource settings.

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Runcong Zhao, Lin Gui, Hanqi Yan, Yulan He

Challenge:	Existing models that infer brand polarity scores from reviews are not able to infer polarities directly.
Approach:	They propose a dynamic Brand-Topic Model which detects and tracks brand-associated sentiment scores and polarity-bearing topics from product reviews organized in temporally ordered time intervals.
Outcome:	The proposed model outperforms competitive models on a MakeupAlley and hotel review datasets.

Integrating Text and Image: Determining Multimodal Document Intent in Instagram Posts (D19-1)

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Julia Kruk, Jonah Lubin, Karan Sikka, Xiao Lin, Dan Jurafsky, Ajay Divakaran

RoleMRC: A Fine-Grained Composite Benchmark for Role-Playing and Instruction-Following (2025.findings-acl)

Challenge:	Existing studies on text-image content have focused on image as primary content, and text as secondary content.
Approach:	They propose a multimodal dataset of 1299 Instagram posts labeled for three orthogonal taxonomies . they show that employing both text and image improves intent detection by 9.6 .
Outcome:	The proposed model shows that using both text and image improves intent detection by 9.6 compared to using only the image modality.

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Junru Lu, Jiazheng Li, Guodong Shen, Lin Gui, Siyu An, Yulan He, Di Yin, Xing Sun

Forest Before Trees: Latent Superposition for Efficient Visual Reasoning (2026.acl-long)

Challenge:	Existing role-playing datasets mostly contribute to controlling role style and knowledge boundaries, but overlook role-following in instruction-follower scenarios.
Approach:	They propose a fine-grained role-playing and instruction-following composite benchmark, named RoleMRC, which includes multi-turn dialogues between ideal roles and humans, including free chats or discussions upon given passages .
Outcome:	The proposed model improves instruction-following without compromising general role-playing and reasoning capabilities.

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Yubo Wang, Juntian Zhang, Yichen Wu, Yankai Lin, Nils Lukas, Yuhan Liu

Better Quality Pre-training Data and T5 Models for African Languages (2023.emnlp-main)

Challenge:	Recent latent reasoning methods suffer from a bandwidth bottleneck . explicit textual rationales suffer from premature semantic collapse .
Approach:	They propose a new paradigm that reformulates visual deduction via Dynamic Windowed Alignment Learning.
Outcome:	The proposed paradigm achieves state-of-the-art performance among latent reasoning methods surpassing the strong baseline Monet by 5.03% on average.

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Akintunde Oladipo, Mofetoluwa Adeyemi, Orevaoghene Ahia, Abraham Owodunni, Odunayo Ogundepo, David Adelani, Jimmy Lin

An Empirical Study of Multimodal Model Merging (2023.findings-emnlp)

Challenge:	Existing web crawls have demonstrated quality issues for low-resource languages . Existing pretraining corpora have numerous quality issues .
Approach:	They propose to audit existing pretraining corpora to understand and rectify quality issues . they pretrain a new T5-based model and evaluate its performance on multiple tasks .
Outcome:	The proposed model outperforms existing pretrained models on four NLP tasks.

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Yi-Lin Sung, Linjie Li, Kevin Lin, Zhe Gan, Mohit Bansal, Lijuan Wang

QuaRTz: An Open-Domain Dataset of Qualitative Relationship Questions (D19-1)

Challenge:	Existing studies have shown that model merging can generate a multi-task solution without synchronous training.
Approach:	They propose to merge vision, language, and cross-modal transformers of a modality-specific architecture to create a parameter-efficient architecture.
Outcome:	The proposed model merging outperforms naive models on various tasks with improvements of 3% on VQA, 7% on COCO retrieval, 25% on NLVR2, 14% on Flickr30k and 3% ADE20k.

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Oyvind Tafjord, Matt Gardner, Kevin Lin, Peter Clark

Challenge:	Unlike previous datasets, the general knowledge is textual and not tied to a fixed set of relationships.
Approach:	They introduce the first open-domain dataset, called QuaRTz, for reasoning about textual qualitative relationships.
Outcome:	The proposed dataset is the first open-domain dataset for reasoning about qualitative relationships.

Alexa Conversations: An Extensible Data-driven Approach for Building Task-oriented Dialogue Systems (2021.naacl-demos)

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Anish Acharya, Suranjit Adhikari, Sanchit Agarwal, Vincent Auvray, Nehal Belgamwar, Arijit Biswas, Shubhra Chandra, Tagyoung Chung, Maryam Fazel-Zarandi, Raefer Gabriel, Shuyang Gao, Rahul Goel, Dilek Hakkani-Tur, Jan Jezabek, Abhay Jha, Jiun-Yu Kao, Prakash Krishnan, Peter Ku, Anuj Goyal, Chien-Wei Lin, Qing Liu, Arindam Mandal, Angeliki Metallinou, Vishal Naik, Yi Pan, Shachi Paul, Vittorio Perera, Abhishek Sethi, Minmin Shen, Nikko Strom, Eddie Wang

SpidR-Adapt: A Universal Speech Representation Model for Few-Shot Adaptation (2026.acl-long)

Challenge:	Traditional goal-oriented dialogue systems require annotations which are hard to obtain for every new domain, limiting scalability.
Approach:	They propose a data-driven approach to building goal-oriented dialogue systems . they use a seed dialogue simulator to generate annotated conversations instead of collecting annotations .
Outcome:	The proposed system improves turn-level action signature prediction accuracy by 50% . the system is scalable, extensible and data efficient .

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Mahi Luthra, Jiayi Shen, Maxime Poli, Angelo Ortiz Tandazo, Yosuke Higuchi, Youssef Benchekroun, Martin Gleize, Charles-Éric Saint-James, Dongyan Lin, Phillip Rust, Angel Villar-Corrales, null Surya, Vanessa Stark, Rashel Moritz, Juan Pino, Yann LeCun, Emmanuel Dupoux

Enhancing Multi-Agent Debate System Performance via Confidence Expression (2025.findings-emnlp)

Challenge:	Empirically, SpidR-Adapt achieves rapid gains in phonemic discriminability and downstream spoken language modeling scores . current self-supervised learning models require thousands of hours of training data to learn meaningful linguistic representations.
Approach:	They propose a bi-level optimization framework for rapid adaptation of speech units to new languages using minimal unlabeled data.
Outcome:	The proposed model achieves rapid gains in phonemic discriminability and spoken language modeling scores . it surpasses in-domain toplines after training on less than 1h of target-language audio .

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Zijie Lin, Bryan Hooi

Challenge:	Multi-Agent Debate systems leverage multiple LLMs to improve task performance.
Approach:	They propose to integrate confidence expression into MAD systems to help LLMs communicate their confidence levels.
Outcome:	The proposed approach improves debate effectiveness and overall system performance by integrating confidence expression into MAD systems.

MPTc-Bench: Measuring Cross-market Generative Ability of Vision-Language Models via Movie Poster Transcreation (2026.findings-acl)

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Youyuan Lin, Yuan Li, Yahan Yu, Fei Cheng, Shin’ya Nishida, Chenhui Chu

FLAG-TRADER: Fusion LLM-Agent with Gradient-based Reinforcement Learning for Financial Trading (2025.findings-acl)

Challenge:	Recent work adapts textual transcreation to image editing and formulates image transcreations to better match a target audience while preserving meaning.
Approach:	They propose a two-stage planner-editor pipeline in which an VLM planner specifies executable edits and an image editor renders them.
Outcome:	The proposed model can transcreate a visual asset for a different market while preserving its identity while matching market-specific design preferences and multilingual typography.

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Guojun Xiong, Zhiyang Deng, Keyi Wang, Yupeng Cao, Haohang Li, Yangyang Yu, Xueqing Peng, Mingquan Lin, Kaleb E Smith, Xiao-Yang Liu, Jimin Huang, Sophia Ananiadou, Qianqian Xie

Token-level Inference-Time Alignment for Vision-Language Models (2026.findings-acl)

Challenge:	Large language models (LLMs) have impressive reasoning capabilities in financial tasks, but struggle with multi-step, goal-oriented scenarios in interactive financial markets.
Approach:	They propose a framework that integrates large language models with gradient-driven reinforcement learning (RL) policy optimization.
Outcome:	The proposed framework improves performance in trading and other financial domain tasks.

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Kejia Chen, Junjun Zheng, Jiawen Zhang, Manxi Lin, Xiao Pan, Jiacong Hu, Jian Lou, Zunlei Feng, Mingli Song

rosaOS: Agentic Operating System for Embodied LLMs (2026.acl-demo)

Challenge:	Vision-Language Models (VLMs) often prioritize linguistic fluency over visual fidelity . despite widespread adoption, VLMs often exhibit a critical failure mode: hallucination .
Approach:	They propose a framework for Token-level Inference-Time Alignment that steers the decoding process without updating the base model parameters.
Outcome:	The proposed framework improves performance on 13 benchmarks across architectures . it boosts LLaVA-1.5-7B by 8.6% on MMVet and achieves a 74.0 MMStar score .

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Yijun Ge, Kushaldeep Mujral, Karthik Nambiar, Jimmy Lin

WR-One2Set: Towards Well-Calibrated Keyphrase Generation (2022.emnlp-main)

Challenge:	Existing LLM–robotic systems are tightly intertwined, making it difficult to switch hardware, add extra capabilities, or expand to multiple devices without bespoke integration.
Approach:	They propose an open-source agentic operating system for embodied LLMs . rosaOS integrates agentic tool-calling and ROS for robot interactions .
Outcome:	The proposed system integrates with the Reachy Mini robot and supports a multi-device setup with a quadruped robot, wheeled mobile robot, and smart lamp.

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Binbin Xie, Xiangpeng Wei, Baosong Yang, Huan Lin, Jun Xie, Xiaoli Wang, Min Zhang, Jinsong Su

Polynomial Expansion Rank Adaptation: Enhancing Low-Rank Fine-Tuning with High-Order Interactions (2026.findings-acl)

Challenge:	Experimental results show that keyphrase generation has serious calibration errors . ONE2SET generates short phrases summarizing an input document .
Approach:	They propose a paradigm for keyphrase generation that generates short phrases summarizing an input document.
Outcome:	The proposed model over-estimates tokens and makes it well-calibrated on common datasets.

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Wenhao Zhang, Lin Mu, Li Ni, Peiquan Jin, Yiwen Zhang

DialogUSR: Complex Dialogue Utterance Splitting and Reformulation for Multiple Intent Detection (2022.findings-emnlp)

Challenge:	Low-rank adaptation (LoRA) is a widely used strategy for efficient fine-tuning of large language models, but its strictly linear structure limits expressive capacity.
Approach:	They propose a method that introduces structured polynomial expansion directly into the low-rank factor space.
Outcome:	The proposed method outperforms state-of-the-art methods across diverse benchmarks.

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Haoran Meng, Zheng Xin, Tianyu Liu, Zizhen Wang, He Feng, Binghuai Lin, Xuemin Zhao, Yunbo Cao, Zhifang Sui

GenPT: Beyond Self-Report for Reliable LLM Psychometrics via Generative Projective Testing (2026.acl-long)

Challenge:	DialogUSR is a plug-in and domain-agnostic module that empowers multi-intent detection for chatbots . a single user query triggers inquiries on highspeed train ticket price and weather of destination.
Approach:	They propose a dialog utterance splitting and reformulation task that splits multi-intent user query into multiple single-intention sub-queries and recovers all coreferred and omitted information in the sub-questions.
Outcome:	The proposed model can be used to split multi-intent user queries into multiple sub-queries . it can be trained in two stages and perform in-depth analyses on the proposed models .

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Ming Wang, Shuang Wu, Bixuan Wang, Lu Lin, Yuxin Chen, Xiaocui Yang, Daling Wang, Shi Feng, Yifei Zhang, Yufan Sun

Challenge:	Large language models (LLMs) inherit contamination from training corpora, directional bias under social-desirability framing, and limited responsiveness to context beyond the item text.
Approach:	They propose a paradigm that reformulates TAT, Rorschach, and SCT with newly generated stimuli and organises assessment as a three-stage pipeline.
Outcome:	The proposed paradigm reformulates TAT, Rorschach, and SCT with newly generated stimuli and organises assessment as a three-stage pipeline.

OntoEA: Ontology-guided Entity Alignment via Joint Knowledge Graph Embedding (2021.findings-acl)

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Yuejia Xiang, Ziheng Zhang, Jiaoyan Chen, Xi Chen, Zhenxi Lin, Yefeng Zheng

A Dual-Perspective NLG Meta-Evaluation Framework with Automatic Benchmark and Better Interpretability (2025.acl-long)

Challenge:	Existing methods for aligning knowledge graph entities ignore the ontology which contains critical meta information such as classes and membership relationships with entities.
Approach:	They propose an ontology-guided method where KGs and ontologies are jointly embedded.
Outcome:	Extensive experiments on seven public and industrial benchmarks show the ontology-guided method performs well and is cost-effective.

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Xinyu Hu, Mingqi Gao, Li Lin, Zhenghan Yu, Xiaojun Wan

MMDialog: A Large-scale Multi-turn Dialogue Dataset Towards Multi-modal Open-domain Conversation (2023.acl-long)

Challenge:	Existing evaluation metrics are insufficient to meet requirements for natural language generation.
Approach:	They propose a dual-perspective NLG meta-evaluation framework that focuses on different evaluation capabilities and a method of automatically constructing benchmarks without requiring new human annotations.
Outcome:	The proposed framework improves interpretability and provides better performance for 16 representative LLMs.

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Jiazhan Feng, Qingfeng Sun, Can Xu, Pu Zhao, Yaming Yang, Chongyang Tao, Dongyan Zhao, Qingwei Lin

Gumbel Reranking: Differentiable End-to-End Reranker Optimization (2025.acl-long)

Challenge:	MMDialog is a dataset of 1.08 million real-world dialogues with 1.53 million unique images across 4,184 topics.
Approach:	They propose to use a curated set of 1.08 million dialogues with 1.53 million unique images to generalize the open domain.
Outcome:	The proposed system can predict responses to multi-modal content with state-of-the-art techniques and measure their performance.

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Siyuan Huang, Zhiyuan Ma, Jintao Du, Changhua Meng, Weiqiang Wang, Jingwen Leng, Minyi Guo, Zhouhan Lin

TrendSim: Simulating Trending Topics in Social Media Under Poisoning Attacks with LLM-based Multi-agent System (2025.findings-naacl)

Challenge:	Existing distillation-based approaches suffer from training-inference misalignment and fail to capture interdependencies among candidate documents.
Approach:	They propose a method to optimize rerankers by learning a stochastic, document-wise Top-k attention mask using the Gumbel Trick and Relaxed Top-K Sampling.
Outcome:	The proposed framework minimizes the overall language loss and improves recall on hotpotQA.

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Zeyu Zhang, Jianxun Lian, Chen Ma, Yaning Qu, Ye Luo, Lei Wang, Rui Li, Xu Chen, Yankai Lin, Le Wu, Xing Xie, Ji-Rong Wen

LongCLI-Bench: A Preliminary Benchmark and Study for Long-horizon Agentic Programming in Command-Line Interfaces (2026.findings-acl)

Challenge:	Trending topics bring in a new channel for poisoning attacks, resulting in negative impacts on society.
Approach:	They propose an LLM-based multi-agent system to simulate trending topics in social media . they propose a time-aware interaction mechanism, centralized message dissemination, and an interactive system .
Outcome:	The proposed system simulates trending topics under poisoning attacks on social media platforms.

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Yukang Feng, Jianwen Sun, Zelai Yang, Jiaxin Ai, Chuanhao Li, Zizhen Li, Fanrui Zhang, Kang He, Rui Ma, Jifan Lin, Jie Sun, Yang Xiao, Sizhuo Zhou, Wenxiao Wu, Yiming Liu, Pengfei Liu, Shenglin Zhang, Kaipeng Zhang

A Middle Path for On-Premises LLM Deployment: Preserving Privacy Without Sacrificing Model Confidentiality (2025.emnlp-main)

Challenge:	Existing benchmarks for agentic programming in long-horizon command-line interface tasks are limited by short task horizons, data contamination from GitHub scraping, and a lack of fine-grained evaluation metrics.
Approach:	They propose a benchmark to evaluate agentic capabilities across long-horizon command-line interface tasks.
Outcome:	The proposed benchmarks cover four engineering categories: from scratch, feature addition, bug fixing, and refactoring.

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Hanbo Huang, Yihan Li, Bowen Jiang, Bo Jiang, Lin Liu, Zhuotao Liu, Ruoyu Sun, Shiyu Liang

PromptReps: Prompting Large Language Models to Generate Dense and Sparse Representations for Zero-Shot Document Retrieval (2024.emnlp-main)

Challenge:	Privacy-sensitive users require deploying large language models within their own infrastructure (on-premises) vulnerabilities in local environments can lead to unauthorized access and potential model theft.
Approach:	They propose a framework that secures a few bottom layers in a secure environment . they propose metric to optimize trade-off between protection and customization flexibility .
Outcome:	The proposed framework outperforms baselines on five models with 1.3B to 70B parameters.

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Shengyao Zhuang, Xueguang Ma, Bevan Koopman, Jimmy Lin, Guido Zuccon

Unmasking Deceptive Visuals: Benchmarking Multimodal Large Language Models on Misleading Chart Question Answering (2025.emnlp-main)

Challenge:	Large language models (LLMs) excel in zero-shot document ranking tasks.
Approach:	They propose a prompt-based re-ranking method that requires no further training but is only feasible for reranking a handful of candidates due to computational costs.
Outcome:	The proposed method can retrieve documents from the entire corpus without training and with a large amount of paired text data.

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Zixin Chen, Sicheng Song, KaShun Shum, Yanna Lin, Rui Sheng, Weiqi Wang, Huamin Qu

Video-LLaVA: Learning United Visual Representation by Alignment Before Projection (2024.emnlp-main)

Challenge:	Misleading visualizations can distort perception and lead to incorrect conclusions.
Approach:	They propose a large-scale multimodal dataset to evaluate MLLMs on misleading chart reasoning.
Outcome:	The proposed framework evaluates MLLMs on misleading chart reasoning on a large-scale multimodal dataset spanning 21 misleader types and 10 chart types . it contains 3,026 curated examples spanning standard chart code, CSV data, multiple-choice questions, and labeled explanations, validated through iterative MLML checks and exhausted expert human review.

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Bin Lin, Yang Ye, Bin Zhu, Jiaxi Cui, Munan Ning, Peng Jin, Li Yuan

TR-BERT: Dynamic Token Reduction for Accelerating BERT Inference (2021.naacl-main)

Challenge:	Existing approaches to visual-language understanding lack unified tokenization for images and videos . lack of unified visual representations makes it difficult to learn multi-modal interactions from poor projection layers.
Approach:	They propose to unify visual representation into the language feature space to advance the foundational LLM towards a unified LVLM.
Outcome:	The proposed model outperforms Video-ChatGPT on image benchmarks and on 9 image benchmark benchmarks.

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Deming Ye, Yankai Lin, Yufei Huang, Maosong Sun

Rethinking Complex Neural Network Architectures for Document Classification (N19-1)

Challenge:	Existing pre-trained language models (PLMs) are expensive in inference, making them impractical in resource-limited real-world applications.
Approach:	They propose a dynamic token reduction approach to accelerate PLMs' inference by adapting the layer number of each token to avoid redundant calculation.
Outcome:	The proposed approach speeds up BERT by 2-5 times and improves performance in long-text tasks with less computation.

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Ashutosh Adhikari, Achyudh Ram, Raphael Tang, Jimmy Lin

HierGR: Hierarchical Semantic Representation Enhancement for Generative Retrieval in Food Delivery Search (2025.acl-industry)

Challenge:	Neural network models for many NLP tasks have grown increasingly complex in recent years . authors of recent papers question the necessity of such architectures and find them quite effective .
Approach:	They propose to use regularization techniques borrowed from language modeling to improve model accuracy . they find that a simple biLSTM architecture with appropriate regularization yields competitive results .
Outcome:	a simple biLSTM model outperforms the state-of-the-art on four benchmark datasets . authors say that improvements are not real, but are attributed to mundane reasons .

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Fuwei Zhang, Xiaoyu Liu, Xinyu Jia, Yingfei Zhang, Zenghua Xia, Fei Jiang, Fuzhen Zhuang, Wei Lin, Zhao Zhang

Act-Adaptive Margin: Dynamically Calibrating Reward Models for Subjective Ambiguity (2026.acl-long)

Challenge:	Generative retrieval (GR) is an emerging search paradigm for food delivery search.
Approach:	They propose a method that harnesses the advanced query understanding capabilities of large language models to enhance the retrieval of results for complex and long-tail queries in food delivery search scenarios.
Outcome:	The proposed method increases the number of online orders by 0.68% for complex search intents.

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Feiteng Fang, Dingwei Chen, Xiang Huang, Ting-En Lin, Yuchuan Wu, Xiong Liu, Jing Ye, Ziqiang Liu, Haonan Zhang, Liang Zhu, Hamid Alinejad-Rokny, Min Yang, Yongbin Li

Challenge:	Existing approaches to reward modeling in reinforcement learning tasks are limited when dealing with ambiguous preferences.
Approach:	They propose to use AAM to dynamically calibrate preference margins using the Bradley-Terry model's internal parameter knowledge to improve reward modeling in subjective tasks.
Outcome:	The proposed approach improves reward modeling by dynamically calibrating preference margins using the model’s internal parameter knowledge.

Review-Instruct: A Review-Driven Multi-Turn Conversations Generation Method for Large Language Models (2025.findings-acl)

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Jiangxu Wu, Cong Wang, Tianhuang Su, Lin Haozhi, JunYang JunYang, Zhangchao Zhangchao, Binqiang Pan, SongpanYang SongpanYang, Mingpeng Mingpeng, Kai Shi, Zixian Li

BC-Prover: Backward Chaining Prover for Formal Theorem Proving (2024.emnlp-main)

Challenge:	Existing methods for generating multi-turn dialogue data struggle to ensure both diversity and quality in instructions.
Approach:	They propose a framework that synthesizes multi-turn conversations through an iterative "Ask-Respond-Review" process involving three agent roles: a Candidate, multiple Reviewers, and a Chairman.
Outcome:	The proposed framework synthesizes multi-turn conversations through an iterative "Ask-Respond-Review" process involving three agent roles: a Candidate, multiple Reviewers, and a Chairman.

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Yuhang He, Jihai Zhang, Jianzhu Bao, Fangquan Lin, Cheng Yang, Bing Qin, Ruifeng Xu, Wotao Yin

Text-Guided Multi-Scale Frequency Representation Adaptation (2026.acl-long)

Challenge:	Existing methods for interactive theorem proving in formal logic lack robustness and robustness.
Approach:	They propose a backward chaining framework guided by pseudo steps for proofstep generation that prioritizes pseudo steps.
Outcome:	The proposed framework improves on the miniF2F benchmark.

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Weicai Yan, Xinhua Ma, Wang Lin, Tao Jin

BotChat: Evaluating LLMs’ Capabilities of Having Multi-Turn Dialogues (2024.findings-naacl)

Challenge:	Existing methods for fine-tuning visual signals are limited by their size and complexity.
Approach:	They propose a multi-scale frequency-based fine-tuning method that integrates textual information and performs multi-level fine- tuning of visual signals in the frequency domain.
Outcome:	Extensive experiments on multimodal models, including CLIP and LLaVA, demonstrate that the proposed method significantly improves performance and efficiency with minimal cost and fast convergence within one epoch.

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Haodong Duan, Jueqi Wei, Chonghua Wang, Hongwei Liu, Yixiao Fang, Songyang Zhang, Dahua Lin, Kai Chen

Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question Answering (2020.emnlp-main)

Challenge:	Modern Large Language Models (LLMs) facilitate high-quality, multi-turn dialogues with humans, but human-based evaluation of such a capability requires substantial manual effort.
Approach:	They propose to evaluate LLMs' ability to emulate human-like, multi-turn conversations using an LLM-centric approach.
Outcome:	The proposed model emulates human-like, multi-turn conversations using an LLM-centric approach.

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Yanlin Feng, Xinyue Chen, Bill Yuchen Lin, Peifeng Wang, Jun Yan, Xiang Ren

Defending Against Social Engineering Attacks in the Age of LLMs (2024.emnlp-main)

Challenge:	Existing work on augmenting question answering models with external knowledge (e.g., knowledge graphs) lacks transparency into the model’s prediction rationale.
Approach:	They propose a knowledge-aware approach that equips pre-trained language models with a multi-hop relational reasoning module that performs multi-relational reasoning over subgraphs extracted from external knowledge graphs.
Outcome:	The proposed model performs multi-hop, multi-relational reasoning over subgraphs extracted from external knowledge graphs.

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Lin Ai, Tharindu Kumarage, Amrita Bhattacharjee, Zizhou Liu, Zheng Hui, Michael Davinroy, James Cook, Laura Cassani, Kirill Trapeznikov, Matthias Kirchner, Arslan Basharat, Anthony Hoogs, Joshua Garland, Huan Liu, Julia Hirschberg

Knowledge Graph Embedding with Hierarchical Relation Structure (D18-1)

Challenge:	Existing research has developed frameworks to understand human-to-human CSE attacks.
Approach:	They propose a modular defense pipeline that improves detection at both the message and conversation levels.
Outcome:	The proposed model can be exploited to facilitate chat-based social engineering attacks and generate high-quality CSE content, but their detection capabilities are suboptimal, leading to increased operational costs for defense.

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Zhao Zhang, Fuzhen Zhuang, Meng Qu, Fen Lin, Qing He

AuriSRec: Adversarial User Intention Learning in Sequential Recommendation (2024.findings-emnlp)

Challenge:	Existing knowledge graph embedding models embed entities and relations into latent vectors without leveraging rich information from relation structure.
Approach:	They extend existing KGE models to learn knowledge representations by leveraging relation structure . authors say their approach is capable of extending other KGEs .
Outcome:	The proposed approach can extend existing KGE models, and validates against baselines.

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Junjie Zhang, Ruobing Xie, Wenqi Sun, Leyu Lin, Xin Zhao, Ji-Rong Wen

GDTB: Genre Diverse Data for English Shallow Discourse Parsing across Modalities, Text Types, and Domains (2024.emnlp-main)

Challenge:	Existing work focuses on capturing user implicit preferences from historical interactions and matching them with the next behavior, instead of predicting user explicit intentions.
Approach:	They propose an adversarial user intention learning approach for sequential recommendaiton . the approach explicitly predicts user current intentions by taking historical reviews as inputs .
Outcome:	The proposed approach explicitly predicts user intentions by inferring their decision-making process as explained in target reviews.

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Yang Janet Liu, Tatsuya Aoyama, Wesley Scivetti, Yilun Zhu, Shabnam Behzad, Lauren Levine, Jessica Lin, Devika Tiwari, Amir Zeldes

How Fast can BERT Learn Simple Natural Language Inference? (2021.eacl-main)

Challenge:	Existing shallow discourse parsing systems focus on the Wall Street Journal corpus, but the data is limited to the news domain and is 35 years old.
Approach:	They propose to use the Wall Street Journal corpus as a benchmark for PDTB-style shallow discourse parsing.
Outcome:	The proposed dataset is compatible with PDTB, but suffers from degradation out-of-domain.

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Yi-Chung Lin, Keh-Yih Su

Boundary-Guided Policy Optimization for Memory-efficient RL of Diffusion Large Language Models (2026.acl-long)

Challenge:	Efficiency of learning of BERT is very slow due to hidden dataset bias . however, some studies show that it can learn with surface clues/patterns .
Approach:	They propose to use a simple entailment judgment case to test whether BERT can learn without hidden dataset bias.
Outcome:	The proposed case shows that BERT can learn without hidden bias without utilizing dataset bias.

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Nianyi Lin, Jiajie Zhang, Lei Hou, Juanzi Li

CrowdSelect: SyntheticInstruction Data Selection with Multi-LLM Wisdom (2026.findings-eacl)

Challenge:	Existing methods for reinforcement learning (RL) require a large sample size to be implemented.
Approach:	They propose a memory-efficient RL algorithm that maximizes a lower bound of the ELBO-based objective.
Outcome:	Experiments show that BGPO outperforms previous RL algorithms for diffusion large language models in math problem solving, code generation, and planning tasks.

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Yisen Li, Lingfeng Yang, Wenxuan Shen, Pan Zhou, Yao Wan, Weiwei Lin, Dongping Chen

Unsupervised Single Document Abstractive Summarization using Semantic Units (2022.aacl-main)

Challenge:	Existing methods for capturing instruction-following complexity rely on single-dimensional signals, but they fail to capture complexity across diverse fields.
Approach:	They propose three foundational metrics that leverage Multi-LLMs wisdom to capture instruction-response pair characteristics and propose CrowdSelect, an integrated metric incorporating a clustering-based approach to maintain response diversity.
Outcome:	The proposed metrics outperform existing models on MT-bench and Arena-hard and show improvements of 4.81% on full and LoRA fine-tuning.

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Jhen-Yi Wu, Ying-Jia Lin, Hung-Yu Kao

RAAMove: A Corpus for Analyzing Moves in Research Article Abstracts (2024.lrec-main)

Challenge:	a lack of sufficient training pairs is a common issue in real-world applications.
Approach:	They propose a framework that lets a model learn the frequency of each semantic unit in the source text.
Outcome:	The proposed model outperforms other unsupervised methods under CNN/Daily Mail task.

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Hongzheng Li, Ruojin Wang, Ge Shi, Xing Lv, Lei Lei, Chong Feng, Fang Liu, Jinkun Lin, Yangguang Mei, Linnan Xu

SrDetection: A Self-Referential Framework for Data Leakage Detection in Code Large Language Models (2026.findings-acl)

Challenge:	RAAMove is a comprehensive multi-domain corpus dedicated to the annotation of move structures in Research Article (RA) abstracts.
Approach:	They propose a multi-domain corpus dedicated to the annotation of move structures in RA abstracts.
Outcome:	The proposed corpus is based on a human-annotated dataset and a BERT-based model to verify its effectiveness.

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Shuaimin Li, Liyang Fan, Zeyang li, Zhuoyue Wan, Yufang Lin, Shiwen Ni, Feiteng Fang, Hamid Alinejad-Rokny, Yuanfeng Song, Kun Jing, Chen Jason Zhang, Min Yang

Gradient-Guided Multi-Judge Prompt Optimization (2026.acl-long)

Challenge:	Existing methods for evaluating code large language models assume access to proprietary training corpora or use external reference sets with manually tuned, non-generalizable thresholds.
Approach:	They propose a framework for self-referential leakage detection for gray-box and black-box settings.
Outcome:	The proposed framework improves average F1 by 21.52 points in the gray-box setting and 14.46 points in black-box settings over strong baselines.

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ChenZhuo Zhao, Xinda Wang, Pu Zhao, Yue Huang, Junting Lu, Ziqian Liu, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang

Limitations of Autoregressive Models and Their Alternatives (2021.naacl-main)

Challenge:	Existing approaches to prompt optimization trade off signal quality against computational cost.
Approach:	They propose a framework that uses a first-order gradient approximation to score segment importance in a continuous masking direction.
Outcome:	The proposed framework improves efficiency and robustness by using a first-order gradient approximation to score segment importance in a continuous masking direction.

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Chu-Cheng Lin, Aaron Jaech, Xin Li, Matthew R. Gormley, Jason Eisner

Drivel-ology: Challenging LLMs with Interpreting Nonsense with Depth (2025.emnlp-main)

Challenge:	Standard autoregressive language models only perform polynomial-time computation to compute probability of next symbol.
Approach:	authors propose alternative to standard autoregressive language models that use polynomial-time computation to compute probability of next symbol.
Outcome:	a large model size can grow superpolynomially in length, allowing it to store precomputed results and verify solutions.

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Yang Wang, Chenghao Xiao, Chia-Yi Hsiao, Zi Yan Chang, Chi-Li Chen, Tyler Loakman, Chenghua Lin

DoTAT: A Domain-oriented Text Annotation Tool (2022.acl-demo)

Challenge:	Despite excelling at many natural language processing tasks, large language models fail to grasp the layered semantics of Drivelological text.
Approach:	They construct a benchmark dataset of over 1,200+ carefully curated and diverse examples across English, Mandarin, Spanish, French, Japanese, and Korean to examine their Drivelological characteristics.
Outcome:	The proposed models lack conceptual understanding and lack conceptual and semantic accuracy.

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Yupian Lin, Tong Ruan, Ming Liang, Tingting Cai, Wen Du, Yi Wang

Challenge:	DoTAT is a domain-oriented text annotation tool that can reduce the time for event annotation by 19.7% . the tool supports multi-person collaborative process with automatically merging and review .
Approach:	They propose a domain-oriented text annotation tool called DoTAT . it provides multi-person collaborative process with automatic merging and review .
Outcome:	The proposed tool can reduce the time for event annotation by 19.7% compared with existing tools.

Honey or Poison? Solving the Trigger Curse in Few-shot Event Detection via Causal Intervention (2021.emnlp-main)

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Jiawei Chen, Hongyu Lin, Xianpei Han, Le Sun

RSA-Bench: Benchmarking Audio Large Models in Real-World Acoustic Scenarios (2026.findings-acl)

Challenge:	Recent supervised ED approaches have achieved promising performance but require large number of manually annotated event data.
Approach:	They propose to overfit the trigger confounder of the context and the result . they propose to intervene on the context via backdoor adjustment during training .
Outcome:	The proposed method significantly improves the FSED on ACE05 and MAVEN datasets.

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Yibo Zhang, Kaiwen Luo, Liang Lin, Shilinlu Yan, Jin Wang, Yaoqi Guo, Yitian Chen, Yalan Qin, Zhenhong Zhou, Kun Wang, Li Sun

Minos: A Multimodal Evaluation Model for Bidirectional Generation Between Image and Text (2026.findings-acl)

Challenge:	Existing evaluations rely on synthetic Gaussian noise or simplistic single-source interference, failing to capture the intricate, multi-layered acoustic dynamics that characterize authentic physical environments.
Approach:	They propose a robustness benchmark to stress-test Audio Large Models (ALLMs) using high-fidelity auditory scene simulations.
Outcome:	The proposed model performs well on a wide range of tasks, including automatic speech recognition, speech translation, and audio-based reasoning.

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Junzhe Zhang, Huixuan Zhang, Xinyu Hu, Li Lin, Mingqi Gao, Shi Qiu, Xiaojun Wan

MobileNMT: Enabling Translation in 15MB and 30ms (2023.acl-industry)

Challenge:	Existing evaluation models struggle to achieve consistent performance across image-to-text (I2T) and text-to image (T2I) tasks.
Approach:	They construct a multimodal evaluation model using a large multimodal dataset and rigorous quality control strategies to train it.
Outcome:	The proposed model achieves state-of-the-art evaluation performance across 16 out-of domain datasets covering both I2T and T2I tasks among all open-source multimodal evaluation models and remain competitive with closed-source models.

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Ye Lin, Xiaohui Wang, Zhexi Zhang, Mingxuan Wang, Tong Xiao, Jingbo Zhu

DIRAS: Efficient LLM Annotation of Document Relevance for Retrieval Augmented Generation (2025.naacl-long)

Challenge:	Existing work on NMT models is limited in storage, memory, computation and power consumption.
Approach:	They propose a mobile machine translation system that can translate in 15MB and 30ms on devices.
Outcome:	The proposed system can translate in 15MB and 30ms on mobile devices.

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Jingwei Ni, Tobias Schimanski, Meihong Lin, Mrinmaya Sachan, Elliott Ash, Markus Leippold

LongFaith: Enhancing Long-Context Reasoning in LLMs with Faithful Synthetic Data (2025.findings-acl)

Challenge:	RAG systems leave out important relevant information (low recall) and excessively related but irrelevant information (high precision) authors propose a manual annotation-free schema that can be used for RAGs with limited performance.
Approach:	They propose a manual annotation-free schema that annotates unseen queries with calibrated relevance scores.
Outcome:	Evaluators show that DIRAS can achieve GPT-4-level performance on annotating and ranking unseen (query, document) pairs.

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Cehao Yang, Xueyuan Lin, Chengjin Xu, Xuhui Jiang, Shengjie Ma, Aofan Liu, Hui Xiong, Jian Guo

Generalized Embedding Models for Industry 4.0 Applications (2025.emnlp-industry)

Challenge:	Long-context processing ability has emerged as a significant challenge for large language models.
Approach:	They propose a pipeline for synthesizing faithful long-context reasoning instruction datasets . they integrate ground truth and citation-based reasoning prompts integrating them .
Outcome:	The proposed pipeline eliminates distractions and improves reasoning chains.

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Christodoulos Constantinides, Shuxin Lin, Dhaval C Patel

Operator Selection and Ordering in a Pipeline Approach to Efficiency Optimizations for Transformers (2023.findings-acl)

Challenge:	Using Large Language Models (LLMs) to automate tasks has emerged as the next frontier of innovation.
Approach:	They propose a model that generalizes to queries involving similar assets and retrieves relevant items from natural language tasks.
Outcome:	The proposed model can be used to generalize to queries involving similar assets, such as identifying sensors relevant to an asset’s failure mode.

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Ji Xin, Raphael Tang, Zhiying Jiang, Yaoliang Yu, Jimmy Lin

Investigating Bias in LLM-Based Bias Detection: Disparities between LLMs and Human Perception (2025.coling-main)

Challenge:	Natural language processing tasks rely on complex neural models . transformer-based models are typically slow to execute, making it a non-trivial challenge to apply them in real-world applications.
Approach:	They propose to consider an efficiency method as an operator applied on a model . they find that the commutativity and cumulativeness of efficiency operators are plausible .
Outcome:	The proposed method is commutative and cumulative, and the results are estimated by combining methods.

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Luyang Lin, Lingzhi Wang, Jinsong Guo, Kam-Fai Wong

Transfer Learning for Context-Aware Question Matching in Information-seeking Conversations in E-commerce (P18-2)

Challenge:	Detecting media bias is critical due to the spread of misinformation and disinformation on social media platforms.
Approach:	They investigate the presence and nature of bias within large language models and its consequential impact on media bias detection.
Outcome:	The proposed debiasing strategies include prompt engineering and model fine-tuning.

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Minghui Qiu, Liu Yang, Feng Ji, Wei Zhou, Jun Huang, Haiqing Chen, Bruce Croft, Wei Lin

WSDMS: Debunk Fake News via Weakly Supervised Detection of Misinforming Sentences with Contextualized Social Wisdom (2023.emnlp-main)

Challenge:	Recent researches focus on deep learning and reinforcement learning for multi-turn information seeking conversation systems.
Approach:	They propose an efficient and effective multi-turn conversation model based on convolutional neural networks and extend it to adapt the knowledge learned from a resource-rich domain to enhance the performance.
Outcome:	The proposed model performs better than the existing model on an industrial chatbot called AliMe Assist.

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Ruichao Yang, Wei Gao, Jing Ma, Hongzhan Lin, Zhiwei Yang

CELI: Simple yet Effective Approach to Enhance Out-of-Domain Generalization of Cross-Encoders. (2024.naacl-short)

Challenge:	Existing methods for debunking fake news rely on blending of authentic and fabricated content by creators.
Approach:	They propose a model that detects misinformation at sentence-level using social media conversations . they use a bag-level annotation system to train the model .
Outcome:	The proposed model outperforms existing state-of-the-art models on three real-world benchmarks and outperformed existing state of the art models in debunking fake news at sentence and article levels.

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Crystina Zhang, Minghan Li, Jimmy Lin

Complex Reasoning in Natural Language (2023.acl-tutorials)

Challenge:	Existing cross-encoders do not capture all information into the [CLS] token . Xiong et al., 2021) find that the out-of-domain approach is less robust.
Approach:	They introduce a cross-encoder with late interaction that incorporates a late interaction layer into existing models.
Outcome:	The proposed method improves BEIR by 5% without compromising in-domain effectiveness or search latency.

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Wenting Zhao, Mor Geva, Bill Yuchen Lin, Michihiro Yasunaga, Aman Madaan, Tao Yu

Integrating Structural Semantic Knowledge for Enhanced Information Extraction Pre-training (2024.emnlp-main)

Challenge:	Recent research shows that pretrained language models are often brittle for complex reasoning tasks.
Approach:	They propose to use pre-trained language models to teach machines to reason over texts . they will review recent promising approaches to tackling complex reasoning tasks .
Outcome:	This tutorial reviews promising approaches to complex reasoning tasks . it reviews the methods that can be used to augment models with robustness .

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Xiaoyang Yi, Yuru Bao, Jian Zhang, Yifang Qin, Faxin Lin

Uncertainty Aware Learning for Language Model Alignment (2024.acl-long)

Challenge:	Existing pre-training methods focus on exploiting textual knowledge, which limits scalability and versatility of resulting models.
Approach:	They propose a pre-training framework that integrates structural semantic knowledge via contrastive learning.
Outcome:	The proposed framework outperforms state-of-the-art pre-training methods across multiple tasks.

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Yikun Wang, Rui Zheng, Liang Ding, Qi Zhang, Dahua Lin, Dacheng Tao

Mega-COV: A Billion-Scale Dataset of 100+ Languages for COVID-19 (2021.eacl-main)

Challenge:	Existing alignment strategies that focus on diverse and high-quality data often overlook the intrinsic uncertainty of tasks, learning all data samples equally.
Approach:	They propose to introduce the sample uncertainty into the alignment of different task scenarios by a simple fashion by setting the label smoothing value of training according to the uncertainty of individual samples.
Outcome:	The proposed model outperforms standard supervised fine-tuning on high-entropy tasks and complex low-entropic tasks.

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Muhammad Abdul-Mageed, AbdelRahim Elmadany, El Moatez Billah Nagoudi, Dinesh Pabbi, Kunal Verma, Rannie Lin

Challenge:	a global pandemic of coronavirus disease 2019 has impacted millions of people . a human annotation study reveals the utility of our models on a subset of Mega-COV .
Approach:	They develop powerful models to analyze tweets related to the pandemic . they use a multilingual Twitter dataset with geo-location information .
Outcome:	The proposed model can identify whether a tweet is related to the pandemic and detect misinformation about it.

Glyph2Vec: Learning Chinese Out-of-Vocabulary Word Embedding from Glyphs (2020.acl-main)

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Hong-You Chen, Sz-Han Yu, Shou-de Lin

HammerBench: Fine-Grained Function-Calling Evaluation in Real Mobile Assistant Scenarios (2025.findings-acl)

Challenge:	Chinese NLP applications that rely on large text often contain huge amounts of vocabulary which are sparse in corpus.
Approach:	They propose a multi-modal model that extracts visual features from Chinese word glyphs to expand current word embedding space without accessing any corpus.
Outcome:	The proposed model can embed words in Chinese without accessing corpus without a corpus.

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Jun Wang, Jiamu Zhou, Xihuai Wang, Xiaoyun Mo, Haoyu Zhang, Qiqiang Lin, Jincheng Jincheng, Muning Wen, Weinan Zhang, Qiuying Peng, Jun Wang

AdapTime: Enabling Adaptive Temporal Reasoning in Large Language Models (2026.findings-acl)

Challenge:	Evaluating the performance of LLMs in multi-turn interactions presents significant challenges due to the complexity and variability of user behavior.
Approach:	They propose a benchmark framework for assessing LLMs’ function-calling capabilities in multi-turn dialogues.
Outcome:	The proposed framework is based on a dataset derived from popular mobile apps and anonymized user logs.

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Yimin Deng, Yejing Wang, Zhenxi Lin, Zichuan Fu, Guoshuai Zhao, Derong Xu, Yefeng Zheng, Xiangyu Zhao, Xian Wu, Li Zhu, Xueming Qian

A Boundary Offset Prediction Network for Named Entity Recognition (2023.findings-emnlp)

Challenge:	Existing methods for temporal reasoning are limited and apply a fixed pipeline to all questions.
Approach:	They propose an adaptive temporal reasoning method that dynamically executes reasoning steps based on context and task requirements.
Outcome:	Experiments on two temporal QA benchmarks show the proposed method works.

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Minghao Tang, Yongquan He, Yongxiu Xu, Hongbo Xu, Wenyuan Zhang, Yang Lin

Retentive or Forgetful? Diving into the Knowledge Memorizing Mechanism of Language Models (2024.lrec-main)

Challenge:	Named entity recognition (NER) is a fundamental task in natural language processing . span-based methods assign entity types to text spans, resulting in imbalanced sample space .
Approach:	They propose a method that predicts boundary offsets between candidate and nearest spans . the method integrates entity type and span representations to generate type-aware boundary offset .
Outcome:	The proposed method outperforms existing methods on eight widely-used NER datasets.

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Boxi Cao, Qiaoyu Tang, Hongyu Lin, Shanshan Jiang, Bin Dong, Xianpei Han, Jiawei Chen, Tianshu Wang, Le Sun

DDPrompt: Differential Diversity Prompting in Large Language Models (2024.acl-short)

Challenge:	Pre-trained language models have shown remarkable memory formation, but vanilla networks without pre-training suffer catastrophic forgetting problem.
Approach:	They conduct experiments to investigate the retentive-forgetful contradiction between vanilla and pre-trained language models by controlling the target knowledge types, learning strategies and learning schedules.
Outcome:	The results show that pre-trained language models are forgetful and pre-training leads to retentive models .

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Lin Mu, Wenhao Zhang, Yiwen Zhang, Peiquan Jin

Mining Cross-Cultural Differences and Similarities in Social Media (P18-1)

Challenge:	Large Language Models (LLMs) have shown that their reasoning ability can be enhanced through approaches like Chain-of-Thought (CoT) prompting.
Approach:	They propose a method that generates differentially diverse reasoning paths for different types of questions by voting on the optimal prompts.
Outcome:	The proposed method improves LLMs' reasoning ability on complex reasoning tasks by learning from demonstrations while keeping their parameters frozen.

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Bill Yuchen Lin, Frank F. Xu, Kenny Zhu, Seung-won Hwang

Zero-shot Generative Linguistic Steganography (2024.naacl-long)

Challenge:	a new paper examines the problem of computing cross-cultural differences and similarities in natural language understanding . cross-culture differences are important for cross-lingual research, especially in social media .
Approach:	They propose a framework for computing cross-cultural differences and similarities from social media . they propose to use a social media platform to find similar terms for slang across languages .
Outcome:	The proposed framework outperforms baseline methods on two novel tasks.

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Ke Lin, Yiyang Luo, Zijian Zhang, Luo Ping

Assessing Dialect Fairness and Robustness of Large Language Models in Reasoning Tasks (2025.acl-long)

Challenge:	Generative linguistic steganography attempts to hide secret messages into covertext . previous studies focused on the statistical differences between the covertext and stegotext - however, ill-formed stegotas can readily be identified by humans .
Approach:	They propose a zero-shot approach based on in-context learning for linguistic steganography to achieve better perceptual and statistical imperceptibility.
Outcome:	The proposed method produces 1.926 more innocent and intelligible stegotext than any other method.

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Fangru Lin, Shaoguang Mao, Emanuele La Malfa, Valentin Hofmann, Adrian de Wynter, Xun Wang, Si-Qing Chen, Michael J. Wooldridge, Janet B. Pierrehumbert, Furu Wei

No, you’re not alone: A better way to find people with similar experiences on Reddit (D19-55)

Challenge:	a study aims to assess the fairness and robustness of Large Language Models in dialectal queries . speakers of "non-standard" dialects are known to experience implicit and explicit discrimination .
Approach:	They propose to use a benchmark to assess the fairness of large language models in dialects . they hire speakers with computer science backgrounds to rewrite seven popular benchmarks based on AAVE .
Outcome:	The proposed benchmarks show that most models show significant brittleness and unfairness to queries in AAVE.

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Zhilin Wang, Elena Rastorgueva, Weizhe Lin, Xiaodong Wu

Text Clustering

Challenge:	a probabilistic clustering algorithm can help users find posts that discuss experiences similar to their own . a recent study shows that probabilistic Clustering can yield a better performance than baseline clustering methods .
Approach:	They propose a probabilistic clustering algorithm that can help Reddit users find posts that discuss experiences similar to their own.
Outcome:	The proposed algorithm can find posts that discuss experiences similar to their own . it performs better than baseline clustering methods due to high runtime overhead .

WordGame: Efficient & Effective LLM Jailbreak via Simultaneous Obfuscation in Query and Response (2025.findings-naacl)

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Tianrong Zhang, Bochuan Cao, Yuanpu Cao, Lu Lin, Prasenjit Mitra, Jinghui Chen

ToolPRM: Fine-Grained Inference Scaling of Structured Outputs for Function Calling (2026.acl-long)

Challenge:	Recent advances in large language models have raised concerns about their susceptibility to jailbreaking attacks, which leads to harmful content inadvertently.
Approach:	They propose to exploit the safety alignment patterns of LLMs by simultaneous obfuscation in queries and responses to break down adversarial intent of query.
Outcome:	The proposed attack breaks down adversarial intent of query and encourages benign content regarding the games to precede anticipated harmful content in the response.

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Jianghao Lin, Yuanyuan Shi, Xin Peng, Renjie Ding, Hairui Wang, Yuxuan Peng, Bizhe Bai, Weixi Song, Fengshuo Bai, Huacan Chai, Weinan Zhang, Fei Huang, Ying Wen

Challenge:	Existing research on inference scaling focuses on unstructured output generation tasks, such as mathematical problems.
Approach:	They propose an inference-scaling framework that combines fine-grained beam search with ToolPRM, a process reward model scoring each intra-call decision.
Outcome:	The proposed framework outperforms outcome and coarse-grained reward models in predictive accuracy and yields consistent test-time gains on multiple function-calling benchmarks.

Visually-Guided Policy Optimization for Multimodal Reasoning (2026.acl-long)

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Zengbin Wang, Feng Xiong, Liang Lin, Xuecai Hu, Yong Wang, Yanlin Wang, Man Zhang, Xiangxiang Chu

Telling the Whole Story: A Manually Annotated Chinese Dataset for the Analysis of Humor in Jokes (D19-1)

Challenge:	Existing RLVRs lack visual faithfulness due to text-dominated reasoning . a novel framework to reinforce visual focus during policy optimization is proposed .
Approach:	They propose a framework to reinforce visual focus during policy optimization using visual attention compensation mechanism.
Outcome:	The proposed framework exhibits better visual activation and superior performance in multimodal reasoning and visual-dependent tasks.

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Dongyu Zhang, Heting Zhang, Xikai Liu, Hongfei Lin, Feng Xia

A Semantic Mention Graph Augmented Model for Document-Level Event Argument Extraction (2024.lrec-main)

Challenge:	Humor plays important role in human communication, which makes it important problem for natural language processing.
Approach:	They propose a novel annotation scheme to give scenarios of how humor arises in text . they report reasonable agreement between annotators and analyze the dataset .
Outcome:	The proposed scheme gives scenarios of how humor arises in text . it contains key words that trigger humor, character relationship, scene, and humor categories .

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Jian Zhang, Changlin Yang, Haiping Zhu, Qika Lin, Fangzhi Xu, Jun Liu

TriggerNER: Learning with Entity Triggers as Explanations for Named Entity Recognition (2020.acl-main)

Challenge:	Document-level Event Argument Extraction (DEAE) aims to identify arguments and their specific roles from unstructured document.
Approach:	They propose a document-prompt-based method for document-level event argument extraction that uses a semantic mention graph to capture relations between documents and prompts.
Outcome:	The proposed method surpasses baseline methods and achieves state-of-the-art performance on RAMS and WikiEvents datasets.

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Bill Yuchen Lin, Dong-Ho Lee, Ming Shen, Ryan Moreno, Xiao Huang, Prashant Shiralkar, Xiang Ren

SERM: Self-Evolving Relevance Model with Agent-Driven Learning from Massive Query Streams (2026.findings-acl)

Challenge:	Named entity recognition (NER) is a fundamental information extraction task that focuses on extracting entities from a given text and classifying them using pre-defined categories.
Approach:	They propose to use “entity triggers” to facilitate label-efficient learning of NER models.
Outcome:	The proposed model is significantly more cost-effective than the traditional neural NER frameworks.

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Chenglong Wang, Canjia Li, Xingzhao Zhu, Yifu Huo, Huiyu Wang, Weixiong Lin, Yun Yang, Qiaozhi He, Tian Hua Zhou, null Changxiaojia, JingBo Zhu, Tong Xiao

Challenge:	Existing approaches to generate relevance judgments are limited due to dynamic nature of query distributions.
Approach:	They propose a self-evolving relevance model approach to generalize queries to practical search scenarios . they use a multi-agent sample miner and a relevance annotator to generate reliable labels .
Outcome:	The proposed approach can achieve significant performance gains on a large-scale industrial platform, validated by offline multilingual evaluations and online testing.

Pretrained Transformers for Text Ranking: BERT and Beyond (2021.naacl-tutorials)

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Andrew Yates, Rodrigo Nogueira, Jimmy Lin

CogEvolve: A Multimodal Benchmark for Evaluating Relational Reasoning in Semantic Extension (2026.acl-long)

Challenge:	This tutorial provides an overview of text ranking using neural network architectures known as transformers.
Approach:	This tutorial provides an overview of text ranking with neural network architectures known as transformers.
Outcome:	This tutorial provides an overview of text ranking with neural network architectures known as transformers.

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Jingjie Zeng, Huayang Li, Liang Yang, Yuanyuan Sun, Shaowu Zhang, Hongfei Lin

Mitigate Position Bias in LLMs via Scaling a Single Hidden States Channel (2025.findings-acl)

Challenge:	a gap exists between human embodied logic and machine statistical learning . authors: models internalize statistical patterns or mimic static recognition .
Approach:	They propose a cognitive linguistic benchmark to test whether large language models internalize statistical logic or not . they find that models function as "Super-Associators" expert at static recognition yet fail at causal reasoning .
Outcome:	The proposed model fails at causal reasoning and has a high-fidelity concept representation but lacks transformational operators essential for true relational understanding.

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Yijiong Yu, Huiqiang Jiang, Xufang Luo, Qianhui Wu, Chin-Yew Lin, Dongsheng Li, Yuqing Yang, Yongfeng Huang, Lili Qiu

Challenge:	Long-context language models exhibit position bias, also known as "lost in the middle" research shows that even long-contemporary LLMs fail to utilize all context information effectively .
Approach:	They propose a method to mitigate position bias by scaling positional hidden states . they propose to use a channel of hidden states to modify positional Hidden states a LCLM's positional bias .
Outcome:	The proposed method can improve performance by 15.2% in a "lost in the middle" benchmark.

Nemotron-CrossThink: Scaling Self-Learning beyond Math Reasoning (2026.eacl-long)

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Syeda Nahida Akter, Shrimai Prabhumoye, Matvei Novikov, Seungju Han, Ying Lin, Evelina Bakhturina, Eric Nyberg, Yejin Choi, Mostofa Patwary, Mohammad Shoeybi, Bryan Catanzaro

LongWanjuan: Towards Systematic Measurement for Long Text Quality (2024.findings-emnlp)

Challenge:	Prior work has successfully applied Reinforcement Learning (RL) to mathematical reasoning, but generalization to broader domains remains challenging due to limited data and lack of verifiable rewards for unstructured domains.
Approach:	They propose a framework that integrates multi-domain corpora into RL training to improve generalization across diverse reasoning tasks.
Outcome:	The proposed framework improves generalization across diverse reasoning tasks.

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Xiaoran Liu, Kai Lv, Qipeng Guo, Hang Yan, Conghui He, Xipeng Qiu, Dahua Lin

Not All Contexts Are Equal: Teaching LLMs Credibility-aware Generation (2024.emnlp-main)

Challenge:	Existing efforts to improve data quality have focused on deduplication and the evaluation of data diversity and difficulty.
Approach:	They propose a set of metrics to evaluate the quality of long texts by evaluating three fundamental linguistic dimensions: coherence, cohesion, and complexity.
Outcome:	The proposed model improves on long-text tasks with over 160B tokens and categorizes long texts into holistic, aggregated, and chaotic types.

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Ruotong Pan, Boxi Cao, Hongyu Lin, Xianpei Han, Jia Zheng, Sirui Wang, Xunliang Cai, Le Sun

Helios: A Foundational Language Model for Smart Energy Knowledge Reasoning and Application (2026.eacl-long)

Challenge:	Existing RAG paradigms suffer from the impact of flawed information introduced during the retrieval phrase, thereby diminishing the reliability and correctness of the generated output.
Approach:	They propose a framework that empowers models to discern and process information based on its credibility.
Outcome:	The proposed framework outperforms existing models with retrieval augmentation and exhibits robustness despite increasing noise in the context.

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Haoyu Jiang, Fanjie Zeng, Boan Qu, Xiaojie Lin, Wei Zhong

Gradient Imitation Reinforcement Learning for Low Resource Relation Extraction (2021.emnlp-main)

Challenge:	Enersys is a collaborative framework for end-to-end dataset construction that combines a large-scale pretraining, SFT, and RLHF datasets to improve performance.
Approach:	They propose a large language model tailored to the smart energy domain and a collaborative framework to advance LLM research in this field.
Outcome:	The proposed model improves domain knowledge mastery, task execution accuracy, and alignment with human preferences.

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Xuming Hu, Chenwei Zhang, Yawen Yang, Xiaohe Li, Li Lin, Lijie Wen, Philip S. Yu

GenRES: Rethinking Evaluation for Generative Relation Extraction in the Era of Large Language Models (2024.naacl-long)

Challenge:	Existing methods to extract relation facts from limited labeled corpora are laborintensive to obtain . Existing approaches use self-training to generate pseudo labels that will cause gradual drift problem or leverage meta-learning scheme which does not solicit feedback explicitly.
Approach:	They propose a Gradient Imitation Reinforcement Learning method to encourage pseudo label data to imitate gradient descent direction on labeled data and bootstrap its optimization capability through trial and error.
Outcome:	The proposed method handles two major scenarios in low-resource relation extraction when no unlabeled data is available.

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Pengcheng Jiang, Jiacheng Lin, Zifeng Wang, Jimeng Sun, Jiawei Han

Evaluating Saliency Explanations in NLP by Crowdsourcing (2024.lrec-main)

Challenge:	Existing relation extraction methods rely on exact matching with human-annotated reference relations, while GRE methods produce diverse and semantically accurate relations.
Approach:	They propose a multi-dimensional assessment of relation extraction methods using human-annotated reference relations.
Outcome:	The proposed method is consistent with human preferences for RE quality.

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Xiaotian Lu, Jiyi Li, Zhen Wan, Xiaofeng Lin, Koh Takeuchi, Hisashi Kashima

LongAttn: Selecting Long-context Training Data via Token-level Attention (2025.findings-acl)

Challenge:	a crowdsourced method to evaluate saliency methods in NLP is proposed . saliencies are difficult for humans to understand, and can cause psychological harm .
Approach:	They propose a method to evaluate saliency methods in NLP by crowdsourcing . they recruited 800 crowd workers and empirically evaluated seven salience methods .
Outcome:	The proposed method evaluates saliency methods on two datasets using crowdsourced data . it shows that the results are comparable to existing methods on NLP and CV fields .

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Longyun Wu, Dawei Zhu, Guangxiang Zhao, Zhuocheng Yu, Junfeng Ran, Xiangyu Wong, Lin Sun, Sujian Li

IntactKV: Improving Large Language Model Quantization by Keeping Pivot Tokens Intact (2024.findings-acl)

Challenge:	Existing methods to select long-context data often rely on sentence-level analysis, which can be greatly optimized in both performance and efficiency.
Approach:	They propose a token-level framework which quantifies long-range dependencies for LLMs by calculating token-based dependency strength and distribution uniformity of token scores.
Outcome:	The proposed framework quantifies long-range dependencies, enabling more accurate and efficient data selection.

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Ruikang Liu, Haoli Bai, Haokun Lin, Yuening Li, Han Gao, Zhengzhuo Xu, Lu Hou, Jun Yao, Chun Yuan

Stochastic Bridges as Effective Regularizers for Parameter-Efficient Tuning (2023.findings-acl)

Challenge:	Existing quantization methods are compromising performance of large language models (LLMs) despite their high computational intensity, LLMs are still demanding intensive computation.
Approach:	They propose to generate the KV cache of pivot tokens losslessly from the full-precision model.
Outcome:	The proposed method generates the KV cache of pivot tokens losslessly from the full-precision model with no extra inference overhead.

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Weize Chen, Xu Han, Yankai Lin, Zhiyuan Liu, Maosong Sun, Jie Zhou

NovAScore: A New Automated Metric for Evaluating Document Level Novelty (2025.coling-main)

Challenge:	Existing methods for tuning pre-trained language models ignore the running cost and only optimize the terminal cost.
Approach:	They propose to use stochastic bridges to regularize intermediate states and use regularization as running cost of PETs.
Outcome:	The proposed methods can be used to tune large pre-trained language models . they can be compared to full-parameter fine-tuning by tuning a small number of parameters .

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Lin Ai, Ziwei Gong, Harshsaiprasad Deshpande, Alexander Johnson, Emmy Phung, Ahmad Emami, Julia Hirschberg

Challenge:	Recent research has focused on identifying text that introduces new, previously unknown information, but has seen a decline in novelty detection due to the rise of large language models.
Approach:	They propose a novel automated metric for evaluating document-level novelty that aggregates the novelty and salience scores of atomic information and provides high interpretability and a detailed analysis of a document's novelty.
Outcome:	The proposed metric scores high on the TAP-DLND 1.0 dataset and a human-annotated dataset.

Harnessing Consistency for Robust Test-Time LLM Ensemble (2026.findings-eacl)

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Zhichen Zeng, Qi Yu, Xiao Lin, Ruizhong Qiu, Xuying Ning, Tianxin Wei, Yuchen Yan, Jingrui He, Hanghang Tong

TCP: a Benchmark for Temporal Constraint-Based Planning (2025.emnlp-main)

Challenge:	Existing efforts to improve LLM ensemble quality have focused on model consistency, but failures are often due to heterogeneous tokenization schemes and varying model expertise.
Approach:	They propose a plug-and-play technique that harnesses model consistency for robust LLM ensemble.
Outcome:	The proposed technique improves ensemble performance and robustness against erroneous signals.

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Zifeng Ding, Sikuan Yan, Moy Yuan, Xianglong Hu, Fangru Lin, Andreas Vlachos

A Timestep aware Sentence Embedding and Acme Coverage for Brief but Informative Title Generation (2022.findings-naacl)

Challenge:	Existing benchmarks evaluate temporal reasoning and planning in isolation and under limited forms of complexity.
Approach:	They propose a temporal constraint-based planning benchmark that assesses temporal reasoning and planning capabilities in large language models.
Outcome:	The proposed model fails to perform well under limited constraints and lacks temporal grounding.

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Quanbin Wang, XieXiong Lin, Feng Wang

Interpretable Short Video Rumor Detection Based on Modality Tampering (2024.lrec-main)

Challenge:	Existing methods for title generation are based on timestep aware sentence embeddings, but they are not effective for generating a title with appropriate information in the content.
Approach:	They propose a Timestep aware Sentence Embedding mechanism which refreshes the sentences’ embeddings with corresponding key words in different decoding timesteps.
Outcome:	The proposed framework outperforms existing methods on various title generation tasks and the evaluation scores are significantly higher than previous approaches.

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Kaixuan Wu, Yanghao Lin, Donglin Cao, Dazhen Lin

A Diffusion Model for Event Skeleton Generation (2023.findings-acl)

Challenge:	Existing methods to detect rumors from the perspective of modality tampering are labor-intensive and time-consuming.
Approach:	They propose a short video rumor detection framework that integrates modality tampering detection and inter-modal matching into a model to detect modality-tampers and interpretability mechanisms to make the results more reasonable.
Outcome:	The proposed model improves on the short video rumor dataset by 4.6%-12% compared with other models and can explain whether the short clip is a rumour or not through the perspective of modality tampering.

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Fangqi Zhu, Lin Zhang, Jun Gao, Bing Qin, Ruifeng Xu, Haiqin Yang

Word Embedding and WordNet Based Metaphor Identification and Interpretation (P18-1)

Challenge:	Existing methods for event schema generation are noise-sensitive and error-accumulating, e.g., inability to correct errors while generating schema.
Approach:	They propose a novel diffusion event graph model that embeds and roundes event graphs into learnable latent representations and a denoising process to maintain the model's robustness.
Outcome:	The proposed model achieves better results than existing state-of-the-art models on three IED bombing datasets.

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Rui Mao, Chenghua Lin, Frank Guerin

Visual Information Guided Zero-Shot Paraphrase Generation (2022.coling-1)

Challenge:	Existing models cannot identify exact metaphorical words within a sentence . current models do not rely on hand-crafted knowledge for training .
Approach:	They propose an unsupervised learning method that identifies and interprets metaphors at word-level without preprocessing.
Outcome:	The proposed method outperforms baseline models in two translation systems for English to Chinese showing that it paraphrases metaphors into their literal counterparts.

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Zhe Lin, Xiaojun Wan

SnapNTell: Enhancing Entity-Centric Visual Question Answering with Retrieval Augmented Multimodal LLM (2024.findings-emnlp)

Challenge:	Several studies use different information as ”pivot” such as language, semantic representation and so on.
Approach:	They propose to use visual information as the "pivot" of back-translation to generate paraphrases using paired image-caption data.
Outcome:	The proposed model generates paraphrase with good relevancy, fluency and diversity . it is based on paired image-caption data and can train a paraphrasing model .

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Jielin Qiu, Andrea Madotto, Zhaojiang Lin, Paul Crook, Yifan Xu, Babak Damavandi, Xin Dong, Christos Faloutsos, Lei Li, Seungwhan Moon

Joint Knowledge Base Completion and Question Answering by Combining Large Language Models and Small Language Models (2026.acl-long)

Challenge:	Vision-extended LLMs have made significant strides in VQA, but they still encounter significant difficulties in handling queries involving long-tail entities.
Approach:	They propose a benchmark to test models' ability to identify entities and provide detailed, entity-specific knowledge by combining 10 images and 10 knowledge-intensive QA pairs.
Outcome:	The proposed model outperforms existing methods on the SnapNTell dataset, achieving a 66.5% improvement in the BELURT score.

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Yinan Liu, Dongying Lin, Sigang Luo, Xiaochun Yang, Bin Wang

Counterfactual Debating with Preset Stances for Hallucination Elimination of LLMs (2025.coling-main)

Challenge:	Existing studies rely on the small language model (SLM) to enhance them jointly, and the large language model’s strong reasoning ability is ignored.
Approach:	They propose a framework which can make knowledge base completion and knowledge base question answering enhance each other in an iterative manner by combining the strengths of the small language model and the large language model.
Outcome:	The proposed framework surpasses baselines for both KBC and KBQA tasks over two public benchmark data sets.

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Yi Fang, Moxin Li, Wenjie Wang, Lin Hui, Fuli Feng

A Grounded Unsupervised Universal Part-of-Speech Tagger for Low-Resource Languages (N19-1)

Challenge:	Existing solutions to alleviate hallucination have considered utilizing LLMs’ inherent reasoning abilities to alleviating hallucinism, such as self-correction and diverse sampling methods.
Approach:	They propose a counterfactual multi-agent debate framework that predetermines LLMs' stances to override their inherent biases for answer inspection.
Outcome:	Extensive experiments on four datasets of three tasks demonstrate the superiority of the proposed framework over existing methods.

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Ronald Cardenas, Ying Lin, Heng Ji, Jonathan May

Highly Efficient Knowledge Graph Embedding Learning with Orthogonal Procrustes Analysis (2021.naacl-main)

Challenge:	Unsupervised part of speech (POS) tagging is often framed as a clustering problem, but taggers need to ground their clusters as well.
Approach:	They propose an approach for low-resource unsupervised part of speech (POS) tagging that yields fully grounded output and requires no labeled training data.
Outcome:	The proposed method achieves reasonable performance across languages, including Sinhalese and Kinyarwanda, with no labeled training data.

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Xutan Peng, Guanyi Chen, Chenghua Lin, Mark Stevenson

“Low-Resource” Text Classification: A Parameter-Free Classification Method with Compressors (2023.findings-acl)

Challenge:	Knowledge Graph Embeddings (KGEs) have been explored in recent years due to their promise for a wide range of applications.
Approach:	They propose a KGE framework which can reduce the training time and carbon footprint by orders of magnitudes compared with state-of-the-art approaches.
Outcome:	The proposed framework reduces the training time and carbon footprint by orders of magnitudes compared with state-of-the-art approaches while producing competitive performance.

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Zhiying Jiang, Matthew Yang, Mikhail Tsirlin, Raphael Tang, Yiqin Dai, Jimmy Lin

ScreenQA: Large-Scale Question-Answer Pairs Over Mobile App Screenshots (2025.naacl-long)

Challenge:	Text classification is one of the most fundamental tasks in natural language processing (NLP), but deep neural networks are data-hungry and expensive to train.
Approach:	They propose a non-parametric alternative to DNNs that uses a compressor like gzip and a k-nearest-neighbor classifier to achieve competitive results.
Outcome:	The proposed method outperforms BERT on all five OOD datasets and outperformed other methods on the few-shot setting.

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Yu-Chung Hsiao, Fedir Zubach, Gilles Baechler, Srinivas Sunkara, Victor Carbune, Jason Lin, Maria Wang, Yun Zhu, Jindong Chen

Outcome Accuracy is Not Enough: Aligning the Reasoning Process of Reward Models (2026.acl-long)

Challenge:	Existing screen datasets focus on low-level structural and component understanding or on a much higher-level composite task such as navigation and task completion for autonomous agents.
Approach:	They propose to annotate 86k question-answer pairs over the RICO dataset to benchmark screen content understanding.
Outcome:	The proposed dataset covers full answers, short answer phrases, and corresponding UI contents with bounding boxes, enabling four subtasks to address various application scenarios.

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Binghai Wang, Yantao Liu, Yuxuan Liu, Tianyi Tang, Shenzhi Wang, Chang Gao, Chujie Zheng, Yichang Zhang, Le Yu, Shixuan Liu, Tao Gui, Qi Zhang, Xuanjing Huang, Bowen Yu, Fei Huang, Junyang Lin

Identifying Cellular Niches in Spatial Transcriptomics: An Investigation into the Capabilities of Large Language Models (2025.acl-long)

Challenge:	Recent studies observe a phenomenon where reward models achieve high accuracy on static datasets but fail to generalize effectively during RLHF.
Approach:	They propose a method that combines rationale consistency with outcome accuracy to improve performance on RM-Bench and JudgeBench.
Outcome:	The proposed method surpasses baselines on RM-Bench and JudgeBench by an average of 5% and improves creative writing tasks by 7%.

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Huanhuan Wei, Xiao Luo, Hongyi Yu, Jinping Liang, Luning Yang, Lixing Lin, Alexandra Popa, Xiting Yan

Fourier Transformer: Fast Long Range Modeling by Removing Sequence Redundancy with FFT Operator (2023.findings-acl)

Challenge:	Spatial transcriptomic technologies allow measuring gene expression profile and spatial information of cells in tissues simultaneously.
Approach:	They propose a spatial transcriptomic approach to identify spatial niches using a zero-shot large language models by transforming spatial transcriptomics data into spatial context prompts.
Outcome:	The proposed model improves performance by leveraging gene expression of neighboring cells/spots, cell type composition, tissue information, and external knowledge.

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Ziwei He, Meng Yang, Minwei Feng, Jingcheng Yin, Xinbing Wang, Jingwen Leng, Zhouhan Lin

AfriCLIRMatrix: Enabling Cross-Lingual Information Retrieval for African Languages (2022.emnlp-main)

Challenge:	Existing transformer models are computationally demanding and prohibitively costly for long sequences due to the quadratic complexity of its selfattention module.
Approach:	They propose a transformer-based model that inherits weights from large pretrained models by removing redundancies in hidden sequences using the ready-made Fast Fourier Transform operator.
Outcome:	The proposed model outperforms the standard BART model on the long-range modeling benchmark LRA with significant improvements in speed and space.

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Odunayo Ogundepo, Xinyu Zhang, Shuo Sun, Kevin Duh, Jimmy Lin

Revisiting Sample Size Determination in Natural Language Understanding (2023.findings-acl)

Challenge:	Existing datasets for cross-lingual information retrieval are limited in many languages, especially those spoken in Africa.
Approach:	They propose to build a test collection for cross-lingual information retrieval in 15 diverse African languages.
Outcome:	AfriCLIRMatrix contains 6 million queries in English and 23 million relevance judgments automatically mined from Wikipedia inter-language links, covering many more African languages than any existing information retrieval test collection.

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Ernie Chang, Muhammad Hassan Rashid, Pin-Jie Lin, Changsheng Zhao, Vera Demberg, Yangyang Shi, Vikas Chandra

MoA: Heterogeneous Mixture of Adapters for Parameter-Efficient Fine-Tuning of Large Language Models (2026.acl-long)

Challenge:	Recent work has sought to reduce the annotation costs through the use of active learning and data sampling.
Approach:	They propose to estimate the training sample size needed to achieve a targeted model performance based on small amount of training samples.
Outcome:	The proposed approach predicts model performance within a small margin of mean absolute error (0.9%) with only 10% data.

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Jie Cao, Tianwei Lin, Bo Yuan, Rolan Yan, Hongyang He, Wenqiao Zhang, Juncheng Li, Dongping Zhang, Siliang Tang, Yueting Zhuang

Lost in Overlap: Exploring Logit-based Watermark Collision in LLMs (2025.findings-naacl)

Challenge:	Existing methods for parameter-efficient fine-tuning (PEFT) are limited by computational costs and performance degradation.
Approach:	They propose a method that integrates Low-Rank Adaptation and Mixture-of-Experts (MoE) they propose combining expert load imbalance and representation collapse to improve LLM performance .
Outcome:	The proposed method outperforms homogeneous MoE-LoRA architectures in performance and parameter efficiency.

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Yiyang Luo, Ke Lin, Chao Gu, Jiahui Hou, Lijie Wen, Luo Ping

Across Programming Language Silos: A Study on Cross-Lingual Retrieval-Augmented Code Generation (2026.findings-acl)

Challenge:	Existing watermarking methods embed imperceptible identifiers into text to address copyright concerns.
Approach:	They propose a new philosophy for watermark attacks that addresses watermark collision . they demonstrate that collision poses a threat to all logit-based watermark algorithms .
Outcome:	The proposed method improves watermark collision performance on top of other methods.

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Qiming Zhu, Jialun Cao, Xuanang Chen, Weili Zhang, Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun, Shing-Chi Cheung

Entrospect: Information-Theoretic Self-Reflection Elicits Better Response Refinement of Small Language Models (2025.findings-acl)

Challenge:	Current research on large language models with retrieval-augmented code generation (RACG) has focused on single-language settings, leaving their cross-lingual effectiveness underexplored.
Approach:	They construct a dataset covering 13 PLs with nearly 14K instances to study cross-lingual code knowledge transfer in RACG.
Outcome:	The proposed model shows unequal cross-lingual knowledge transfer even with direct injection and shows limited reliance on natural language information embedded in code when equipped with a code-specific retriever.

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Tianqiang Yan, Ziqiao Lin, Lin Zhang, Zhenglong Sun, Yuan Gao

VideoCuRL: Video Curriculum Reinforcement Learning with Orthogonal Difficulty Decomposition (2026.acl-long)

Challenge:	Existing approaches to self-reflection fail to deliver robust response refinement for models with parameter sizes of 10 billion or smaller.
Approach:	They propose to redesign Self-Refine and introduce an information-theoretic framework based on Chain-of-Thought prompt engineering to improve self-reflection in Small Language Models.
Outcome:	The proposed framework improves reasoning accuracy and computational efficiency by up to 36.2% under identical model and data settings.

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Hongbo Jin, Kuanwei Lin, Wenhao Zhang, Yichen Jin, Ge Li

SALMON: A Structure-Aware Language Model with logicality and densification strategy for Temporal Knowledge Graph Reasoning (2024.findings-emnlp)

Challenge:	Reinforcement Learning (RL) is crucial for Video-LLMs with complex spatiotemporal reasoning.
Approach:	They propose a framework that decomposes difficulty into two axes in video understanding . they employ efficient, training-free proxies to map data onto a 2D curriculum grid .
Outcome:	The proposed framework surpasses strong RL baselines on reasoning and perception tasks.

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Fu Zhang, Jinghao Lin, Jingwei Cheng

Dive into Deep Learning for Natural Language Processing (D19-2)

Challenge:	Temporal knowledge graph reasoning (TKGR) is a crucial task that involves reasoning at known timestamps to complete the future facts.
Approach:	They propose a temporal knowledge graph reasoning model with logicality and densification strategy that captures temporal evolving pattern and structural information in TKGs.
Outcome:	The proposed model outperforms the state-of-the-art models and is based on a structure-aware language model with logicality and densification strategy.

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Haibin Lin, Xingjian Shi, Leonard Lausen, Aston Zhang, He He, Sheng Zha, Alexander Smola

Challenge:	GluonNLP is a powerful new toolkit that automates the most laborious aspects of deep learning for NLP.
Approach:	This hands-on tutorial demonstrates how to scale unsupervised pre-training techniques with Apache MXNet and GluonNLP.
Outcome:	This hands-on tutorial examines the challenges of scaling these models and algorithms effectively with Apache MXNet and GluonNLP.

Weakly-Supervised Spatio-Temporally Grounding Natural Sentence in Video (P19-1)

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Zhenfang Chen, Lin Ma, Wenhan Luo, Kwan-Yee Kenneth Wong

VulLibGen: Generating Names of Vulnerability-Affected Packages via a Large Language Model (2024.acl-long)

Challenge:	Existing techniques for weakly-supervised spatio-temporally grounding natural sentence in video are lacking .
Approach:	They propose a weakly-supervised task for spatially grounding sentences in video . they extract instances from video and encode them using attentive interactor . results demonstrate superiority of their proposed task over baseline approaches .
Outcome:	The proposed model outperforms baseline approaches in a weakly-supervised task . it can characterize reliable instance-sentence pairs and penalize unreliable ones .

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Tianyu Chen, Lin Li, ZhuLiuchuan ZhuLiuchuan, Zongyang Li, Xueqing Liu, Guangtai Liang, Qianxiang Wang, Tao Xie

On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey (2024.findings-acl)

Challenge:	Existing work on affected package identification is limited by large language models . a recent study shows that 84% third-party packages contain security vulnerabilities .
Approach:	They propose a method to use LLM to generate the affected package . they propose supervised fine-tuning, retrieval augmented generation and a local search algorithm .
Outcome:	The proposed method has an average precision of 0.806 for identifying vulnerable packages in four most popular ecosystems in GitHub Advisory.

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Lin Long, Rui Wang, Ruixuan Xiao, Junbo Zhao, Xiao Ding, Gang Chen, Haobo Wang

Challenge:	Large Language Models (LLMs) provide a data-centric solution to alleviate limitations of real-world data with synthetic data generation.
Approach:	They propose a generic workflow for LLM-driven synthetic data generation.
Outcome:	The proposed workflows highlight gaps in existing research and outline avenues for future studies.

Dual-Feedback Knowledge Retrieval for Task-Oriented Dialogue Systems (2023.emnlp-main)

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Tianyuan Shi, Liangzhi Li, Zijian Lin, Tao Yang, Xiaojun Quan, Qifan Wang

CoachMe: Decoding Sport Elements with a Reference-Based Coaching Instruction Generation Model (2025.acl-long)

Challenge:	Current approaches to task-oriented dialogue systems integrate knowledge retrieval and response generation, which poses scalability challenges when dealing with extensive knowledge bases.
Approach:	They propose a retriever-generator architecture that harnesses a retrieval and a generator to generate system responses by using feedback from the generator as pseudo-labels.
Outcome:	The proposed architecture shows superior performance on three benchmark datasets.

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Wei-Hsin Yeh, Yu-An Su, Chih-Ning Chen, Yi-Hsueh Lin, Calvin Ku, Wenhsin Chiu, Min-Chun Hu, Lun-Wei Ku

Challenge:	Existing multimodal models for motion related tasks have shown significant progress.
Approach:	They propose a reference-based model that analyzes the differences between a learner’s motion and a physical reference under temporal and physical aspects.
Outcome:	The proposed model outperforms GPT-4o on figure skating and boxing by 31.6% and 58.3% respectively.

Empathetic Dialogue Generation via Sensitive Emotion Recognition and Sensible Knowledge Selection (2022.findings-emnlp)

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Lanrui Wang, Jiangnan Li, Zheng Lin, Fandong Meng, Chenxu Yang, Weiping Wang, Jie Zhou

InternLM-XComposer2.5-Reward: A Simple Yet Effective Multi-Modal Reward Model (2025.findings-acl)

Challenge:	Empathy is a key trait of everyday human conversations.
Approach:	They propose a serial encoding and Emotion-Knowledge interaction method for empathetic dialogue generation which is more sensitive to emotion dynamics in conversations.
Outcome:	The proposed method outperforms baseline evaluations on the utterance-level annotated EMPATHETICDIALOGUES.

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Yuhang Zang, Xiaoyi Dong, Pan Zhang, Yuhang Cao, Ziyu Liu, Shengyuan Ding, Shenxi Wu, Yubo Ma, Haodong Duan, Wenwei Zhang, Kai Chen, Dahua Lin, Jiaqi Wang

Incorporating Exponential Smoothing into MLP: a Simple but Effective Sequence Model (2024.findings-naacl)

Challenge:	Despite the promising performance of Large Vision Language Models, they sometimes generate incorrect outputs.
Approach:	They propose a multi-modal reward model that aligns LVLMs with human preferences.
Outcome:	The proposed model achieves excellent results on the latest multi-modal reward model benchmark and shows competitive performance on text-only reward model.

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JiqunChu JiqunChu, Zuoquan Lin

Challenge:	Structured State Space models (SSMs) have been used for long-range sequence learning but are limited in their complexity and computational and memory requirements.
Approach:	They propose to incorporate a simple SSM into an element-wise MLP to reduce inductive bias.
Outcome:	The proposed model achieves comparable results to existing models on the LRA benchmark.

mPLM-Sim: Better Cross-Lingual Similarity and Transfer in Multilingual Pretrained Language Models (2024.findings-eacl)

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Peiqin Lin, Chengzhi Hu, Zheyu Zhang, Andre Martins, Hinrich Schuetze

Scaling Parameter-Constrained Language Models with Quality Data (2024.emnlp-industry)

Challenge:	Recent multilingual pretrained language models encode strong language-specific signals, which are not explicitly provided during pretraining.
Approach:	They propose a language similarity measure that induces similarities across languages from mPLMs using multi-parallel corpora.
Outcome:	The proposed measure exhibits moderately high correlations with linguistic similarity measures, and more accurate similarity results on low correlation languages.

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Ernie Chang, Matteo Paltenghi, Yang Li, Pin-Jie Lin, Changsheng Zhao, Patrick Huber, Zechun Liu, Rastislav Rabatin, Yangyang Shi, Vikas Chandra

Retrieval-Augmented Parsing for Complex Graphs by Exploiting Structure and Uncertainty (2023.findings-emnlp)

Challenge:	Scaling laws in language modeling quantify training loss as a function of dataset size and model parameters, but neglect the critical role of data quality in model generalization.
Approach:	They propose to use effective training tokens as a combination of text diversity and syntheticity as measured by a teacher model to calculate scaling laws.
Outcome:	The proposed term effective training tokens is a combination of two readily-computed indicators of text diversity and syntheticity as measured by a teacher model.

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Zi Lin, Quan Yuan, Panupong Pasupat, Jeremiah Liu, Jingbo Shang

Improve Interpretability of Neural Networks via Sparse Contrastive Coding (2022.findings-emnlp)

Challenge:	Retrieval augmentation is effective for large graph parsing tasks, but can fail to identify the most informative exemplars . structure-aware and uncertainty-guided adaptive retrieval (SUGAR) exploits two unique sources of information: structural similarity and model uncertainty.
Approach:	They propose a structure-aware and uncertainty-guided adaptive retrieval approach that exploits structural similarity and model uncertainty to improve retrieval-augmented parsing for complex graph problems.
Outcome:	The proposed method improves retrieval-augmented parsing for graph parsers with large output graphs and non-trivial structure.

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Junhong Liu, Yijie Lin, Liang Jiang, Jia Liu, Zujie Wen, Xi Peng

Interactive Language Learning by Question Answering (D19-1)

Challenge:	XAI has achieved remarkable advances, but few efforts have been devoted to solving the problem.
Approach:	They propose a model-agnostic explanation method termed Sparse Contrastive Coding . they use model-based explanations to explain the black-box in a more model-oriented way .
Outcome:	The proposed method outperforms five state-of-the-art methods in interpretability and classification metrics.

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Xingdi Yuan, Marc-Alexandre Côté, Jie Fu, Zhouhan Lin, Chris Pal, Yoshua Bengio, Adam Trischler

Inferring Rewards from Language in Context (2022.acl-long)

Challenge:	Existing machine reading comprehension tasks lack interactive information-seeking component of comprehension.
Approach:	They propose a question-asking task that asks questions in a text-based environment . they propose QAit, which uses a game generator to build models that include deep reinforcement learning agents.
Outcome:	The proposed task poses questions about existence, location, and attributes of objects found in environment.

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Jessy Lin, Daniel Fried, Dan Klein, Anca Dragan

Challenge:	a new study grounding language to reward functions extends the standard instruction following setup in this way.
Approach:	They propose a model that infers rewards from language pragmatically by reasoning about how speakers choose utterances to elicit desired actions and reveal information about their preferences.
Outcome:	The proposed model infers rewards from language pragmatically on a flight–booking task with natural language.

Robust Dialogue State Tracking with Weak Supervision and Sparse Data (2022.tacl-1)

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Michael Heck, Nurul Lubis, Carel van Niekerk, Shutong Feng, Christian Geishauser, Hsien-Chin Lin, Milica Gašić

ORANGE: Text-video Retrieval via Watch-time-aware Heterogeneous Graph Contrastive Learning (2023.emnlp-industry)

Challenge:	Generalizing dialogue state tracking (DST) to new data and domains is especially challenging due to the strong reliance on abundant and fine-grained supervision during training.
Approach:	They propose a training strategy to build extractive DST models without the need for fine-grained manual span labels.
Outcome:	The proposed model improves robustness against sample sparsity, new concepts, and topics, leading to state-of-the-art performance on a range of benchmarks.

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Yucheng Lin, Tim Chang, Yaning Chang, Jianqiang Ma, Donghui Li, Ting Peng, Zang Li, Zhiyi Zhou, Feng Wang

Development of a Benchmark Corpus to Support Entity Recognition in Job Descriptions (2022.lrec-1)

Challenge:	Existing methods for text-video retrieval focus on informative representations and delicate matching mechanisms, but real-world scenarios often involve brief, ambiguous queries and low-quality videos.
Approach:	They propose a novel method to learn informative embeddings for queries and videos . they use a watch-time-aware contrastive learning paradigm to capture dependencies .
Outcome:	The proposed method is effective in a real-world video-search service.

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Thomas Green, Diana Maynard, Chenghua Lin

On-Policy Self-Alignment with Fine-grained Knowledge Feedback for Hallucination Mitigation (2025.findings-acl)

Challenge:	Existing tools for identifying and extracting salient entities from job descriptions are limited by the lack of publicly available training data.
Approach:	They propose to use a standard definition of entities and a training corpus to develop a benchmark Entity Recognition (ER) model.
Outcome:	The proposed model achieves an F1 score of 0.59 from 18.6k entities comprising five types (Skill, Qualification, Experience, Occupation, and Domain).

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Xueru Wen, Jie Lou, Xinyu Lu, Yuqiu Ji, Xinyan Guan, Yaojie Lu, Hongyu Lin, Ben He, Xianpei Han, Debing Zhang, Le Sun

TRIGO: Benchmarking Formal Mathematical Proof Reduction for Generative Language Models (2023.emnlp-main)

Challenge:	Large language models exhibit behavior that deviates from the boundaries of their knowledge during response generation.
Approach:	They propose a framework that allows large language models to explore their knowledge boundaries and self-correct generation behavior through fine-grained feedback signals.
Outcome:	The proposed framework enables LLMs to explore their knowledge boundaries and self-correct generation behavior through fine-grained feedback signals.

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Jing Xiong, Jianhao Shen, Ye Yuan, Haiming Wang, Yichun Yin, Zhengying Liu, Lin Li, Zhijiang Guo, Qingxing Cao, Yinya Huang, Chuanyang Zheng, Xiaodan Liang, Ming Zhang, Qun Liu

GAPO: Robust Advantage Estimation for Real-World Code LLMs (2026.findings-acl)

Challenge:	Automated theorem proving (ATP) benchmarks focus on symbolic inference but rarely involve understanding complex number combination reasoning.
Approach:	They propose a benchmark that requires a model to reduce a trigonometric expression with step-by-step proof and evaluates a generative LM’s reasoning ability on formulas and ability to manipulate, group, and factor number terms.
Outcome:	The proposed benchmark evaluates a generative LM’s reasoning ability on formulas and ability to manipulate, group, and factor number terms.

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Jianqing Zhang, Zhezheng Hao, Wei Xia, Hande Dong, Hong Wang, Chenxing Wei, Yuyan Zhou, Yubin Qi, Qiang Lin, Jian Cao

AutoPKG: An Automated Framework for Dynamic E-commerce Product-Attribute Knowledge Graph Construction (2026.findings-acl)

Challenge:	Reinforcement learning (RL) is widely used for post-training large language models (LLMs) in code editing, but in real-world code editing scenarios, reward distributions are often skewed with unpredictable noise, leading to distorted advantage computation and increased rollout outliers.
Approach:	They propose a group-relative method that finds an interval with the highest SNR and uses the median of that interval as an adaptive Q to replace the group mean in advantage calculation.
Outcome:	The proposed method improves on nine instruction-tuned LLMs while remaining plug-and-play and efficient.

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Pollawat Hongwimol, Haoning Shang, Chutong Wang, Zhichao Wan, Yi Gao, Yuanming Li, Lin Gui, Wenhao Sun, Cheng Yu

Image Corruption-Inspired Membership Inference Attacks against Large Vision-Language Models (2026.eacl-long)

Challenge:	Product attribute extraction in e-commerce is bottlenecked by ontologies that are inconsistent, incomplete, and costly to maintain.
Approach:	They propose a multi-agent Large Language Model framework that constructs a Product-attribute Knowledge Graph from multimodal product content.
Outcome:	The proposed framework achieves 0.953 WKE for product types, 0.724 WKEs for attribute keys, and 0.531 edge-level accuracy for value assertions after canonicalization on a large real-world marketplace catalog dataset from Lazada (Alibaba).

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Zongyu Wu, Minhua Lin, Zhiwei Zhang, Fali Wang, Xianren Zhang, Xiang Zhang, Suhang Wang

Zero-Shot Learners for Natural Language Understanding via a Unified Multiple Choice Perspective (2022.emnlp-main)

Challenge:	Large vision-language models (LVLMs) are trained on large-scale datasets, which can pose privacy risks if training images contain sensitive information.
Approach:	They propose to detect whether a target image is used to train LVLMs by using image-text pairs and single-modality content to detect image-related data.
Outcome:	The proposed methods detect whether a target image is used to train the LVLM on large-scale datasets.

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Ping Yang, Junjie Wang, Ruyi Gan, Xinyu Zhu, Lin Zhang, Ziwei Wu, Xinyu Gao, Jiaxing Zhang, Tetsuya Sakai

Disentangle-based Continual Graph Representation Learning (2020.emnlp-main)

Challenge:	Existing approaches to zero-shot learning are format-agnostic and can address new learning tasks without additional training.
Approach:	They propose a new paradigm for zero-shot learning that is format agnostic and compatible with any format and applicable to a list of language tasks.
Outcome:	The proposed model shows state-of-the-art performance on several benchmarks and produces satisfactory results on tasks such as text classification and commonsense reasoning.

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Xiaoyu Kou, Yankai Lin, Shaobo Liu, Peng Li, Jie Zhou, Yan Zhang

Facilitating Long Context Understanding via Supervised Chain-of-Thought Reasoning (2025.emnlp-main)

Challenge:	Existing graph embedding methods overlook streaming nature of incoming data in real-world applications.
Approach:	They propose a disentangle-based continual graph representation learning framework inspired by the human’s ability to learn procedural knowledge.
Outcome:	The proposed framework outperforms state-of-the-art continual graph representation learning framework and alleviate catastrophic forgetting problem.

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Jingyang Lin, Andy Wong, Tian Xia, Shenghua He, Hui Wei, Mei Han, Jiebo Luo

Aligning Paralinguistic Understanding and Generation in Speech LLMs via Multi-Task Reinforcement Learning (2026.eacl-industry)

Challenge:	Recent advances in Large Language Models (LLMs) have enabled them to process increasingly longer sequences, ranging from 2K to 2M tokens and even beyond.
Approach:	They propose a synthetic dataset in the financial domain that integrates Chain-of-Thought reasoning into LLMs in a supervised manner to facilitate effective long-context understanding.
Outcome:	The proposed model outperforms standard GPT-4o-mini on the Loong benchmark and fine tunes LLaMA-3.1-8B-Instruct on the model, achieving a 28.0% gain on the financial subset.

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Minseok Kim, Jingxiang Chen, Seong-Gyun Leem, Yin Huang, Rashi Rungta, Zhicheng Ouyang, Haibin Wu, Surya Teja Appini, Ankur Bansal, Yang Bai, Yue Liu, Florian Metze, Ahmed A Aly, Anuj Kumar, Ariya Rastrow, Zhaojiang Lin

Past, Present, and Future: Conversational Emotion Recognition through Structural Modeling of Psychological Knowledge (2021.findings-emnlp)

Challenge:	Using paralinguistic cues is challenging for speech large language models, authors say . limited training data, annotation difficulty, and models exploiting lexical shortcuts are challenges . a recent study shows that modeling paralinguistic reasoning with multitask RL improves paralinguistics understanding .
Approach:	They propose multi-task reinforcement learning with chain-of-thought prompting that elicits explicit affective reasoning.
Outcome:	The proposed model improves paralinguistics understanding over baselines and strong proprietary models by 8-12% on Expresso, IEMOCAP, and RAVDESS.

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Jiangnan Li, Zheng Lin, Peng Fu, Weiping Wang

Metaphor Detection via Explicit Basic Meanings Modelling (2023.acl-short)

Challenge:	Conversational Emotion Recognition (CER) is a task to predict the emotion of an utterance in the context of a conversation.
Approach:	They propose a pSychological-Knowledge-Aware Interaction Graph to model the emotional state of an utterance in the context of a conversation.
Outcome:	The proposed method achieves state-of-the-art and competitive performance on four popular CER datasets.

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Yucheng Li, Shun Wang, Chenghua Lin, Frank Guerin

Mind the Biases: Quantifying Cognitive Biases in Language Model Prompting (2023.findings-acl)

Challenge:	Existing methods for metaphor detection use the aggregated meaning of a word to approximate its basic meaning.
Approach:	They propose a method which models the basic meaning of a word based on literal annotations and compares this with the contextual meaning in a target sentence to identify metaphors.
Outcome:	The proposed method outperforms the state-of-the-art method significantly in the F1 score and even reaches the theoretical upper bound on the VUA18 benchmark.

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Ruixi Lin, Hwee Tou Ng

Active Prompting with Chain-of-Thought for Large Language Models (2024.acl-long)

Challenge:	Cognitive biases in the human decision making process can lead to flawed responses when we are under uncertainty.
Approach:	They propose to expose cognitive biases on results of language model prompting which display bias modes resembling cognitive bias.
Outcome:	The proposed methods show that a toning-down transformation of the drug-drug description in a prompt can elicit a bias similar to the framing effect, warning users to distrust when prompting language models for answers.

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Shizhe Diao, Pengcheng Wang, Yong Lin, Rui Pan, Xiang Liu, Tong Zhang

Pushing Paraphrase Away from Original Sentence: A Multi-Round Paraphrase Generation Approach (2021.findings-acl)

Challenge:	Existing methods to annotate large language models rely on a fixed set of human-annotated exemplars, which are not always the most effective for different tasks.
Approach:	They propose a method to adapt large language models to different tasks with task-specific example prompts (annotated with human-designed CoT reasoning) they introduce several metrics to characterize uncertainty so as to select the most uncertain questions for annotation.
Outcome:	The proposed method significantly improves performance on eight complex reasoning tasks.

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Zhe Lin, Xiaojun Wan

Knowing What You Know: Calibrating Dialogue Belief State Distributions via Ensembles (2020.findings-emnlp)

Challenge:	Recent years, neural paraphrase generation models have demonstrated superior performance, but the output paraphrase still lacks diversity.
Approach:	They propose a back-translation guided multi-round paraphrase generation framework which leverages multi- round paraphrases to improve diversity while preserving semantic information.
Outcome:	The proposed model improves diversity while preserving semantic information.

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Carel van Niekerk, Michael Heck, Christian Geishauser, Hsien-chin Lin, Nurul Lubis, Marco Moresi, Milica Gasic

Think Hard Only When Needed: A Hybrid Best-of-N and Beam Search for Efficient Test-Time Compute (2026.findings-eacl)

Challenge:	Current models for dialogue state tracking only achieve 55% accuracy . however, they lack in performance compared to belief trackers and do not produce well calibrated distributions.
Approach:	They propose to calibrate a model for dialogue belief trackers to measure dialogue state accuracy.
Outcome:	The proposed model outperforms existing models in terms of accuracy and accuracy.

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Hyewon Suh, Chaojian Li, Cheng-Jhih Shih, Zheng Wang, Kejing Xia, Yonggan Fu, Yingyan Celine Lin

Timely Machine: Awareness of Time Makes Test-Time Scaling Agentic (2026.acl-long)

Challenge:	Large language models (LLMs) exhibit remarkable reasoning and planning capabilities, yet their substantial inference-time cost significantly impedes deployment in resourceconstrained applications.
Approach:	They propose a hybrid inference pipeline that combines beam search and Best-of-N . THROW generates shorter initial trajectories and evaluates them using PRMs .
Outcome:	THROW achieves 1.54 and 14.38 latency speedups and 35.7% and 80.4% token reductions on average compared to Best-of-N and beam search .

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Yichuan Ma, Linyang Li, Yongkang Chen, Peiji Li, Xiaozhe Li, Qipeng Guo, Dahua Lin, Kai Chen

RESIN: A Dockerized Schema-Guided Cross-document Cross-lingual Cross-media Information Extraction and Event Tracking System (2021.naacl-demos)

Challenge:	Large language models (LLMs) tackle complex reasoning tasks, but test-time scaling is becoming expensive.
Approach:	They propose to redefine test-time as wall-clock time, where models dynamically adjust strategies based on time budgets.
Outcome:	The proposed model improves time budget awareness and boosts performance across Timely-Eval.

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Haoyang Wen, Ying Lin, Tuan Lai, Xiaoman Pan, Sha Li, Xudong Lin, Ben Zhou, Manling Li, Haoyu Wang, Hongming Zhang, Xiaodong Yu, Alexander Dong, Zhenhailong Wang, Yi Fung, Piyush Mishra, Qing Lyu, Dídac Surís, Brian Chen, Susan Windisch Brown, Martha Palmer, Chris Callison-Burch, Carl Vondrick, Jiawei Han, Dan Roth, Shih-Fu Chang, Heng Ji

Efficient Model Development through Fine-tuning Transfer (2025.emnlp-main)

Challenge:	We present a new information extraction system that can construct temporal event graphs from news documents.
Approach:	They propose a temporal event graph extraction system that can extract news documents . they extend the system from sentence-level event extraction to cross-document cross-media event extraction .
Outcome:	The proposed system can extract temporal event graphs from news documents in multiple languages and multiple data modalities.

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Pin-Jie Lin, Rishab Balasubramanian, Fengyuan Liu, Nikhil Kandpal, Tu Vu

RealMedDial: A Real Telemedical Dialogue Dataset Collected from Online Chinese Short-Video Clips (2022.coling-1)

Challenge:	Modern large language models face a major bottleneck: each new version of a pre-trained model requires expensive and repetitive alignment.
Approach:	They propose a method that transfers fine-tuning updates across model versions . they extract the diff vector, which is the difference in parameters induced by fine-uning, from a source model and apply it to the base of a different target model.
Outcome:	The proposed method reduces training costs while maintaining model performance.

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Bo Xu, Hongtong Zhang, Jian Wang, Xiaokun Zhang, Dezhi Hao, Linlin Zong, Hongfei Lin, Fenglong Ma

MC2: Towards Transparent and Culturally-Aware NLP for Minority Languages in China (2024.acl-long)

Challenge:	Existing medical dialogue systems are limited by the lack of corpora and data from real scenarios.
Approach:	They construct a Chinese medical dialogue dataset based on real medical consultations.
Outcome:	The proposed dataset is applicable to a wide range of NLP tasks with respect to medical dialogue.

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Chen Zhang, Mingxu Tao, Quzhe Huang, Jiuheng Lin, Zhibin Chen, Yansong Feng

Challenge:	MC2 is the largest open-source corpus of minority languages in china . MC2, however, includes four underrepresented languages: Tibetan, Uyghur, Kazakh, and Mongolian .
Approach:	They propose a multilingual corpus of minority languages in China that includes four underrepresented languages . they prioritize accuracy while enhancing diversity by using a quality-centric approach .
Outcome:	The proposed model prioritizes accuracy while enhancing diversity, the authors say . MC2 includes four underrepresented languages: Tibetan, Uyghur, Kazakh, and Mongolian .

STATE ToxiCN: A Benchmark for Span-level Target-Aware Toxicity Extraction in Chinese Hate Speech Detection (2025.findings-acl)

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Zewen Bai, Liang Yang, Shengdi Yin, Junyu Lu, Jingjie Zeng, Haohao Zhu, Yuanyuan Sun, Hongfei Lin

Ethical NLP

Challenge:	Existing studies on Chinese hate speech detection lack span-level fine-grained annotations.
Approach:	They construct a Span-level target-aware Toxicity Extraction dataset and evaluate existing models for Chinese hateful slang.
Outcome:	The proposed dataset is the first span-level Chinese hate speech dataset and evaluates the ability of existing models to understand hate semantics.

Discovering Semantic Subdimensions through Disentangled Conceptual Representations (2025.findings-emnlp)

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Yunhao Zhang, Shaonan Wang, Nan Lin, Xinyi Dong, Chong Li, Chengqing Zong

Dual-Reasoner: Bridging Interleaved Atomicity and Streaming Latency via Thinking-while-Talking (2026.findings-acl)

Challenge:	Existing approaches focus on predefined dimensions that overlook finer conceptual distinctions . a new framework is proposed to investigate the subdimensions underlying coarse-grained semantic dimensions .
Approach:	They propose a framework that decomposes word embeddings into multiple sub-embeddings . they propose to map these subdimensions to brain activation to assess their plausibility .
Outcome:	The proposed framework decomposes word embeddings from large language models into sub-embeddings, each encoding specific semantic information.

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Yangzhuo Li, Shengpeng Ji, Yifu Chen, Tianle Liang, Haoyu Yang, null Junboli, Jun Fang, Lin Li, Qingyang Hong

WarriorCoder: Learning from Expert Battles to Augment Code Large Language Models (2025.acl-long)

Challenge:	Existing methods to integrate Chain-of-Thought into spoken dialogue models incur prohibitive latency.
Approach:	They propose a Streaming Masking Mechanism to ensure uninterrupted audio streaming . they use a quadruple-constraint system to reconstruct logical atomicity .
Outcome:	Experimental results show that Dual-Reasoner improves speech generation performance with low latency.

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Huawen Feng, Pu Zhao, Qingfeng Sun, Can Xu, Fangkai Yang, Lu Wang, Qianli Ma, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang, Qi Zhang

PLAWBENCH: A Rubric-Based Benchmark for Evaluating LLMs in Real-World Legal Practice (2026.acl-long)

Challenge:	Recent code large language models have demonstrated impressive performance on code-related tasks.
Approach:	They propose a paradigm that learns from expert battles to address these limitations . they create an arena where leading LLMs challenge each other with evaluations .
Outcome:	The proposed model improves on existing models by leveraging expert battles . it achieves state-of-the-art performance even without relying on proprietary models .

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Yuzhen Shi, Huanghai Liu, Yiran HU, Song Gaojie, Xu Xinran, Yubo Ma, Tianyi Tang, Li Zhang, Qingjing Chen, Feng Di, Wenbo Lv, Weiheng Wu, Kexin Yang, Sen Yang, Wei Wang, Rongyao Shi, Qiu Yuanyang, Yuemeng Qi, Zhang Jingwen, Sui Xiaoyu, Yifan Chen, Zhang Yi, An Yang, Bowen Yu, Dayiheng Liu, Junyang Lin, Weixing Shen, Bing Zhao, Charles L. A. Clarke, HU Wei

Position Bias Mitigation: A Knowledge-Aware Graph Model for Emotion Cause Extraction (2021.acl-long)

Challenge:	Existing benchmarks for large language models (LLMs) are coarse, single-dimensional metrics and do not explicitly assess fine-grained legal reasoning.
Approach:	They propose a Practical Law Benchmark to evaluate large language models in real-world legal practice scenarios.
Outcome:	The proposed model is based on 850 questions and 13 scenarios with expert-designed evaluation rubrics.

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Hanqi Yan, Lin Gui, Gabriele Pergola, Yulan He

Dialectical Structured Reasoning for Explainable Multimodal Fake News Detection (2026.findings-acl)

Challenge:	Existing models for ECE tend to explore relative position information and suffer from the dataset bias.
Approach:	They propose to generate adversarial examples where relative position is no longer indicative feature of cause clauses to address the dataset bias.
Outcome:	The proposed method performs on par with existing state-of-the-art methods on the original ECE dataset and is more robust against adversarial attacks compared to existing models.

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Ruichao Yang, Yufan Bian, Wei Gao, Bo-Wen Zhang, Jing Ma, Hongzhan Lin, Ziyang Luo, Xiaobin Zhu, Xu-Cheng Yin

AED-RAG: Continuous Multi-Granular Context Fusion for Retrieval-Augmented Generation via Adaptive Ensemble Decoding (2026.findings-acl)

Challenge:	Existing fake news detection models are opaque and lack deductive transparency . a framework for dialectical structured reasoning is proposed to address this limitation .
Approach:	They propose a framework that model fake news detection as an explicit dialectical process over multimodal social context.
Outcome:	The proposed framework achieves state-of-the-art while producing transparent explanations that mirror human reasoning process.

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Junzhe Zhou, Fulin Lin, Tairan Cheng, Shaowen Chen, Hongwei Wang

CINO: A Chinese Minority Pre-trained Language Model (2022.coling-1)

Challenge:	Existing alignment strategies that rely on discrete reranking struggle to address this granularity mismatch or effectively balance external evidence with internal knowledge.
Approach:	They propose a framework that synergizes discrete retrieval with continuous reranking to discern the information density differences between unstructured narrative passages and structured knowledge triplets.
Outcome:	Extensive experiments on four open-domain QA benchmarks show that AED-RAG significantly outperforms competitive baselines.

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Ziqing Yang, Zihang Xu, Yiming Cui, Baoxin Wang, Min Lin, Dayong Wu, Zhigang Chen

BanditMTL: Bandit-based Multi-task Learning for Text Classification (2021.acl-long)

Challenge:	Existing multilingual pre-trained language models do not perform well on some low-resource languages.
Approach:	They propose a multilingual pre-trained language model for Chinese minority languages . they collect documents from Wikipedia and construct two classification datasets .
Outcome:	The proposed model outperforms baseline models on various classification tasks.

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Yuren Mao, Zekai Wang, Weiwei Liu, Xuemin Lin, Wenbin Hu

STELLA: A Multimodal LLM for Protein Functional Annotation via Unified Sequence-Structure Encoding (2026.findings-acl)

Challenge:	Existing methods to regularize task variance are unexplored in multi-task text classification.
Approach:	They propose a multi-task learning method based on adversarial multi-armed bandit to regularize the task variance by means of a mirror gradient ascent-descent algorithm.
Outcome:	The proposed method achieves state-of-the-art in multi-task text classification.

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Hongwang Xiao, Wenjun Lin, Xi Chen, Hui Wang, Kai Chen, Jiashan Li, Yuancheng Sun, Sicheng Dai, Boya Wu, Qiwei Ye

EA-Agent: A Structured Multi-Step Reasoning Agent for Entity Alignment (2026.acl-long)

Challenge:	a multimodal protein language model (LLM) integrates sequence, structure, and function into functional annotation.
Approach:	They propose a multimodal protein language model that synergistically aligns bimodal representations with the textual modality to advance protein functional annotation.
Outcome:	The proposed model synergizes bimodal representations with the textual modality to advance protein functional annotation.

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Yixuan Nan, Xixun Lin, Yanmin Shang, Ge Zhang, Zheng Fang, Fang Fang, Yanan Cao

TailorKV: A Hybrid Framework for Long-Context Inference via Tailored KV Cache Optimization (2025.findings-acl)

Challenge:	Entity alignment (EA) aims to identify entities across different knowledge graphs (KGs) that refer to the same real-world object.
Approach:	They propose to use large language models to integrate semantic knowledge into EA to identify entities across different knowledge graphs that refer to the same object.
Outcome:	The proposed agent outperforms existing methods and achieves state-of-the-art performance on three benchmark datasets.

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Dingyu Yao, Bowen Shen, Zheng Lin, Wei Liu, Jian Luan, Bin Wang, Weiping Wang

Challenge:	Existing work mitigates memory overhead by offloading or compressing the Key-Value cache.
Approach:	They propose a method that integrates quantization and offloading into a generative large language model by using a hybrid compression method.
Outcome:	The proposed method outperforms the state-of-the-art in long-context evaluations.

Emergent Modularity in Pre-trained Transformers (2023.findings-acl)

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Zhengyan Zhang, Zhiyuan Zeng, Yankai Lin, Chaojun Xiao, Xiaozhi Wang, Xu Han, Zhiyuan Liu, Ruobing Xie, Maosong Sun, Jie Zhou

Prototype Tuning: A Meta-Learning Approach for Few-Shot Document-Level Relation Extraction with Large Language Models (2025.findings-naacl)

Challenge:	Existing studies on pre-trained Transformers show that they learn fine-grained neuron functions.
Approach:	They examine the presence of modularity in pre-trained Transformers . they focus on Mixture-of-Experts, a promising candidate for modularity .
Outcome:	The proposed structure stabilizes at the early stage, which is faster than neuron stabilization.

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Dinghao Pan, Yuanyuan Sun, Bo Xu, Jiru Li, Zhihao Yang, Ling Luo, Hongfei Lin, Jian Wang

Attribution-Based Analysis and Optimization of Modular Agentic Workflows (2026.findings-acl)

Challenge:	Few-Shot Document-Level Relation Extraction (FSDLRE) aims to develop models capable of generalizing to new categories with minimal support examples.
Approach:	They propose a meta-training approach to train Large Language Models to improve their ICL capabilities . they construct simulated episodes using relation types that do not overlap with test corpus .
Outcome:	Experimental results show that the proposed approach outperforms baseline models on few-shot tasks.

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Yingxuan Yang, Bo Huang, Siyuan Qi, Chao Feng, Haoyi Hu, Yuxuan Zhu, Jinbo Hu, Haoran Zhao, Ziyi He, Xiao Liu, ZongYu Wang, Muning Wen, Lin Qiu, Xuezhi Cao, Xunliang Cai, Yong Yu, Weinan Zhang

Challenge:	Large Language Models (LLMs) have driven the rise of agentic workflows . yet, how can we attribute performance gains to individual upgrades and their interactions?
Approach:	They propose a game-theoretic framework that models component upgrades as players and evaluates component coalitions to compute Shapley values.
Outcome:	The proposed framework provides interaction-aware attribution and recommendation for model allocation under a fixed workflow structure.

Making Better Use of Bilingual Information for Cross-Lingual AMR Parsing (2021.findings-acl)

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Yitao Cai, Zhe Lin, Xiaojun Wan

Mem-Gallery: Benchmarking Multimodal Long-Term Conversational Memory for MLLM Agents (2026.acl-long)

Challenge:	Existing work on meaning representations for English and other languages finds that concepts in their predicted AMR graphs are less specific.
Approach:	They propose a cross-lingual AMR parser that can predict more precise concepts by translating translated texts and non-English texts.
Outcome:	The proposed model surpasses state-of-the-art parser by 10.6 points on Smatch F1 score.

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Yuanchen Bei, Tianxin Wei, Xuying Ning, Yanjun Zhao, Zhining Liu, Xiao Lin, Yada Zhu, Hendrik Hamann, Jingrui He, Hanghang Tong

FastCorrect 2: Fast Error Correction on Multiple Candidates for Automatic Speech Recognition (2021.findings-emnlp)

Challenge:	Existing benchmarks evaluate multi-session memory in text-only conversations or assess multimodal understanding within localized contexts.
Approach:	They propose a benchmark for evaluating multimodal long-term conversational memory in MLLM agents.
Outcome:	The proposed framework assesses key memory capabilities along three functional dimensions: memory extraction and test-time adaptation, memory reasoning, and memory knowledge management.

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Yichong Leng, Xu Tan, Rui Wang, Linchen Zhu, Jin Xu, Wenjie Liu, Linquan Liu, Xiang-Yang Li, Tao Qin, Edward Lin, Tie-Yan Liu

Agent Lumos: Unified and Modular Training for Open-Source Language Agents (2024.acl-long)

Challenge:	Error correction is widely used in automatic speech recognition (ASR) to post-process the generated sentence.
Approach:	They propose a fast correction model that takes multiple ASR candidates as input for better correction accuracy.
Outcome:	The proposed model can reduce the word error rate (WER) with multiple candidates by 3.2% and 2.6%.

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Da Yin, Faeze Brahman, Abhilasha Ravichander, Khyathi Chandu, Kai-Wei Chang, Yejin Choi, Bill Yuchen Lin

Challenge:	Lumos is a framework for training open-source agents on complex interactive tasks.
Approach:	They propose a framework for training open-source LLM-based agents called Lumos . Lumos features a learnable, unified and modular architecture with a planning module that learns high-level subgoal generation and a grounding module trained to translate these into the actions using various tools in the execution module.
Outcome:	The framework outperforms open-source agents on QA and web tasks.

An Empirical Study of Clinical Note Generation from Doctor-Patient Encounters (2023.eacl-main)

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Asma Ben Abacha, Wen-wai Yim, Yadan Fan, Thomas Lin

Do Before You Judge: Self-Reference as a Pathway to Better LLM Evaluation (2025.findings-emnlp)

Challenge:	Medical doctors spend 52 to 102 minutes per day writing clinical notes from patient encounters.
Approach:	They propose to use a new dataset to generate automated and manual clinical notes from doctor-patient conversations in a clinical setting.
Outcome:	The proposed model could reduce the time spent writing clinical notes from doctor-patient conversations in a clinical setting.

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Wei-Hsiang Lin, Sheng-Lun Wei, Hen-Hsen Huang, Hsin-Hsi Chen

Challenge:	LLM-as-Judge frameworks are increasingly popular for AI evaluation, yet research findings on the relationship between models’ generation and judgment abilities remain inconsistent.
Approach:	They propose a self-reference-guided evaluation strategy that leverages a model’s own answers as references to strengthen the correlation between generation and judgment abilities.
Outcome:	The proposed approach strengthens the correlation between model generation and judgment abilities and provides a reliable proxy for model selection in evaluation tasks.

Enhancing Neural Topic Model with Multi-Level Supervisions from Seed Words (2023.findings-acl)

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Yang Lin, Xin Gao, Xu Chu, Yasha Wang, Junfeng Zhao, Chao Chen

Second Language (Arabic) Acquisition of LLMs via Progressive Vocabulary Expansion (2025.acl-long)

Challenge:	Existing topic seed words are difficult to incorporate into topic models due to the semantic diversity of natural language.
Approach:	They propose a neural topic model enhanced with supervisions from seed words on word and document levels.
Outcome:	The proposed model outperforms the state-of-the-art seeded topic models in terms of topic quality and classification accuracy.

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Jianqing Zhu, Huang Huang, Zhihang Lin, Juhao Liang, Zhengyang Tang, Khalid Almubarak, Mosen Alharthi, Bang An, Juncai He, Xiangbo Wu, Fei Yu, Junying Chen, Ma Zhuoheng, Yuhao Du, He Zhang, Saied Alshahrani, Emad A. Alghamdi, Lian Zhang, Ruoyu Sun, Haizhou Li, Benyou Wang, Jinchao Xu

Text-Transport: Toward Learning Causal Effects of Natural Language (2023.emnlp-main)

Challenge:	In the evolving landscape of large language models, the predominant focus has been on English and Chinese.
Approach:	They propose to utilize Arabic-specific vocabulary in the tokenizer to accelerate decoding.
Outcome:	The proposed model achieves decent performance comparable to the best Arabic LLMs across various Arabic benchmarks.

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Victoria Lin, Louis-Philippe Morency, Eli Ben-Michael

Challenge:	Existing methods for causal inference require strong assumptions about the data, meaning the data from which one can estimate valid causal effects is not representative of the actual target domain of interest.
Approach:	They propose a method for estimation of causal effects from natural language under any text distribution using the notion of distribution shift.
Outcome:	The proposed method can be used to estimate causal effects from natural language under any text distribution.

SMARTMiner: Extracting and Evaluating SMART Goals from Low-Resource Health Coaching Notes (2025.findings-emnlp)

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Iva Bojic, Qi Chwen Ong, Stephanie Hilary Xinyi Ma, Lin Ai, Zheng Liu, Ziwei Gong, Julia Hirschberg, Andy Hau Yan Ho, Andy W. H. Khong

Improving Chinese Story Generation via Awareness of Syntactic Dependencies and Semantics (2022.aacl-short)

Challenge:	SMARTMiner extracts specific, measurable, attainable, relevant, time-bound (SMART) goals from unstructured health coaching notes.
Approach:	They propose a framework for extracting and evaluating specific, measurable, attainable, relevant, time-bound (SMART) goals from unstructured health coaching notes.
Outcome:	The framework extracts behavior change goal spans and categorizes their SMARTness.

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Henglin Huang, Chen Tang, Tyler Loakman, Frank Guerin, Chenghua Lin

PLATO-XL: Exploring the Large-scale Pre-training of Dialogue Generation (2022.findings-aacl)

Challenge:	Current neural models for Chinese story generation struggle to generate high-quality long text narratives due to ambiguity in syntactically parsing the Chinese language.
Approach:	They propose a framework that enhances the feature capturing mechanism by informing the generation model of dependencies between words and additionally augmenting the semantic representation learning through synonym denoising training.
Outcome:	The proposed framework outperforms the state-of-the-art Chinese generation models on all evaluation metrics, showing that it enhances dependency and semantic representation learning.

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Siqi Bao, Huang He, Fan Wang, Hua Wu, Haifeng Wang, Wenquan Wu, Zhihua Wu, Zhen Guo, Hua Lu, Xinxian Huang, Xin Tian, Xinchao Xu, Yingzhan Lin, Zheng-Yu Niu

MUR: Momentum Uncertainty guided Reasoning for Large Language Models (2026.acl-long)

Challenge:	Experimental results show PLATO-XL achieves state-of-the-art results across multiple conversational tasks.
Approach:	They propose to train PLATO-XL models with up to 11 billion parameters, trained on Chinese and English social media conversations.
Outcome:	The proposed model achieves state-of-the-art on multiple conversational tasks, verifying its potential as a foundation model of conversational AI.

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Hang Yan, Fangzhi Xu, Rongman Xu, Yifei Li, Jian Zhang, Haoran Luo, Xiaobao Wu, Anh Tuan Luu, Haiteng Zhao, Qika Lin, Jun Liu

Hierarchical Pointer Net Parsing (D19-1)

Challenge:	Existing methods for optimizing reasoning quality are limited by overthinking.
Approach:	They propose a method that allocates thinking budgets to critical reasoning steps by tracking and aggregating step-wise uncertainty over time.
Outcome:	The proposed method reduces computation by over 45% on average while improving accuracy by 0.33–3.46%.

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Linlin Liu, Xiang Lin, Shafiq Joty, Simeng Han, Lidong Bing

Challenge:	Existing approaches to parsing are greedy transition-based and globally optimized . however, the decision-making process is based on local information, causing error propagation to subsequent steps.
Approach:	They propose hierarchical pointer network parsers and apply them to dependency and sentence-level discourse parsing tasks.
Outcome:	The proposed method outperforms existing methods and sets new state-of-the-art methods on benchmark datasets.

GAM: Hierarchical Graph-based Agentic Memory for LLM Agents (2026.acl-long)

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Zhaofen Wu, Hanrong Zhang, Fulin Lin, Wujiang Xu, Xinran Xu, Yankai Chen, Henry Peng Zou, Shaowen Chen, Weizhi Zhang, Xue Liu, Philip S. Yu, Hongwei Wang

RAM-SD: Retrieval-Augmented Multi-agent framework for Sarcasm Detection (2026.acl-long)

Challenge:	Current unified stream-based memory systems facilitate context updates but remain vulnerable to interference from transient noise.
Approach:	They propose a hierarchical Graph-based Agentic Memory framework that explicitly decouples memory encoding from consolidation to resolve conflict between rapid context perception and stable knowledge retention.
Outcome:	The proposed framework outperforms state-of-the-art benchmarks on LoCoMo and LongDialQA.

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Ziyang Zhou, Ziqi Liu, Yan Wang, Yiming Lin, Yangbin Chen

PersLEARN: Research Training through the Lens of Perspective Cultivation (2023.acl-demo)

Challenge:	Existing approaches to sarcastic detection use a uniform reasoning strategy . existing approaches lack a framework to deal with the diverse analytical demands of sarcasm .
Approach:	They propose a Retrieval-Augmented Multi-Agent framework for Sarcasm Detection . the framework provides transparent and interpretable reasoning traces .
Outcome:	The proposed framework outperforms existing methods on four benchmarks and outperformed the strong GPT-4o+CoC baseline.

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Yu-Zhe Shi, Shiqian Li, Xinyi Niu, Qiao Xu, Jiawen Liu, Yifan Xu, Shiyu Gu, Bingru He, Xinyang Li, Xinyu Zhao, Zijian Zhao, Yidong Lyu, Zhen Li, Sijia Liu, Lin Qiu, Jinhao Ji, Lecheng Ruan, Yuxi Ma, Wenjuan Han, Yixin Zhu

Challenge:	PersLEARN is a tool designed to facilitate the cultivation of scientific perspectives . junior researchers struggle to identify the perspectives reflected in the literature and struggle to develop their own viewpoints.
Approach:	They propose a tool to facilitate the cultivation of scientific perspectives by interacting with a prompt-based model and allowing students to develop their own perspectives explicitly.
Outcome:	The proposed tool outperforms baseline approaches across multiple domains of literature from different perspectives.

SEEN: Structured Event Enhancement Network for Explainable Need Detection of Information Recall Assistance (2022.emnlp-main)

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You-En Lin, An-Zi Yen, Hen-Hsen Huang, Hsin-Hsi Chen

Structured Preference Optimization for Vision-Language Long-Horizon Task Planning (2025.emnlp-main)

Challenge:	Existing work on information recall focuses on reactively retrieving life events . but, proactively detecting the need for information recall services is rarely discussed .
Approach:	They propose a human-annotated life experience retelling dataset to detect the right time to trigger an information recall service.
Outcome:	The proposed system detects life event inconsistency, additional information in life events, and forgotten events.

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Xiwen Liang, Min Lin, Weiqi Ruan, Rongtao Xu, Yuecheng Liu, Jiaqi Chen, Bingqian Lin, Yuzheng Zhuang, Xiaodan Liang

ICLEval: Evaluating In-Context Learning Ability of Large Language Models (2025.coling-main)

Challenge:	Existing vision-language planning methods struggle with long-horizon reasoning in dynamic environments due to the difficulty of training models to generate high-quality reasoning processes.
Approach:	They propose a framework that enhances reasoning and action selection for long-horizon task planning through structured evaluation and optimized training.
Outcome:	The proposed framework outperforms existing methods on short-horizon tasks but struggles with long-horizon reasoning in dynamic environments.

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Wentong Chen, Yankai Lin, ZhenHao Zhou, HongYun Huang, YanTao Jia, Zhao Cao, Ji-Rong Wen

Verify Before You Commit: Towards Faithful Reasoning in LLM Agents via Self-Auditing (2026.acl-long)

Challenge:	Existing evaluation frameworks focus on language abilities and knowledge, often overlooking the assessment of ICL ability.
Approach:	They propose to evaluate the ICL ability of Large Language Models (LLMs) using the ICLEval benchmark.
Outcome:	The proposed benchmark demonstrates that ICL ability is universally present in different LLMs and model size is not the sole determinant of ICL efficacy.

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Wenhao Yuan, Chenchen Lin, Jian Chen, Jinfeng Xu, Xuehe Wang, Edith Cheuk-Han Ngai

REFLEX: Self-Refining Explainable Fact-Checking via Verdict-Anchored Style Control (2026.acl-long)

Challenge:	Existing approaches to reasoning faithfulness violate constraints, authors say . a science fantasy series and companion books are among the books .
Approach:	They propose a framework that enforces verification over internal belief states within the agent before action commitment, achieving faithful reasoning.
Outcome:	The proposed framework improves reasoning faithfulness while preserving competitive end-task performance.

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Chuyi Kong, Wei Gao, Jing Ma, Hongzhan Lin, Yuxi Sun

RESIN-11: Schema-guided Event Prediction for 11 Newsworthy Scenarios (2022.naacl-demo)

Challenge:	Existing methods for automated fact-checking often overlook deceptive misinformation styles in generated explanations.
Approach:	They propose a framework that explicitly controls reasoning style by anchoring explanations to the predicted verdict.
Outcome:	The proposed framework achieves state-of-the-art under LLaMA-series models with 465 samples.

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Xinya Du, Zixuan Zhang, Sha Li, Pengfei Yu, Hongwei Wang, Tuan Lai, Xudong Lin, Ziqi Wang, Iris Liu, Ben Zhou, Haoyang Wen, Manling Li, Darryl Hannan, Jie Lei, Hyounghun Kim, Rotem Dror, Haoyu Wang, Michael Regan, Qi Zeng, Qing Lyu, Charles Yu, Carl Edwards, Xiaomeng Jin, Yizhu Jiao, Ghazaleh Kazeminejad, Zhenhailong Wang, Chris Callison-Burch, Mohit Bansal, Carl Vondrick, Jiawei Han, Dan Roth, Shih-Fu Chang, Martha Palmer, Heng Ji

MMTE: Corpus and Metrics for Evaluating Machine Translation Quality of Metaphorical Language (2024.emnlp-main)

Challenge:	Existing methods for event prediction are incomplete and noisy.
Approach:	They propose to use news-related event schemas to extract newsworthy events . they build a demo website and include a video demonstrating the framework .
Outcome:	The proposed framework can be applied to a wide variety of newsworthy scenarios.

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Shun Wang, Ge Zhang, Han Wu, Tyler Loakman, Wenhao Huang, Chenghua Lin

Train a Unified Multimodal Data Quality Classifier with Synthetic Data (2025.findings-emnlp)

Challenge:	Existing evaluation methods focus on fluency and factual reliability, while neglecting figurative quality.
Approach:	They propose a set of human evaluation metrics focused on the translation of figurative language and a parallel metaphor corpus generated by post-editing.
Outcome:	The proposed evaluation protocol estimates four aspects of MT: Metaphorical Equivalence, Emotion, Authenticity, and Quality.

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Weizhi Wang, Rongmei Lin, Shiyang Li, Colin Lockard, Ritesh Sarkhel, Sanket Lokegaonkar, Jingbo Shang, Xifeng Yan, Nasser Zalmout, Xian Li

MAC-Reasoner: A Multi-Agent Collaborative Framework for Enhancing Logical Reasoning in Large Language Models (2026.findings-acl)

Challenge:	Multimodal Large Language Models are pre-trained on image-text caption data and interleaved document data.
Approach:	They propose to train an efficient MLLM as a Unified Mulitmodal Data Quality Classifier to filter image-text caption and interleaved data.
Outcome:	The proposed method enables efficient creation of sample-score pairs for caption and interleaved data to train UniFilter.

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Yehua Lin, Liping Zheng, Yin Chen

Beyond Full Fine-tuning: Harnessing the Power of LoRA for Multi-Task Instruction Tuning (2024.lrec-main)

Challenge:	Existing methods for logical reasoning are limited to a limited number of tasks.
Approach:	They propose a multi-agent framework that constructs a Logic-Augmented Context to enhance LLMs’ reasoning by a translator agent.
Outcome:	The proposed framework improves on three backbone LLMs on four challenging benchmarks and shows consistent and robust improvements over baselines.

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Chunlei Xin, Yaojie Lu, Hongyu Lin, Shuheng Zhou, Huijia Zhu, Weiqiang Wang, Zhongyi Liu, Xianpei Han, Le Sun

Extracting Financial Events from Raw Texts via Matrix Chunking (2024.lrec-main)

Challenge:	Low-Rank Adaptation (LoRA) is a parameter-efficient fine-tuning algorithm for large-scale language models.
Approach:	They conduct a systematic study of Low-Rank Adaptation (LoRA) on diverse tasks and rich resources with different learning capacities.
Outcome:	The proposed algorithm can achieve remarkable performance in high-resource and multi-task scenarios, even comparable to full fine-tuning.

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Yusheng Huang, Ning Hu, Kunping Li, Nan Wang, Zhouhan Lin

Chunking

Challenge:	Event Extraction (EE) is widely used in the Chinese financial field to provide valuable structured information.
Approach:	They propose a task which extracts financial events from raw texts and an efficient method called MACK.
Outcome:	The proposed method is fault-tolerant and can visualize interactions among text components.

Honkling: In-Browser Personalization for Ubiquitous Keyword Spotting (D19-3)

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Jaejun Lee, Raphael Tang, Jimmy Lin

Challenge:	keyword spotting systems are used for simple commands recognition on devices . however, voice-enabled web applications are few and far between . a prominent drawback is that most of these systems perform speech recognition in the cloud .
Approach:	Honkling is a JavaScript-based keyword spotting system that can be deployed on user devices.
Outcome:	Honkling is a JavaScript-based keyword spotting system that can be deployed on user devices.

UReader: Universal OCR-free Visually-situated Language Understanding with Multimodal Large Language Model (2023.findings-emnlp)

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Jiabo Ye, Anwen Hu, Haiyang Xu, Qinghao Ye, Ming Yan, Guohai Xu, Chenliang Li, Junfeng Tian, Qi Qian, Ji Zhang, Qin Jin, Liang He, Xin Lin, Fei Huang

HyperHatePrompt: A Hypergraph-based Prompting Fusion Model for Multimodal Hate Detection (2025.coling-main)

Challenge:	Existing studies for visually-situated language understanding have shown shallow zero-shot visual text recognition ability when fed a low-resolution image with salient text information.
Approach:	They propose a model for universal OCR-free visually-situated language understanding based on the Multimodal Large Language Model (MLLM) their model is jointly finetuned on a wide range of visually situated language understanding tasks via a unified instruction format.
Outcome:	The proposed model achieves state-of-the-art ocr-free performance in 8 out of 10 visually-situated language understanding tasks across 5 domains: documents, tables, charts, natural images, and webpage screenshots.

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Bo Xu, Erchen Yu, Jiahui Zhou, Hongfei Lin, Linlin Zong

Towards IP Intelligence: Benchmarking Large Language Models on Intellectual Property Knowledge and Practice (2026.findings-acl)

Challenge:	Existing models for multimodal hate detection lack implicit hateful cues, cross-modal-induced hate, and diversity of hate target groups.
Approach:	They propose a hypergraph-based prompting fusion model that uses LLMs to generate hate cue prompts and hypergraph learning to merge multimodal hate features.
Outcome:	The proposed model outperforms state-of-the-art models on two benchmark datasets showing that it can detect hate content across multiple modalities.

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Qiyao Wang, Guhong Chen, Hongbo Wang, Huaren Liu, Minghui Zhu, Zhifei Qin, Li Linwei, Yilin Yue, Shiqiang Wang, Jiayan Li, Wu Yihang, Ziqiang Liu, Longze Chen, Run Luo, Liyang Fan, Jiaming Li, Lei Zhang, Kan Xu, Hamid Alinejad-Rokny, Chengming Li, Shiwen Ni, Yuan Lin, Min Yang

Can LLMs Hear the Dogwhistle? (2026.findings-acl)

Challenge:	Existing datasets and benchmarks focus only on patents or cover limited aspects of the IP field, lacking alignment with real-world scenarios.
Approach:	They propose a bilingual IP task taxonomy and a large-scale bilingual benchmark to evaluate LLMs in real-world IP practice.
Outcome:	The proposed model achieves only 75.8% accuracy, indicating room for improvement . open-source IP and law-oriented models lag behind closed-source general-purpose models .

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Yifan Liu, Yi Lin, Xinwei Guo, Ziwei Wang, Jiaxin Zhang, Guanhua Chen, Haiyan Wu, Xiangyu Zhao, Xin Yao, Xuetao Wei

Leveraging Slot Descriptions for Zero-Shot Cross-Domain Dialogue StateTracking (2021.naacl-main)

Challenge:	Existing safety benchmarks focus on explicitly harmful content, but ignore context-dependent expressions such as dogwhistles.
Approach:	They propose a benchmark for evaluating LLM safety under dogwhistle-driven prompts . their findings expose a blind spot in current safety evaluation practices .
Outcome:	The proposed benchmark compared safety performance with toxic terms using dogwhistle-driven prompts.

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Zhaojiang Lin, Bing Liu, Seungwhan Moon, Paul Crook, Zhenpeng Zhou, Zhiguang Wang, Zhou Yu, Andrea Madotto, Eunjoon Cho, Rajen Subba

SLIDE: A Framework Integrating Small and Large Language Models for Open-Domain Dialogues Evaluation (2024.findings-acl)

Challenge:	Existing models for zero-shot cross-domain dialogue state tracking require in-domain data to model a new domain.
Approach:	They propose a slot descriptions enhanced generative approach for zero-shot cross-domain DST by encoding a dialogue context and a slots with a pre-trained encoder and generating slot value in auto-regressive manner.
Outcome:	The proposed model significantly improves state-of-the-art results in zero-shot cross-domain setting.

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Kun Zhao, Bohao Yang, Chen Tang, Chenghua Lin, Liang Zhan

OFA: A Framework of Initializing Unseen Subword Embeddings for Efficient Large-scale Multilingual Continued Pretraining (2024.findings-naacl)

Challenge:	Existing approaches to evaluate open domain dialogues have a one-to-many problem . existing approaches lack commonsense reasoning biases and perform poorly in domain-specific scenarios.
Approach:	They propose a framework that leverages both a small, specialised model and LLMs for the evaluation of open-domain dialogues.
Outcome:	The proposed framework achieves state-of-the-art performance in both classification and evaluation tasks and exhibits better correlation with human judgements.

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Yihong Liu, Peiqin Lin, Mingyang Wang, Hinrich Schuetze

Does the Correctness of Factual Knowledge Matter for Factual Knowledge-Enhanced Pre-trained Language Models? (2023.emnlp-main)

Challenge:	Existing methods to pretrain multilingual models are limited by the number of embedding parameters and the complexity of the model.
Approach:	They propose a framework that initializes the embeddings of unseen subwords and can adapt a model to multiple languages.
Outcome:	The proposed framework can adapt a pre-trained model to multiple languages efficiently and effectively.

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Boxi Cao, Qiaoyu Tang, Hongyu Lin, Xianpei Han, Le Sun

Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models (2025.acl-long)

Challenge:	Existing work neither proves that pre-trained models successfully learn the injected factual knowledge nor proves there is a causal relation between injected knowledge and downstream performance improvements.
Approach:	They propose a counterfactual-based analysis framework to explore the causal effects of factual knowledge injection on the performance of language models within pretrain-finetune paradigm.
Outcome:	The proposed framework shows that factual knowledge injection is successful but correctness of injected knowledge only has limited effect on the models’ downstream performance.

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Zihan Qiu, Zeyu Huang, Bo Zheng, Kaiyue Wen, Zekun Wang, Rui Men, Ivan Titov, Dayiheng Liu, Jingren Zhou, Junyang Lin

JarviX: A LLM No code Platform for Tabular Data Analysis and Optimization (2023.emnlp-industry)

Challenge:	Existing Mixture-of-Experts training frameworks use a micro-batch to calculate LBL . micro-batches are restricted to a single sequence, preventing expert specialization .
Approach:	They propose to use a global-batch to loosen the load balance constraint for MoEs models . they propose to synchronize fi across micro-batches and then use it to calculate the LBL .
Outcome:	The proposed global-batch LBL improves the domain specialization of experts . the micro-battery LBL is almost at the sequence level, and the router is pushed to distribute the token evenly .

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Shang-Ching Liu, ShengKun Wang, Tsungyao Chang, Wenqi Lin, Chung-Wei Hsiung, Yi-Chen Hsieh, Yu-Ping Cheng, Sian-Hong Luo, Jianwei Zhang

MemeArena: Automating Context-Aware Unbiased Evaluation of Harmfulness Understanding for Multimodal Large Language Models (2025.emnlp-main)

Challenge:	Tabular data analysis is an important application task of large language models, but advanced models are not yet on par with expert level performance.
Approach:	They propose to employ Large Language Models to facilitate an automated guide and execute high-precision data analyzes on tabular datasets.
Outcome:	The proposed framework is based on large language models and an automated machine learning pipeline for predictive modeling.

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Zixin Chen, Hongzhan Lin, Kaixin Li, Ziyang Luo, Yayue Deng, Jing Ma

Using Intermediate Representations to Solve Math Word Problems (P18-1)

Challenge:	Existing evaluation approaches focus on mLLMs’ detection accuracy for binary classification tasks, which often fail to reflect the in-depth interpretive nuance of harmfulness across diverse contexts.
Approach:	They propose an agent-based arena-style evaluation framework that provides context-aware and unbiased assessment for mLLMs’ understanding of multimodal harmfulness.
Outcome:	The proposed framework reduces evaluation biases of judge agents and provides unbiased comparisons of mLLMs’ abilities to interpret multimodal harmfulness.

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Danqing Huang, Jin-Ge Yao, Chin-Yew Lin, Qingyu Zhou, Jian Yin

Numerical Reasoning

Challenge:	Existing approaches to solving math word problems do not include higher-order operations that cannot be explicitly represented in equations.
Approach:	They propose an iterative labeling framework that generates intermediate forms and executes them to obtain the final answers.
Outcome:	The proposed model outperforms existing models in solving math word problems.

“Knowing When You Don’t Know”: A Multilingual Relevance Assessment Dataset for Robust Retrieval-Augmented Generation (2024.findings-emnlp)

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Nandan Thakur, Luiz Bonifacio, Crystina Zhang, Odunayo Ogundepo, Ehsan Kamalloo, David Alfonso-Hermelo, Xiaoguang Li, Qun Liu, Boxing Chen, Mehdi Rezagholizadeh, Jimmy Lin

BattleAgent: Multi-modal Dynamic Emulation on Historical Battles to Complement Historical Analysis (2024.emnlp-demo)

Challenge:	Prior work on RAG grounds Large Language Models to reduce factual hallucinations lacks a comprehensive evaluation of different language families.
Approach:	They propose a human-annotated dataset for evaluating LLM robustness in RAG . they find that most models struggle to balance the two capacities .
Outcome:	The proposed dataset includes both a non-relevant and a relevant subset.

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Shuhang Lin, Wenyue Hua, Lingyao Li, Che-Jui Chang, Lizhou Fan, Jianchao Ji, Hang Hua, Mingyu Jin, Jiebo Luo, Yongfeng Zhang

Chain of Condition: Construct, Verify and Solve Conditions for Conditional Question Answering (2024.findings-emnlp)

Challenge:	Recent advances in large language models have demonstrated impressive reasoning capabilities, indicating their potential to serve as the foundation for agents.
Approach:	They propose a detailed emulation system that combines large vision-language model and multi-agent system to emulate dynamic interactions between multiple agents over a period of time.
Outcome:	The proposed system combines large vision-language model and multi-agent system to emulate dynamic interactions between agents and their environments over a period of time.

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Jiuheng Lin, Yuxuan Lai, Yansong Feng

LOFT: Scalable and More Realistic Long-Context Evaluation (2025.findings-naacl)

Challenge:	Existing methods for conditional question answering struggle with finding probable answers and identifying missing conditions.
Approach:	They propose a conditional question answering prompting approach that first identifies all conditions and constructs their logical relationships explicitly according to the document, then verifyes whether these conditions are satisfied and finally solves the logical expression to indicate any missing conditions.
Outcome:	The proposed method outperforms existing prompting baselines on two CQA benchmark datasets and can facilitate GPT-3.5-Turbo or GPT-4 to outperFORM all existing supervised models.

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Jinhyuk Lee, Anthony Chen, Zhuyun Dai, Dheeru Dua, Devendra Singh Sachan, Michael Boratko, Yi Luan, Séb Arnold, Vincent Perot, Siddharth Dalmia, Hexiang Hu, Xudong Lin, Panupong Pasupat, Aida Amini, Jeremy R. Cole, Sebastian Riedel, Iftekhar Naim, Ming-Wei Chang, Kelvin Guu

Unsupervised Domain Adaptation Method with Semantic-Structural Alignment for Dependency Parsing (2021.findings-emnlp)

Challenge:	Long-context language models (LCLMs) can be used to perform tasks traditionally reliant on external tools like retrieval systems or databases.
Approach:	They propose a benchmark to evaluate LCLMs' performance on in-context retrieval and reasoning tasks using a set of tokens.
Outcome:	The proposed model outperforms state-of-the-art retrieval and RAG systems on in-context retrieval tasks while still requiring prompting strategies.

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Boda Lin, Mingzheng Li, Si Li, Yong Luo

ObfusLM: Privacy-preserving Language Model Service against Embedding Inversion Attacks (2025.acl-long)

Challenge:	Existing methods for dependency parsing are often of the pseudo-annotation type, but they fail to consider the change of model structure for domain adaptation.
Approach:	They propose a method that accomplishes unsupervised cross-domain dependency parsing without using labeled data.
Outcome:	The proposed method achieves consistent performance improvement on CODT1 and CTB9 domains.

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Yu Lin, Ruining Yang, Yunlong Mao, Qizhi Zhang, Jue Hong, Quanwei Cai, Ye Wu, Huiqi Liu, Zhiyu Chen, Bing Duan, Sheng Zhong

DeepSolution: Boosting Complex Engineering Solution Design via Tree-based Exploration and Bi-point Thinking (2025.acl-long)

Challenge:	Recent studies show that obfuscation techniques for MLaaS are susceptible to embedding inversion attacks (EIAs).
Approach:	They propose a model obfuscation framework that protects client inputs from embedding inversion attacks by obliviously obbing models.
Outcome:	The proposed framework outperforms existing works in utility by 10% with a nearly 80% resistance rate against embedding inversion attacks.

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Zhuoqun Li, Haiyang Yu, Xuanang Chen, Hongyu Lin, Yaojie Lu, Fei Huang, Xianpei Han, Yongbin Li, Le Sun

Challenge:	Existing studies in retrieval-augmented generation (RAG) do not sufficiently address the design of complex engineering solutions.
Approach:	They propose a retrieval-augmented generation system that leverages tree-based exploration and bi-point thinking mechanism to generate reliable solutions.
Outcome:	Experiments show that the proposed system achieves state-of-the-art (SOTA) performance on the SolutionBench, highlighting its potential to enhance the automation and reliability of complex engineering solution design in real-world applications.

Training LLMs to be Better Text Embedders through Bidirectional Reconstruction (2025.emnlp-main)

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Chang Su, Dengliang Shi, Siyuan Huang, Jintao Du, Changhua Meng, Yu Cheng, Weiqiang Wang, Zhouhan Lin

X-LeBench: A Benchmark for Extremely Long Egocentric Video Understanding (2025.findings-emnlp)

Challenge:	Existing text embedding approaches often leverage the embeddment of the final token, typically a reserved special token such as ‘[EOS]‘.
Approach:	They propose to add a new training stage before contrastive learning to enrich the semantics of the final token embedding.
Outcome:	The proposed training stage improves performance on the Massive Text Embedding Benchmark (MTEB), achieving new state-of-the-art results across different LLM base models and scales.

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Wenqi Zhou, Kai Cao, Hao Zheng, Yunze Liu, Xinyi Zheng, Miao Liu, Per Ola Kristensson, Walterio W. Mayol-Cuevas, Fan Zhang, Weizhe Lin, Junxiao Shen

Enhancing Pre-Trained Generative Language Models with Question Attended Span Extraction on Machine Reading Comprehension (2024.emnlp-main)

Challenge:	Existing benchmark datasets focus on short to moderately long videos, leaving a substantial gap in evaluating extensive, ultra-long egocentric video recordings.
Approach:	X-LeBench is a benchmark dataset designed to evaluate long egocentric video recordings . it uses a life-logging pipeline to produce realistic, coherent daily plans .
Outcome:	X-LeBench is a new benchmark dataset designed to evaluate long-form egocentric video understanding . the approach produces realistic, coherent daily plans aligned with real-world video data .

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Lin Ai, Zheng Hui, Zizhou Liu, Julia Hirschberg

CLEVE: Contrastive Pre-training for Event Extraction (2021.acl-long)

Challenge:	Extractive Machine Reading Comprehension (MRC) is a challenging field in the field of Natural Language Processing.
Approach:	They propose a Question-Attended Span Extraction module to address the limitations of generative approaches for extractive machine reading comprehension (MRC) . module significantly enhances performance of pre-trained generative language models, enabling them to surpass the extractive capabilities of advanced Large Language Models (LLMs)
Outcome:	The QASE module surpasses state-of-the-art models in few-shot settings.

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Ziqi Wang, Xiaozhi Wang, Xu Han, Yankai Lin, Lei Hou, Zhiyuan Liu, Peng Li, Juanzi Li, Jie Zhou

Using Customer Service Dialogues for Satisfaction Analysis with Context-Assisted Multiple Instance Learning (D19-1)

Challenge:	Existing EE methods do not model event characteristics from large unsupervised data.
Approach:	They propose a contrastive pre-training framework for event extraction to better learn event knowledge from large unsupervised data and their semantic structures.
Outcome:	The proposed framework improves on ACE 2005 and MAVEN datasets on event extraction tasks.

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Kaisong Song, Lidong Bing, Wei Gao, Jun Lin, Lujun Zhao, Jiancheng Wang, Changlong Sun, Xiaozhong Liu, Qiong Zhang

Making Science Simple: Corpora for the Lay Summarisation of Scientific Literature (2022.emnlp-main)

Challenge:	Existing studies fail to provide comprehensive service satisfaction analysis . Existing models fail to include satisfaction polarity classification and sentimental utterance identification .
Approach:	They propose a model that predicts customer sentiments and aggregates them into service satisfaction polarity.
Outcome:	The proposed model predicts customer sentiments and aggregates them into service satisfaction polarity and reasoning clues.

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Tomas Goldsack, Zhihao Zhang, Chenghua Lin, Carolina Scarton

Disentangling Preference Representation and Text Generation for Efficient Individual Preference Alignment (2025.coling-main)

Challenge:	Existing datasets for lay summarisation are limited in size and scope, hindering the development of data-driven approaches.
Approach:	They propose to use two new datasets for the lay summarisation of biomedical research articles to characterise their lay summaries.
Outcome:	The proposed datasets are compared with existing datasets and show they can be leveraged to support different audiences and applications.

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Jianfei Zhang, Jun Bai, Bei Li, Yanmeng Wang, Rumei Li, Chenghua Lin, Wenge Rong

COPR: Continual Human Preference Learning via Optimal Policy Regularization (2025.findings-acl)

Challenge:	Human values are inherently diverse, making it insufficient to align LLMs solely with general preferences.
Approach:	They propose a flexible paradigm for individual preference alignment that disentangles preference representation from text generation in LLMs.
Outcome:	The proposed method produces aligned quality and better than PEFT-based methods while reducing training time for each new individual preference by 80% to 90%.

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Han Zhang, Lin Gui, Yu Lei, Yuanzhao Zhai, Yehong Zhang, Zhuo Zhang, Yulan He, Hui Wang, Yue Yu, Kam-Fai Wong, Bin Liang, Ruifeng Xu

Importance Estimation from Multiple Perspectives for Keyphrase Extraction (2021.emnlp-main)

Challenge:	Reinforcement Learning from Human Feedback (RLHF) is effective for aligning Large Language Models with human preferences, but its complex process limits its ability to continually learn human feedback.
Approach:	They propose a non-RL offline method to convert historical optimal policies into optimization constraints when continually learning new preferences.
Outcome:	The proposed method outperforms strong CL baselines in terms of reward-based evaluations and human assessment.

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Mingyang Song, Liping Jing, Lin Xiao

Can Large Language Models Translate Unseen Languages in Underrepresented Scripts? (2025.emnlp-main)

Challenge:	Existing keyphrase extraction methods focus on the part of phrase that is important . experimental results show that KIEMP outperforms existing keyphrase extracting methods .
Approach:	They propose to estimate the importance of keyphrase from multiple perspectives using a chunking module, ranking module and matching module.
Outcome:	The proposed method outperforms the state-of-the-art keyphrase extraction methods on six benchmark datasets.

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Dianqing Lin, null Aruukhan, Hongxu Hou, Shuo Sun, Wei Chen, Yichen Yang, Guo Dong Shi

When Language Model Meets Private Library (2022.findings-emnlp)

Challenge:	Large language models (LLMs) have demonstrated impressive performance in machine translation, but struggle with unseen low-resource languages.
Approach:	They propose a benchmark to evaluate translation for Mongolian and Yi using linguistic resources.
Outcome:	The proposed model can translate Mongolian (in traditional script) and Yi with the help of linguistic resources, but is limited in its ability to handle these languages effectively.

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Daoguang Zan, Bei Chen, Zeqi Lin, Bei Guan, Wang Yongji, Jian-Guang Lou

Leveraging LLMs for Synthesizing Training Data Across Many Languages in Multilingual Dense Retrieval (2024.naacl-long)

Challenge:	Existing language models have been pre-trained on large-scale code corpora and generate decent code snippets.
Approach:	They propose a framework that can provide pre-trained language models with the ability to generate code using private libraries.
Outcome:	The proposed framework can generate code using private libraries using off-the-shelf language models or pre-trained models on code corpus containing API information.

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Nandan Thakur, Jianmo Ni, Gustavo Hernandez Abrego, John Wieting, Jimmy Lin, Daniel Cer

A Compare-and-contrast Multistage Pipeline for Uncovering Financial Signals in Financial Reports (2023.acl-long)

Challenge:	et al., 2020: performance of dense retrieval models in multilingual retrieval is limited due to uneven and scarce training data available across multiple languages.
Approach:	They propose a synthetic retrieval training dataset containing 33 languages for fine-tuning multilingual retrievers without human supervision.
Outcome:	The proposed model outperforms human-supervised retrieval models on three retrieval benchmarks.

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Jia-Huei Ju, Yu-Shiang Huang, Cheng-Wei Lin, Che Lin, Chuan-Ju Wang

Challenge:	Recent advances in natural language processing (NLP) have included attempts to efficiently and effectively comprehend lengthy financial documents.
Approach:	They propose a signal-highlighting task that analyzes relationships between financial reports . they also create and publicly release a human-annotated dataset for the task .
Outcome:	The proposed pipeline is based on a human-annotated dataset and validates its effectiveness.

None of the Above, Less of the Right Parallel Patterns in Human and LLM Performance on Multi-Choice Questions Answering (2025.findings-acl)

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Zhi Rui Tam, Cheng-Kuang Wu, Chieh-Yen Lin, Yun-Nung Chen

Towards Transferable Personality Representation Learning based on Triplet Comparisons and Its Applications (2025.emnlp-main)

Challenge:	Multiple-choice exam questions with “None of the above” (NA) options have been extensively studied in educational testing . however, their impact on Large Language Models (LLMs) evaluation remains underexplored .
Approach:	They conduct systematic experiments with 28 LLMs on the MMLU benchmark to examine how NA options affect model performance and confidence calibration.
Outcome:	The results highlight important implications for benchmark design and raise questions about LLMs’ ability to handle uncertainty in real-world applications.

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Kai Tang, Rui Wang, Renyu Zhu, Minmin Lin, Xiao Ding, Tangjie Lv, Changjie Fan, Runze Wu, Haobo Wang

CMQCIC-Bench: A Chinese Benchmark for Evaluating Large Language Models in Medical Quality Control Indicator Calculation (2025.findings-acl)

Challenge:	Existing methods for personality analysis treat corpus as a single unit for classification, but this approach presents several challenges.
Approach:	They propose a task paradigm for text-based personality representation learning that uses a triplet personality trend comparison dataset to learn single-sentence personality embeddings with desirable metric properties.
Outcome:	The proposed model significantly boosts performance across various applications, including personality detection, personality retrieval, and emotion translation prediction.

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Guangya Yu, Yanhao Li, Zongying Jiang, Yuxiong Jin, Li Dai, Yupian Lin, Ruihui Hou, Weiyan Zhang, Yongqi Fan, Qi Ye, Jingping Liu, Tong Ruan

SEE: Signal Embedding Energy for Quantifying Noise Interference in Large Audio Language Models (2026.acl-long)

Challenge:	Medical quality control indicators are essential to assess the qualifications of healthcare institutions for medical services.
Approach:	They propose a Chinese electronic medical records-based dataset for MQCIC and propose CF-IR method that disentangles clinical fact verification and inferential rule reasoning actions.
Outcome:	The proposed method outperforms Chain-of-Thought methods on 20 representative LLMs, covering general and medical models.

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Yuanhe Zhang, Jiayu Tian, Yibo Zhang, Shilinlu Yan, Liang Lin, Zhenhong Zhou, Li Sun, Sen Su

Towards Quantifiable Dialogue Coherence Evaluation (2021.acl-long)

Challenge:	Existing studies on noise lack quantitative analysis and rely on intuition and empirical observation, thus failing to understand practical robustness.
Approach:	They propose a method for quantifying the impact of noise intensity on LALM inputs by using a structured activation subspace derived from the model's internal representations.
Outcome:	The proposed method outperforms existing denoising methods and demonstrates that noise is perceived more accurately than raw audio features.

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Zheng Ye, Liucun Lu, Lishan Huang, Liang Lin, Xiaodan Liang

Text Smoothing: Enhance Various Data Augmentation Methods on Text Classification Tasks (2022.acl-short)

Challenge:	Existing automatic dialogue coherence evaluation metrics are expensive and high-latency, which cannot meet the requirements of a dialogue system.
Approach:	They propose a framework to train a quantifiable dialogue coherence metric that can reflect actual human rating standards.
Outcome:	Experimental results show that the model trained by QuantiDCE presents stronger correlations with human judgements than the other state-of-the-art metrics.

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Xing Wu, Chaochen Gao, Meng Lin, Liangjun Zang, Songlin Hu

Self-distilled Transitive Instance Weighting for Denoised Distantly Supervised Relation Extraction (2023.findings-emnlp)

Challenge:	Experimental results show text smoothing outperforms data augmentation methods by a substantial margin.
Approach:	They propose to use a masked language model to convert a token to a smoothed representation by converting a sentence from its one-hot representation to 'controllable smoothes' they propose to combine text smoothing with other data augmentation methods to achieve better performance.
Outcome:	The proposed method outperforms mainstream data augmentation methods by a substantial margin on different datasets in a low-resource regime.

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Xiangyu Lin, Weijia Jia, Zhiguo Gong

DANCER: Entity Description Augmented Named Entity Corrector for Automatic Speech Recognition (2024.lrec-main)

Challenge:	Existing approaches to reducing wrongly labeled instances are based on a bag-level setting . however, sentence-level training is vulnerable to the noise brought by DS, which limits its application.
Approach:	They propose a transitive instance weighting mechanism integrated with the self-distilled BERT backbone to generate dynamic instance weights for denoised sentence-level training.
Outcome:	The proposed method can tackle wrongly labeled instances and prevent overfitting.

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Yi-Cheng Wang, Hsin-Wei Wang, Bi-Cheng Yan, Chi-Han Lin, Berlin Chen

Answer-driven Deep Question Generation based on Reinforcement Learning (2020.coling-main)

Challenge:	End-to-end automatic speech recognition systems suffer from mistranscription of domain-specific phrases, such as named entities.
Approach:	They propose a named entity correction model that leverages phonetic con-fusion to mitigate phonetic confusion.
Outcome:	The proposed model outperforms the existing model on AISHELL-1 and Homophone datasets.

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Liuyin Wang, Zihan Xu, Zibo Lin, Haitao Zheng, Ying Shen

Thinking with Reasoning Skills: Fewer Tokens, More Accuracy (2026.acl-industry)

Challenge:	Existing methods for deep question generation focus on enhancing document representations, but little attention is paid to the answer information.
Approach:	They propose a deep question generation model that makes better use of the target answer as a guidance to facilitate question generation.
Outcome:	The proposed model outperforms state-of-the-art models in automatic and human evaluations on the hotpotQA dataset.

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Guangxiang Zhao, Qilong Shi, Xusen Xiao, Xiangzheng Zhang, Tong Yang, Lin Sun

WAFFLE: Fine-tuning Multi-Modal Model for Automated Front-End Development (2025.acl-long)

Challenge:	Reasoning LLMs often spend tokens on long intermediate reasoning traces when solving new problems.
Approach:	They propose to store reusable reasoning skills distilled from extensive deliberation and trial-and-error exploration and retrieve these skills at inference time to guide future reasoning.
Outcome:	The proposed approach reduces reasoning tokens while improving overall performance on coding and mathematical reasoning tasks.

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Shanchao Liang, Nan Jiang, Shangshu Qian, Lin Tan

XAMPLER: Learning to Retrieve Cross-Lingual In-Context Examples (2025.findings-naacl)

Challenge:	Large Language Models (LLMs) have shown promise in generating source code, but two major challenges persist in UI-to-HTML code generation: (1) effectively representing HTML’s hierarchical structure for LLMs; and (2) bridging the gap between the visual nature of UI designs and the text-based format of HTML code.
Approach:	They propose a structure-aware attention mechanism that uses a contrastive fine-tuning approach to align LLMs’ understanding of UI images and HTML code.
Outcome:	The proposed model outperforms existing methods on the WebSight-Test and Design2Code benchmarks.

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Peiqin Lin, Andre Martins, Hinrich Schuetze

Speech-Text Pre-training for Spoken Dialog Understanding with Explicit Cross-Modal Alignment (2023.acl-long)

Challenge:	XAMPLER: Cross-Lingual Example Retrieval is a cross-lingual example retrieval method . large language models (LLMs) have emerged as effective in-context learning methods .
Approach:	They propose a method to train a multilingual model with annotated English examples . they use annotized English data to train the model and use it to train other languages .
Outcome:	XAMPLER: Cross-Lingual Example Retrieval improves in-context learning in English . it trains a retriever based on a multilingual small language model using annotated English examples .

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Tianshu Yu, Haoyu Gao, Ting-En Lin, Min Yang, Yuchuan Wu, Wentao Ma, Chao Wang, Fei Huang, Yongbin Li

CIF-Bench: A Chinese Instruction-Following Benchmark for Evaluating the Generalizability of Large Language Models (2024.findings-acl)

Challenge:	Existing speech-text pre-training methods are limited to one or two specific tasks, despite their success in speech-language processing tasks.
Approach:	They propose a temporal position prediction task to capture the speech-text alignment . they use a textual dialog pre-training task to generalize a response selection task .
Outcome:	The proposed model is superior in learning speech-text alignment and multi-turn dialog context.

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Yizhi Li, Ge Zhang, Xingwei Qu, Jiali Li, Zhaoqun Li, Noah Wang, Hao Li, Ruibin Yuan, Yinghao Ma, Kai Zhang, Wangchunshu Zhou, Yiming Liang, Lei Zhang, Lei Ma, Jiajun Zhang, Zuowen Li, Wenhao Huang, Chenghua Lin, Jie Fu

Embedding-based In-Context Prompt Training for Enhancing LLMs as Text Encoders (2026.findings-acl)

Challenge:	a recent study shows that large language models have limited generalization in low-resource languages like Chinese.
Approach:	They propose to evaluate the zero-shot generalizability of large language models to the Chinese language . they release only half of the dataset publicly, with the remainder kept private .
Outcome:	The Chinese Instruction-Following Benchmark evaluates the generalizability of LLMs to the Chinese language.

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Ailiang Lin, Zhuoyun Li, Keyu Mao, Kotaro Funakoshi, Manabu Okumura

TECHS: Temporal Logical Graph Networks for Explainable Extrapolation Reasoning (2023.acl-long)

Challenge:	Large language models (LLMs) have been widely explored for embedding generation.
Approach:	They propose an embedding-based in-context prompt training strategy that leverages in-constext learning to generate high-quality embeddables while reducing computational burden.
Outcome:	The proposed method surpasses models trained on publicly available retrieval data and achieves state-of-the-art embedding performance on the MTEB benchmark.

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Qika Lin, Jun Liu, Rui Mao, Fangzhi Xu, Erik Cambria

Know Thy Strengths: Comprehensive Dialogue State Tracking Diagnostics (2022.findings-emnlp)

Challenge:	Existing frameworks for extrapolating knowledge graphs are incomplete and do not represent real-world knowledge.
Approach:	They propose an explainable extrapolation reasoning framework that integrates propositional reasoning and first-order reasoning by introducing a reasoning graph that iteratively expands to find the answer.
Outcome:	The proposed framework outperforms state-of-the-art baselines in explaining future facts based on past counterparts.

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Hyundong Cho, Chinnadhurai Sankar, Christopher Lin, Kaushik Sadagopan, Shahin Shayandeh, Asli Celikyilmaz, Jonathan May, Ahmad Beirami

START: Self-taught Reasoner with Tools (2025.emnlp-main)

Challenge:	Recent studies have revealed the vulnerability of dialogue state tracking models to distributional shifts, resulting in poor performance.
Approach:	They present a toolkit for standardized and comprehensive dialogue state tracking diagnoses that provides a richer summary of strengths and weaknesses.
Outcome:	The proposed toolkit shows that different classes of DST models have clear strengths and weaknesses, while generation models are more promising for handling language variety and span-based classification models are robust to unseen entities.

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Chengpeng Li, Mingfeng Xue, Zhenru Zhang, Jiaxi Yang, Beichen Zhang, Bowen Yu, Binyuan Hui, Junyang Lin, Xiang Wang, Dayiheng Liu

SwarmAgentic: Towards Fully Automated Agentic System Generation via Swarm Intelligence (2025.emnlp-main)

Challenge:	Large Reasoning Models (LRMs) have demonstrated remarkable capabilities in complex reasoning through long chain-of-thought, yet they struggle with precise computations and algorithmic operations.
Approach:	They propose a training-free approach that activates LRMs’ latent tool-use capabilities through artificial hints and a framework that enables models to learn effective tool utilization through diverse hint patterns and rejection-based data synthesis.
Outcome:	Experiments show that START significantly improves state-of-the-art LRMs across challenging benchmarks, including competition-level mathematics (AMC23: 95.0%, AIME24: 75.6%) and graduate-level science questions (GPQA: 64.6%).

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Yao Zhang, Chenyang Lin, Shijie Tang, Haokun Chen, Shijie Zhou, Yunpu Ma, Volker Tresp

Challenge:	Existing agentic system generation frameworks lack autonomy, autonomy, and functionality . current frameworks are too rigid, limiting adaptability and scalability.
Approach:	They propose a framework that fully automates agentic system generation, optimization, and collaboration . they construct agents from scratch and jointly refine functionality and coordination .
Outcome:	The proposed framework outperforms ADAS on six real-world, open-ended, and exploratory tasks on the TravelPlanner benchmark.

Improving Variational Autoencoder for Text Modelling with Timestep-Wise Regularisation (2020.coling-main)

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Ruizhe Li, Xiao Li, Guanyi Chen, Chenghua Lin

Code Generation From Flowcharts with Texts: A Benchmark Dataset and An Approach (2022.findings-emnlp)

Challenge:	Variational Autoencoders (VAEs) have been widely used in text modelling but posterior collapse is a problem when RNN-based models are employed.
Approach:	They propose a timestep-wise regularisation VAE architecture which can effectively avoid posterior collapse when used in text modelling.
Outcome:	The proposed model avoids posterior collapse and can be applied to any RNN-based VAE model.

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Zejie Liu, Xiaoyu Hu, Deyu Zhou, Lin Li, Xu Zhang, Yanzheng Xiang

Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions (D19-1)

Challenge:	Currently, researchers focus on generating codes from requirement documents.
Approach:	They propose to generate source code from flowcharts with texts instead of directly translating requirements into codes.
Outcome:	The proposed model improves on the baselines by transforming flowcharts into pseudo-code . the proposed model is based on 320 flowchartes with their corresponding source codes .

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Rui Zhang, Tao Yu, Heyang Er, Sungrok Shim, Eric Xue, Xi Victoria Lin, Tianze Shi, Caiming Xiong, Richard Socher, Dragomir Radev

SCALAR: Scientific Citation-based Live Assessment of Long-context Academic Reasoning (2026.eacl-long)

Challenge:	Generating SQL queries from user utterances is an important task to help end users acquire information from databases.
Approach:	They propose a context-dependent text-to-SQL generation task that edits previous queries . they use an utterance-table encoder and a table-aware decoder to incorporate context .
Outcome:	The proposed model is flexible to change individual tokens and robust to error propagation.

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Renxi Wang, Honglin Mu, Liqun Ma, Lizhi Lin, Yunlong Feng, Timothy Baldwin, Xudong Han, Haonan Li

Hyperbolic Geometry is Not Necessary: Lightweight Euclidean-Based Models for Low-Dimensional Knowledge Graph Embeddings (2021.findings-emnlp)

Challenge:	Long-context understanding is a critical capability for large language models . evaluating this capability requires extensive human annotation, which is time-consuming and costly.
Approach:	They propose a benchmark to assess citation-grounded long-context reasoning in academic writing.
Outcome:	The proposed benchmark compares state-of-the-art models with human experts on two tasks . human experts achieve 90% accuracy, but most models struggle with the cloze-style task .

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Kai Wang, Yu Liu, Dan Lin, Michael Sheng

DetectBench: Can Large Language Model Detect and Piece Together Implicit Evidence? (2024.findings-emnlp)

Challenge:	Recent knowledge graph embedding models based on hyperbolic geometry are complicated than Euclidean operations.
Approach:	They propose to use hyperbolic geometry to generate high-fidelity and parsimonious representations of hierarchical patterns in knowledge graphs.
Outcome:	The proposed models achieve state-of-the-art performance on two widely-used datasets and cost less than RotH.

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Zhouhong Gu, Lin Zhang, Xiaoxuan Zhu, Jiangjie Chen, Wenhao Huang, Yikai Zhang, Shusen Wang, Zheyu Ye, Yan Gao, Hongwei Feng, Yanghua Xiao

LLM-Based Multi-Agent Systems are Scalable Graph Generative Models (2025.findings-acl)

Challenge:	Existing LLMs' abilities to detect evidence in long contexts are far inferior to humans.
Approach:	They propose a benchmark to assess LLMs' abilities in evidence and multi-step commonsense reasoning within a long context.
Outcome:	The proposed method improves the performance of LLMs in evidence detection and commonsense reasoning.

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Jiarui Ji, Runlin Lei, Jialing Bi, Zhewei Wei, Xu Chen, Yankai Lin, Xuchen Pan, Yaliang Li, Bolin Ding

Adversarial Multi-lingual Neural Relation Extraction (C18-1)

Challenge:	Social graphs are mathematical structures stem from pairwise interactions between entities through nodes and edges.
Approach:	They propose a framework for dynamic, text-attributed social graph generation that simulates the temporal node and edge generation processes for zero-shot social graphs.
Outcome:	The proposed framework improves macroscopic graph structure metrics by 11% . the proposed model can generate graphs with up to 100,000 nodes or 10 million edges .

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Xiaozhi Wang, Xu Han, Yankai Lin, Zhiyuan Liu, Maosong Sun

Arxiv Copilot: A Self-Evolving and Efficient LLM System for Personalized Academic Assistance (2024.emnlp-demo)

Challenge:	Existing models cannot capture consistency and diversity of relation patterns in different languages.
Approach:	They propose an adversarial multi-lingual neural relation extraction model which considers consistency and diversity among languages.
Outcome:	The proposed model outperforms the state-of-the-art models on real-world datasets.

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Guanyu Lin, Tao Feng, Pengrui Han, Ge Liu, Jiaxuan You

UICOMPASS: UI Map Guided Mobile Task Automation via Adaptive Action Generation (2025.emnlp-main)

Challenge:	Existing solutions for document QA fail to provide personalized and up-to-date information efficiently.
Approach:	They propose to deploy a self-evolving, efficient LLM system that can offer personalized research services, maintaining a real-time updated database.
Outcome:	The proposed system saves 69.92% of time after efficient deployment.

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Yuanzhang Lin, Zhe Zhang, He Rui, Qingao Dong, Mingyi Zhou, Jing Zhang, Xiang Gao, Hailong Sun

Challenge:	Mobile task automation is an emerging technology that leverages AI to automatically execute routine tasks by users’ commands on mobile devices like Android.
Approach:	They propose a UI Map-guided LLM-based approach to automate mobile tasks using static analysis and LLMs.
Outcome:	The proposed approach achieves a 15.87% higher task execution success rate than SOTA approaches even when only APK is available.

Exploring the Capability Boundaries of LLMs in Mastering of Chinese Chouxiang Language (2026.findings-acl)

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Dianqing Lin, Tian Lan, Jiali Zhu, Jiang Li, Wei Chen, Xu Liu, null Aruukhan, Xiangdong Su, Hongxu Hou, Guanglai Gao

Lifting the Curse of Multilinguality by Pre-training Modular Transformers (2022.naacl-main)

Challenge:	Current state-of-the-art LLMs exhibit clear limitations on multiple tasks, while performing well on tasks that involve contextual semantic understanding.
Approach:	They propose a mouse-based benchmark to evaluate LLMs' performance on NLP tasks involving Chouxiang Language.
Outcome:	The proposed benchmark evaluates the performance of LLMs on six NLP tasks involving Chouxiang Language.

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Jonas Pfeiffer, Naman Goyal, Xi Lin, Xian Li, James Cross, Sebastian Riedel, Mikel Artetxe

TLoRA: Task-aware Low Rank Adaptation of Large Language Models (2026.acl-long)

Challenge:	Recent work on multilingual pre-trained models has focused on pre-training transformers on concatenated corpora of a large number of languages.
Approach:	They propose a language-specific module approach that allows for more languages to be trained post-hoc.
Outcome:	The proposed model can be pre-trained on multiple languages with no drop in performance .

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Weicheng Lin, Yi Zhang, Jiawei Dang, Liang-Jie Zhang

Even the Simplest Baseline Needs Careful Re-investigation: A Case Study on XML-CNN (2022.naacl-main)

Challenge:	Existing low-rank Adaptation (LoRA) methods address only one factor, often at the cost of increased training complexity or reduced practical efficiency.
Approach:	They propose a low-rank Adaptation framework that optimizes initialization and resource allocation at the outset of training.
Outcome:	The proposed framework performs excellently across various tasks while reducing the number of trainable parameters.

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Si-An Chen, Jie-jyun Liu, Tsung-Han Yang, Hsuan-Tien Lin, Chih-Jen Lin

Decoding Time Series with LLMs: A Multi-Agent Framework for Cross-Domain Annotation (2026.findings-eacl)

Challenge:	XML-CNN has been a popular research topic in NLP due to its superior performance . however, the increasing complexity brings difficulties to ensure the true architectural progress .
Approach:	They propose to re-examine an influential multi-label text classification method . they propose suitable baselines for multi-level text classification tasks .
Outcome:	The proposed method performs better than the original model, the authors show . they show that the re-implementation reveals contradictory results to the original work .

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Minhua Lin, Zhengzhang Chen, Yanchi Liu, Xujiang Zhao, Zongyu Wu, Junxiang Wang, Xiang Zhang, Suhang Wang, Haifeng Chen

Challenge:	Time series data is ubiquitous across various domains, including manufacturing, finance, and healthcare.
Approach:	They propose a multi-agent system to generate general and domain-specific annotations for time series data.
Outcome:	The proposed system outperforms existing methods on synthetic and real-world datasets.

Cross-Domain Modeling of Sentence-Level Evidence for Document Retrieval (D19-1)

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Zeynep Akkalyoncu Yilmaz, Wei Yang, Haotian Zhang, Jimmy Lin

Getting To Know You: User Attribute Extraction from Dialogues (2020.lrec-1)

Challenge:	Existing test collections provide only document-level relevance judgments, and documents exceed the length that BERT was designed to handle.
Approach:	They propose to aggregate sentence-level evidence to rank news articles using BERT . they also leverage passage-level relevance judgments available in other domains to fine-tune BERT models that capture cross-domain notions of relevance.
Outcome:	The proposed model aggregates sentence-level evidence to rank documents on three standard test collections.

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Chien-Sheng Wu, Andrea Madotto, Zhaojiang Lin, Peng Xu, Pascale Fung

Improving Knowledge Graph Completion with Structure-Aware Supervised Contrastive Learning (2024.emnlp-main)

Challenge:	a new method to extract user attributes from dialogues is needed to improve user understanding.
Approach:	They propose to leverage dialogues with conversational agents to automatically extract user attributes from dialogues.
Outcome:	The proposed model surpasses retrieval and generation baselines on human evaluation.

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Jiashi Lin, Lifang Wang, Xinyu Lu, Zhongtian Hu, Wei Zhang, Wenxuan Lu

Explaining Word Embeddings via Disentangled Representation (2020.aacl-main)

Challenge:	Existing contrastive methods focus on individual triples, overlooking the broader structural connectivities and topologies of KGs.
Approach:	They propose a new contrastive learning framework that incorporates four tasks specifically tailored to KG data: Vertex-level CL, Neighbor-level Cl, Path-levelCL, and Relation composition level CL.
Outcome:	The proposed framework achieves SOTA performance under standard supervised and low-resource settings.

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Keng-Te Liao, Cheng-Syuan Lee, Zhong-Yu Huang, Shou-de Lin

Multi-task Adversarial Attacks against Black-box Model with Few-shot Queries (2025.acl-long)

Challenge:	Disentangled representations are known to represent interpretable factors in separated dimensions.
Approach:	They propose to transform dense word vectors into disentangled embeddings with improved interpretability by encoding polysemous semantics separately.
Outcome:	The proposed model can be encoded into multiple sub-embeddings or sub-areas and generates more efficient and effective features for natural language processing.

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Wenqiang Wang, Yan Xiao, Hao Lin, Yangshijie Zhang, Xiaochun Cao

CHROMIC: Chronological Reasoning Across Multi-Panel Comics (2026.eacl-long)

Challenge:	Existing adversarial text attacks rely on abundant access to shared internal features and numerous queries, limited to a single task type.
Approach:	They propose a black-box attack that exploits the transferability of adversarial texts . they use a deep-level substitute model trained in a plug-and-play manner for text classification .
Outcome:	The proposed attack can target multiple tasks with minimal perturbations . it can target commercial APIs, large language models, and image-generation models .

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Bingxuan Hou, Jiayi Lin, Chenyang Zhang, Dapeng Yin, Shuyue Zhu, Qingqing Hong, Mengna Gao, Junli Wang

ImpRIF: Stronger Implicit Reasoning Leads to Better Complex Instruction Following (2026.acl-long)

Challenge:	Large-scale vision–language models have achieved remarkable progress on various reasoning tasks, but most studies focus on natural photographic images and pay limited attention to multi-panel visual narratives such as comics.
Approach:	They propose a benchmark dataset for chronological reasoning in multi-panel comics that covers six types of reasoning questions and spans both Western and Japanese comic styles.
Outcome:	The proposed dataset covers six types of reasoning questions and spans both Western and Japanese comic styles.

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Yuancheng Yang, Lin Yang, Xu Wang, Chao Tong, Haihua Yang

Regularized Attentive Capsule Network for Overlapped Relation Extraction (2020.coling-main)

Challenge:	Experiments show that enhancing implicit reasoning capabilities can significantly improve complex instruction following in large language models.
Approach:	They propose a method to enhance LLMs’ understanding of implicit reasoning instructions by formalizing such instructions as verifiable reasoning graphs and fine-tuning with graph reasoning.
Outcome:	The proposed method outperforms existing models on five complex instruction following benchmarks and will be open-sourced in the near future.

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Tianyi Liu, Xiangyu Lin, Weijia Jia, Mingliang Zhou, Wei Zhao

What the DAAM: Interpreting Stable Diffusion Using Cross Attention (2023.acl-long)

Challenge:	Existing methods to extract relations from distant supervision contain low-quality instances with noisy words and overlapped relations.
Approach:	They propose a Regularized Attentive Capsule Network to better identify overlapped relations in informal sentences . they embed multi-head attention into the capsule network as the low-level capsules .
Outcome:	Extensive experiments show that the proposed model improves relation extraction.

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Raphael Tang, Linqing Liu, Akshat Pandey, Zhiying Jiang, Gefei Yang, Karun Kumar, Pontus Stenetorp, Jimmy Lin, Ferhan Ture

Consultant Decoding: Yet Another Synergistic Mechanism (2025.findings-acl)

Challenge:	a new text-image attribution analysis model for text-to-image generation is understudied due to ethical constraints . corporators have restricted the general public from using the models and their weights .
Approach:	They perform a text-image attribution analysis on Stable Diffusion, a recently open-sourced model.
Outcome:	The proposed method achieves a competitive 58.8-64.8 mIoU on noun segmentation and fair to good mean opinion scores on all parts of speech rated by humans . it also achieves good attribution quality on all part of speech, rated in humans - and the first to interpret large diffusion models from a visuolinguistic perspective.

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Chuanghao Ding, Jiaping Wang, Ziqing Yang, Xiaoliang Wang, Dahua Lin, Nguyen Cam-Tu, Fei Tan

Rethinking Token Reduction for State Space Models (2024.emnlp-main)

Challenge:	Large language models (LLMs) have attracted widespread attention and adoption across diverse domains due to their exceptional performance and robust generalization abilities.
Approach:	They propose a synergetic mechanism for Consultant Decoding (CD) that achieves a 2.5-fold increase in inference speed compared to the target model while maintaining comparable generation quality.
Outcome:	The proposed mechanism achieves 2.5-fold increase in inference speed while maintaining comparable generation quality (100% of the target model’s performance).

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Zheng Zhan, Yushu Wu, Zhenglun Kong, Changdi Yang, Yifan Gong, Xuan Shen, Xue Lin, Pu Zhao, Yanzhi Wang

Challenge:	Existing methods for token reduction for SSMs lead to performance drops . a recent study shows that Mamba-2 improves the accuracy of the model by 5.7% to 13.1% .
Approach:	They propose a token reduction method that integrates token importance and similarity into SSMs and takes advantage of pruning and merging.
Outcome:	The proposed method improves accuracy by 5.7% to 13.1% on six benchmarks with Mamba-2 compared to existing methods while reducing computational demands and memory requirements.

Reasoning Over Paragraph Effects in Situations (D19-58)

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Kevin Lin, Oyvind Tafjord, Peter Clark, Matt Gardner

ReTraCk: A Flexible and Efficient Framework for Knowledge Base Question Answering (2021.acl-demo)

Challenge:	a key component of reading a passage is the ability to apply knowledge gained from the passage to a new situation.
Approach:	They propose a benchmark for reading comprehension targeting Reasoning Over Paragraph Effects in Situations.
Outcome:	The proposed model performs slightly better than randomly guessing an answer of the correct type, but is below the human performance of 89.0%.

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Shuang Chen, Qian Liu, Zhiwei Yu, Chin-Yew Lin, Jian-Guang Lou, Feng Jiang

BLADE: Benchmarking Language Model Agents for Data-Driven Science (2024.findings-emnlp)

Challenge:	Existing neural semantic parsing methods for knowledge base question answering are lacking . a generic and extensible framework is lacking for KBQA.
Approach:	They propose a neural semantic parsing framework for large scale knowledge base question answering . they propose 'retriever-transducer-checker' framework that provides a retriever and a transducer .
Outcome:	The proposed framework is ranked at top1 overall performance on the GrailQA leaderboard and achieves competitive performance on typical WebQuestionsSP benchmark.

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Ken Gu, Ruoxi Shang, Ruien Jiang, Keying Kuang, Richard-John Lin, Donghe Lyu, Yue Mao, Youran Pan, Teng Wu, Jiaqian Yu, Yikun Zhang, Tianmai Zhang, Lanyi Zhu, Mike Merrill, Jeffrey Heer, Tim Althoff

Temporally Grounding Natural Sentence in Video (D18-1)

Challenge:	Language model-based agents can be used to conduct and support data-driven science, but evaluating them on open-ended tasks is challenging due to multiple valid approaches, partially correct steps, and different ways to express the same decisions.
Approach:	They propose a benchmark to automatically evaluate agents’ multifaceted approaches to open-ended research questions.
Outcome:	BLADE evaluates agents’ multifaceted approaches to open-ended research questions using data from 12 datasets and research questions drawn from existing scientific literature.

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Jingyuan Chen, Xinpeng Chen, Lin Ma, Zequn Jie, Tat-Seng Chua

One Panel Does Not Fit All: Case-Adaptive Multi-Agent Deliberation for Clinical Prediction (2026.acl-srw)

Challenge:	Existing methods for grounding natural sentences in video are limited to a single pass.
Approach:	They propose a Temporal GroundNet (TGN) method that captures the evolving fine-grained frame-by-word interactions between video and sentence to ground the segment corresponding to the sentence.
Outcome:	The proposed method significantly improves on the state-of-the-art methods on three public datasets and shows significant improvements in performance.

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Yuxing Lu, Yushuhong Lin, Jason Zhang

Challenge:	Existing single-agent strategies sample from one role-conditioned distribution, and multi-agend frameworks use fixed roles with flat majority voting, discarding the diagnostic signal in disagreement.
Approach:	They propose a case-adaptive multi-agent panel where an attending-physician agent dynamically assembles a specialist panel tailored to each case’s diagnostic uncertainty.
Outcome:	The proposed model outperforms baseline models on diagnostic prediction and brief hospital course generation using MIMIC-IV.

LycheeCluster: Efficient Long-Context Inference with Structure-Aware Chunking and Hierarchical KV Indexing (2026.findings-acl)

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Dongfang Li, Zixuan Liu, Gang Lin, Baotian Hu, Min Zhang

FormosanBench: Benchmarking Low-Resource Austronesian Languages in the Era of Large Language Models (2025.findings-emnlp)

Challenge:	Existing retrieval-based methods compromise semantic integrity through fixed-size chunking and suffer from inefficient linear scanning.
Approach:	They propose a method that preserves local semantic coherence through boundary-aware chunking and constructs a recursive hierarchical index rooted in the triangle inequality.
Outcome:	The proposed method achieves 3.6 end-to-end inference speedup with negligible degradation in model performance.

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Kaiying Kevin Lin, Hsi-Yu Chen, Haopeng Zhang

Ensemble Privacy Defense for Knowledge-Intensive LLMs against Membership Inference Attacks (2026.findings-eacl)

Challenge:	Existing LLMs consistently underperform across all tasks, with 10-shot learning and fine-tuning offering only limited improvements.
Approach:	They introduce FormosanBench, a benchmark for evaluating LLMs on low-resource Austronesian languages.
Outcome:	The proposed benchmark covers three endangered Formosan languages: Atayal, Amis, and Paiwan . existing LLMs consistently underperform across all tasks, with 10-shot learning and fine-tuning offering only limited improvements.

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Haowei Fu, Bo Ni, Han Xu, Kunpeng Liu, Dan Lin, Tyler Derr

Self-Discriminative Learning for Unsupervised Document Embedding (N19-1)

Challenge:	Large language models (LLMs) are the foundation of modern natural language processing, powering applications across diverse domains.
Approach:	They propose a model-agnostic defense framework which aggregates and evaluates the outputs of a knowledge-injected LLM, a base LLM and a dedicated judge model to enhance resistance against membership inference attacks.
Outcome:	The proposed framework reduces MIA success by up to 27.8% for SFT and 526.3% for RAG compared to inference-time baseline while maintaining answer quality.

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Hong-You Chen, Chin-Hua Hu, Leila Wehbe, Shou-De Lin

AMA: Adaptive Memory via Multi-Agent Collaboration (2026.findings-acl)

Challenge:	Existing methods for document embedding learning do not consider inter-document relationships.
Approach:	They propose to exploit the inter-document information and directly model the relations of documents in embedding space with a discriminative network and a novel objective.
Outcome:	The proposed method has errors that are 5 to 13% lower than state-of-the-art models and is even more pronounced in scarce label setting.

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Weiquan Huang, Zixuan Wang, Hehai Lin, Sudong Wang, Bo Xu, Qian Li, Beier Zhu, Linyi Yang, Chengwei Qin

Linguistic Rule Induction Improves Adversarial and OOD Robustness in Large Language Models (2024.lrec-main)

Challenge:	Existing approaches to longterm memory rely on rigid retrieval granularity, accumulation-heavy maintenance strategies, and coarse-grained update mechanisms.
Approach:	They propose a framework that leverages coordinated agents to manage memory across multiple granularities.
Outcome:	The proposed framework outperforms state-of-the-art benchmarks while reducing token consumption by approximately 80%.

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Shuoran Jiang, Qingcai Chen, Yang Xiang, Youcheng Pan, Yukang Lin

Layer-Level Self-Exposure and Patch: Affirmative Token Mitigation for Jailbreak Attack Defense (2025.naacl-long)

Challenge:	Existing large language models (LLMs) do not perform satisfactorily in OOD and adversarial robustness evaluations.
Approach:	They propose to use linguistic rule induction to fine-tune large language models with linguistic rules to achieve better adversarial and OOD robustness.
Outcome:	The proposed model achieves comparable or better results with GPT-3.5 and GPT-4 on various adversarial and OOD robustness evaluations.

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Yang Ouyang, Hengrui Gu, Shuhang Lin, Wenyue Hua, Jie Peng, Bhavya Kailkhura, Meijun Gao, Tianlong Chen, Kaixiong Zhou

More Robust Schema-Guided Dialogue State Tracking via Tree-Based Paraphrase Ranking (2023.findings-eacl)

Challenge:	Existing methods to defend against jailbreak attacks exploit vulnerabilities to elicit unintended or harmful outputs.
Approach:	They propose a method to defend against jailbreak attacks by patching specific layers within large language models through self-augmented datasets.
Outcome:	The proposed approach reduces harmfulness and attack success rate of jailbreak attacks without compromising utility for benign queries compared to previous methods.

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Alexandru Coca, Bo-Hsiang Tseng, Weizhe Lin, Bill Byrne

When Models Outthink Their Safety: Unveiling and Mitigating Self-Jailbreak in Large Reasoning Models (2026.findings-acl)

Challenge:	Pre-trained language models (PLMs) can track only slots drawn from a database or domain ontology.
Approach:	They propose a framework for generating synthetic schemas which uses tree-based ranking to optimise lexical diversity and semantic faithfulness.
Outcome:	The proposed framework improves the generalisation of strong baselines by augmenting training data with prompts generated by the framework.

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Yingzhi Mao, Chunkang Zhang, Junxiang Wang, Xinyan Guan, Boxi Cao, Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun

Incorporating Chinese Characters of Words for Lexical Sememe Prediction (P18-1)

Challenge:	Existing methods often apply coarse-grained constraints over entire reasoning trajectories . Existing approaches often apply unsafe constraints, causing unsafe outputs .
Approach:	They propose a trajectory-level training framework that mitigates Self-Jailbreak . they propose 'chain-of-guardrail' to mitigate self-jailbreak by targeting step-level interventions .
Outcome:	The proposed framework mitigates Self-Jailbreak by targeting step-level interventions while maintaining reasoning ability.

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Huiming Jin, Hao Zhu, Zhiyuan Liu, Ruobing Xie, Maosong Sun, Fen Lin, Leyu Lin

Generating Attractive and Authentic Copywriting from Customer Reviews (2024.naacl-long)

Challenge:	Existing methods of lexical sememe prediction rely on external context information of words to represent meaning.
Approach:	They propose a character-enhanced sememe prediction framework for Chinese language that takes advantage of internal character information and external context information.
Outcome:	The proposed framework outperforms state-of-the-art methods on a Chinese sememe knowledge base and maintains robust performance even for low-frequency words.

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Yu-Xiang Lin, Wei-Yun Ma

Challenge:	Typical approaches to copywriting focus on product attributes, leading to dull and repetitive content.
Approach:	They propose to generate copywriting based on customer reviews as they provide firsthand practical experiences with products, offering a richer source of information than just product attributes.
Outcome:	The proposed framework outperforms baseline and zero-shot large language models in terms of both attractiveness and faithfulness.

EtriCA: Event-Triggered Context-Aware Story Generation Augmented by Cross Attention (2022.findings-emnlp)

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Chen Tang, Chenghua Lin, Henglin Huang, Frank Guerin, Zhihao Zhang

Improving Hateful Meme Detection through Retrieval-Guided Contrastive Learning (2024.acl-long)

Challenge:	Existing methods for story generation still suffer from problems of relevance and coherence.
Approach:	They propose a novel neural generation model which maps contextual and event features to event sequences with a cross-attention mechanism and exploits logical relatedness between events.
Outcome:	The proposed model outperforms state-of-the-art models on automatic and human evaluations and shows that it can leverage contextual and event features.

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Jingbiao Mei, Jinghong Chen, Weizhe Lin, Bill Byrne, Marcus Tomalin

MEDSYN: Benchmarking Multi-EviDence SYNthesis in Complex Clinical Cases for Multimodal Large Language Models (2026.findings-acl)

Challenge:	Existing systems for detecting hateful memes lack sensitivity to subtle differences in memes that are vital for correct hatefulness classification.
Approach:	They propose to construct a hatefulness-aware embedding space through retrieval-guided contrastive training to identify hatefulness based on data unseen in training.
Outcome:	The proposed system outperforms existing models on the HatefulMemes dataset with an AUROC of 87.0 and improves contextual understanding across domains.

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Boqi Chen, Xudong Liu, Jiachuan Peng, Marianne Frey-Marti, Kyle Lam, Bang Zheng, Lin Li, Jianing Qiu

A Coarse-to-fine Cascaded Evidence-Distillation Neural Network for Explainable Fake News Detection (2022.coling-1)

Challenge:	Existing benchmarks for multimodal large language models do not capture real-world clinical complexity.
Approach:	They evaluate multilingual, multimodal multimodal models of clinical cases with up to 7 distinct visual clinical evidence types per case.
Outcome:	The proposed model outperforms human models on differential diagnosis (DDx) generation and final diagnosis (FDx) selection.

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Zhiwei Yang, Jing Ma, Hechang Chen, Hongzhan Lin, Ziyang Luo, Yi Chang

Sailor: Open Language Models for South-East Asia (2024.emnlp-demo)

Challenge:	Existing methods for fake news detection focus on fact-checked reports, resulting in limited coverage and debunking delays.
Approach:	They propose a Coarse-to-fine Cascaded Evidence-Distillation neural network for explainable fake news detection based on raw reports . they use hierarchical encoders and cascaded selectors to select most explainable sentences for verdicts on top of selected top-K reports based upon raw reports.
Outcome:	The proposed model outperforms baseline detection methods and generates high-quality explanations from diverse evaluation perspectives.

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Longxu Dou, Qian Liu, Guangtao Zeng, Jia Guo, Jiahui Zhou, Xin Mao, Ziqi Jin, Wei Lu, Min Lin

LLM-OREF: An Open Relation Extraction Framework Based on Large Language Models (2025.emnlp-main)

Challenge:	Large language models (LLMs) rely on English data for training, but are often not comparable across other languages.
Approach:	They propose to develop a family of open language models for SEA languages . they use BPE dropout, aggressive data cleaning and deduplication to improve model robustness .
Outcome:	The proposed models perform well across four benchmarks, including commonsense reasoning, question answering, reading comprehension and examination.

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Hongyao Tu, Liang Zhang, Yujie Lin, Xin Lin, Haibo Zhang, Long Zhang, Jinsong Su

Breaking the Boundaries: A Unified Framework for Chinese Named Entity Recognition Across Text and Speech (2024.findings-emnlp)

Challenge:	Existing studies focus on building models that can only handle predefined relations . however, their reliance on human annotation limits their practicality .
Approach:	They propose an open relation extraction framework that can generalize to new relations not encountered during training.
Outcome:	The proposed framework can generalize to new relations not encountered during training.

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Jinzhong Ning, Yuanyuan Sun, Bo Xu, Zhihao Yang, Ling Luo, Hongfei Lin

Knowledge Base Question Answering via Encoding of Complex Query Graphs (D18-1)

Challenge:	Existing approaches to Named Entity Recognition (NER) tasks are limited by the complexity of the data and the potential connections between tasks.
Approach:	They propose a task to break the boundaries between different modal NER tasks by using a unified data format for inputs from different modalités.
Outcome:	The proposed task breaks the boundaries between different modal NER tasks and is a unified implementation of them.

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Kangqi Luo, Fengli Lin, Xusheng Luo, Kenny Zhu

Exploring the Impact of Model Scaling on Parameter-Efficient Tuning (2023.emnlp-main)

Challenge:	Existing KBQA methods focus on simpler questions and do not work well on complex questions . a knowledge-based question answering approach is able to answer complex questions using a standard knowledge base .
Approach:	They propose to encode query structure into a uniform vector representation of a question and its semantic components into .
Outcome:	The proposed approach outperforms existing methods on complex questions while staying competitive on simple questions.

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Yusheng Su, Chi-Min Chan, Jiali Cheng, Yujia Qin, Yankai Lin, Shengding Hu, Zonghan Yang, Ning Ding, Xingzhi Sun, Guotong Xie, Zhiyuan Liu, Maosong Sun

ETRQA: A Comprehensive Benchmark for Evaluating Event Temporal Reasoning Abilities of Large Language Models (2025.findings-acl)

Challenge:	Parameter-efficient tuning (PET) methods can drive large pre-trained language models by training only minimal parameters.
Approach:	They propose a parameter-efficient tuning method that is compatible with a tunable module and uses a random number generator to optimize fewer table parameters.
Outcome:	The proposed method is compatible with a tunable module and tested on 11 NLP tasks.

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Sigang Luo, Yinan Liu, Dongying Lin, Yingying Zhai, Bin Wang, Xiaochun Yang, Junpeng Liu

“Barking up the Right Tree”, a GAN-Based Pun Generation Model through Semantic Pruning (2024.lrec-main)

Challenge:	Event temporal reasoning (ETR) is a significant indicator that a large language model understands the physical world.
Approach:	They propose a unified taxonomy for event temporal questions and construct a benchmark based on this taxonomies.
Outcome:	The proposed taxonomy inherits and expands existing datasets and contains multiple categories of compound questions.

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JingJie Zeng, Liang Yang, Jiahao Kang, Yufeng Diao, Zhihao Yang, Hongfei Lin

Analyzing and Mitigating Inconsistency in Discrete Speech Tokens for Neural Codec Language Models (2025.acl-long)

Challenge:	Existing methods for generating humorous puns are limited and require a broad spectrum of commonsense and worldly skills.
Approach:	They propose a GAN-based approach that employs semantic pruning and contrastive learning to generate humorous puns using a model that captures the semantic nuances of puns.
Outcome:	The proposed model produces semantically coherent and humorous puns while ensuring both correctness and humor.

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Wenrui Liu, Zhifang Guo, Jin Xu, Yuanjun Lv, Yunfei Chu, Zemin Liu, Junyang Lin

Mitigating Hallucinations in Multi-modal Large Language Models via Image Token Attention-Guided Decoding (2025.naacl-long)

Challenge:	Recent advances in Large Language Models (LLMs) have demonstrated significant strides in generating high-quality speech . discretizing speech by neural audio codecs often results in sequences that differ from text sequences .
Approach:	They quantitatively analyze the Discrete Representation Inconsistency phenomenon within popular audio tokenizers such as EnCodec.
Outcome:	The proposed method mitigates the DRI phenomenon within popular audio tokenizers such as EnCodec.

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Xinhao Xu, Hui Chen, Mengyao Lyu, Sicheng Zhao, Yizhe Xiong, Zijia Lin, Jungong Han, Guiguang Ding

FISH: A Financial Interactive System for Signal Highlighting (2023.eacl-demo)

Challenge:	Multi-modal large language models (MLLMs) generate plausible but incorrect content, resulting in hallucinations . recent advances in MLLM technology have demonstrated their outstanding performance in a variety of visual tasks, such as object detection.
Approach:	They propose a plug-and-play method which leverages MLLMs’ internal representations to mitigate hallucinations by analyzing input and output tokens.
Outcome:	The proposed method exploits MLLMs’ internal representations to mitigate hallucinations.

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Ta-wei Huang, Jia-huei Ju, Yu-shiang Huang, Cheng-wei Lin, Yi-shyuan Chiang, Chuan-ju Wang

OpenS2S: Advancing Fully Open-Source End-to-End Empathetic Large Speech Language Model (2025.emnlp-demos)

Challenge:	Existing systems or studies lack interactivity and do not provide off-the-shelf signals.
Approach:	They propose an interactive system that extracts and highlights crucial financial signals . they integrate pre-trained BERT representations and a fine-tuned BERT highlighting model .
Outcome:	The proposed system extracts and highlights key financial signals efficiently and precisely.

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Chen Wang, Tianyu Peng, Wen Yang, YiNan Bai, Guangfu Wang, Jun Lin, Lanpeng Jia, Lingxiang Wu, Jinqiao Wang, Chengqing Zong, Jiajun Zhang

LLM-Driven Implicit Target Augmentation and Fine-Grained Contextual Modeling for Zero-Shot and Few-Shot Stance Detection (2025.emnlp-main)

Challenge:	Empathetic speech models are increasingly closed off, leaving details about the architecture, data and development opaque to researchers.
Approach:	They propose an open-source empathetic speech-to-text model with a streaming interleaved decoding architecture and a data pipeline to enable end-to end training.
Outcome:	The proposed model is open-source and transparent, with no data or data required to build it.

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Yanxu Ji, Jinzhong Ning, Yijia Zhang, Zhi Liu, Hongfei Lin

Towards One-to-Many Visual Question Answering (2024.findings-emnlp)

Challenge:	Recent studies on zero-shot and few-shot stance detection neglect implicit yet semantically important targets.
Approach:	They propose a framework that uses Large Language Models to annotate implicit targets . they also propose 'DyMCA' to dynamically adjust text-target contributions based on context .
Outcome:	The proposed framework achieves state-of-the-art on a benchmark dataset.

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Huishan Ji, Qingyi Si, Zheng Lin, Yanan Cao, Weiping Wang

Filling the Long Tail: Structure-Aware Curriculum-Gap Completion for Medical Education with LLMs (2026.acl-srw)

Challenge:	Existing Visual Question Answering systems are constrained to support domain-specific questions . a model trained on a single specific domain may not be competent for real-world application.
Approach:	They propose a task to enable a single model to answer as many different domains of questions as possible . they break the task down into the integration of three key abilities .
Outcome:	The proposed model can answer as many domains of questions as possible, the authors argue . the proposed model generalizes well to three extra zero-shot datasets, and the results are published.

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Wenjie Lin

Pre-training to Match for Unified Low-shot Relation Extraction (2022.acl-long)

Challenge:	Medical education resources are dense for common diseases, but sparse for under-covered conditions, atypical presentations, and fine-grained concept distinctions.
Approach:	They propose a task where a model reconstructs missing educational units from a partially specified curriculum graph.
Outcome:	The proposed model predicts omitted concepts, restores missing instructional links, and completes automatically verifiable teaching content.

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Fangchao Liu, Hongyu Lin, Xianpei Han, Boxi Cao, Le Sun

CATE: A Contrastive Pre-trained Model for Metaphor Detection with Semi-supervised Learning (2021.emnlp-main)

Challenge:	Low-shot relation extraction (RE) aims to recognize novel relations with very few or even no samples.
Approach:	They propose a method that leverages triplet paraphrase to pre-train zero-shot label matching ability and uses meta-learning paradigm to learn few-shot instance summarizing ability.
Outcome:	The proposed method outperforms strong baselines and achieves the best performance on few-shot RE leaderboard.

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Zhenxi Lin, Qianli Ma, Jiangyue Yan, Jieyu Chen

A Graph Interaction Framework on Relevance for Multimodal Named Entity Recognition with Multiple Images (2025.coling-main)

Challenge:	Existing models for metaphor detection require a large amount of labeled data and are not linguistically-based.
Approach:	They propose a ContrAstive pre-Trained modEl (CATE) for metaphor detection with semi-supervised learning using a pre-trained model to obtain a contextual representation of target words.
Outcome:	The proposed model outperforms existing models on several benchmark datasets and achieves better performance against state-of-the-art models.

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Jiachen Zhao, Shizhou Huang, Xin Lin

Dual Contrastive Learning Framework for Incremental Text Classification (2023.findings-emnlp)

Challenge:	Existing methods to determine whether images are related to named entities are not effective in multi-image scenarios.
Approach:	They propose a graph interaction framework on relevance for Multimodal Named Entity Recognition with multiple images to integrate human abilities into the model.
Outcome:	The proposed framework achieves state-of-the-art on benchmark datasets and compares with CLIP and CLIP-based approaches.

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Yigong Wang, Zhuoyi Wang, Yu Lin, Jinghui Guo, Sadaf Halim, Latifur Khan

HierDiffuse: Progressive Diffusion for Robust Interest Fusion in CTR Prediction (2025.emnlp-industry)

Challenge:	In incremental learning, large models learn and refresh knowledge continuously . many approaches have been proposed to preserve knowledge from previous tasks while learning new concepts in online NLP applications.
Approach:	They propose a dual contrastive learning framework that fosters transferability across different tasks . they use global contrastive and task-specific learning to promote a generalized embedding space .
Outcome:	The proposed framework outperforms the current state-of-the-art methods on text datasets.

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Ziheng Ni, Congcong Liu, Yuying Chen, Zhiwei Fang, Changping Peng, Zhangang Lin, Ching Law, Jingping Shao

Inserting Information Bottlenecks for Attribution in Transformers (2020.findings-emnlp)

Challenge:	Existing approaches fuse long-term behavioral profiles and short-term interactions, suffering from representational misalignment and noise in transient signals.
Approach:	They propose a framework that redefines interest fusion as a hierarchical denoising process through diffusion models.
Outcome:	The proposed framework redefines interest fusion as a hierarchical denoising process through diffusion models.

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Zhiying Jiang, Raphael Tang, Ji Xin, Jimmy Lin

Beyond Prompting: An Efficient Embedding Framework for Open-Domain Question Answering (2025.acl-long)

Challenge:	Pretrained transformers are a popular approach for understanding features important for prediction.
Approach:	They apply information bottlenecks to analyze attribution of features for prediction on a black-box model.
Outcome:	The proposed method outperforms two competing methods in degradation tests on four datasets.

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Zhanghao Hu, Hanqi Yan, Qinglin Zhu, Zhenyi Shen, Yulan He, Lin Gui

Birds have four legs?! NumerSense: Probing Numerical Commonsense Knowledge of Pre-Trained Language Models (2020.emnlp-main)

Challenge:	Large language models (LLMs) have recently pushed open-domain question answering (ODQA) to new heights.
Approach:	They propose an embedding-level framework that enhances both the retriever and the reader by reordering query representations via lightweight linear layers under an unsupervised contrastive learning objective.
Outcome:	The proposed framework outperforms baselines in accuracy and efficiency across three open-source LLMs, three retrieval methods, and four ODQA benchmarks.

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Bill Yuchen Lin, Seyeon Lee, Rahul Khanna, Xiang Ren

Unsupervised Graph-Text Mutual Conversion with a Unified Pretrained Language Model (2023.acl-long)

Challenge:	Recent studies show that pre-trained language models possess certain commonsense and factual knowledge.
Approach:	They propose to use pre-trained language models to predict masked words . they introduce a probing task with 13.6k m-word-prediction probes .
Outcome:	The proposed model performs poorly on the diagnostic dataset prior to any fine-tuning and fine-testing with distant supervision.

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Yi Xu, Shuqian Sheng, Jiexing Qi, Luoyi Fu, Zhouhan Lin, Xinbing Wang, Chenghu Zhou

CITADEL: Conditional Token Interaction via Dynamic Lexical Routing for Efficient and Effective Multi-Vector Retrieval (2023.acl-long)

Challenge:	Existing unsupervised approaches for learning knowledge graphs require multiple modules and require entity information or relation type for training.
Approach:	They propose a method that uses a unified pretrained language model to achieve fully unsupervised graph-text mutual conversion for the first time.
Outcome:	The proposed method outperforms state-of-the-art methods for G2T and T2G tasks by fine-tuning only one pretrained model.

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Minghan Li, Sheng-Chieh Lin, Barlas Oguz, Asish Ghoshal, Jimmy Lin, Yashar Mehdad, Wen-tau Yih, Xilun Chen

What Part of the Neural Network Does This? Understanding LSTMs by Measuring and Dissecting Neurons (D19-1)

Challenge:	Existing multi-vector retrieval methods are slower and require more space to store indices compared to their single-vektor counterparts.
Approach:	They propose a multi-vector retrieval method that uses dynamic lexical routing to route different token vectors to the predicted lexicals.
Outcome:	The proposed method achieves state-of-the-art performance on several benchmark datasets while being nearly 40 times faster than the current state-out-of the-art method.

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Ji Xin, Jimmy Lin, Yaoliang Yu

A Scalable Framework for Table of Contents Extraction from Complex ESG Annual Reports (2023.emnlp-main)

Challenge:	Biological neural systems consist of a huge number of neurons, and can react to the environment in complicated ways.
Approach:	They propose a metric to quantify the sensitivity of neurons to each label and conduct experiments to prove it.
Outcome:	The proposed metric is based on a set of experiments that show that dropping an arbitrary neuron significantly degrades the accuracy of the model.

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Xinyu Wang, Lin Gui, Yulan He

Beyond Templates: Dynamic Adaptation of Reasoning Demonstrations via Feasibility-Aware Exploration (2026.findings-acl)

Challenge:	Existing methods for document classification focus on local layout, sidelining holistic comprehension of content and organisation.
Approach:	They propose a framework for Table of Contents extraction that uses hierarchical structure to extract text from ESG annual reports.
Outcome:	The proposed framework outperforms the state-of-the-art with a fraction of running time.

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Yong Wu, Weihang Pan, Ke Li, Chen Binhui, Ping Li, Binbin Lin

Akan Cinematic Emotions (ACE): A Multimodal Multi-party Dataset for Emotion Recognition in Movie Dialogues (2025.findings-acl)

Challenge:	Existing reasoning datasets that are designed for powerful LLMs often lead to degraded performance when directly applied to weaker models.
Approach:	They propose a data adaptation framework that bridges the capability gap between expert reasoning trajectories and diverse SLMs by employing a selective imitation strategy guided by step-wise adaptability estimation via solution simulation.
Outcome:	The proposed framework improves generalization and data efficiency over static fine-tuning and can be applied to large models with limited model capacity.

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David Sasu, Zehui Wu, Ziwei Gong, Run Chen, Pengyuan Shi, Lin Ai, Julia Hirschberg, Natalie Schluter

COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation (2021.naacl-demos)

Challenge:	Akan Cinematic Emotions (AkaCE) is the first multimodal emotion dialogue dataset for an African language . it contains 385 emotion-labeled dialogues and 6162 utterances across audio, visual, and textual modalities, along with word-level prosodic prominence annotations.
Approach:	They propose to use AkaCE to analyze African cinematic emotions using word-level prosodic prominence annotations.
Outcome:	The Akan Cinematic Emotions (AkaCE) dataset addresses the significant lack of resources for low-resource languages in emotion recognition research.

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Qingyun Wang, Manling Li, Xuan Wang, Nikolaus Parulian, Guangxing Han, Jiawei Ma, Jingxuan Tu, Ying Lin, Ranran Haoran Zhang, Weili Liu, Aabhas Chauhan, Yingjun Guan, Bangzheng Li, Ruisong Li, Xiangchen Song, Yi Fung, Heng Ji, Jiawei Han, Shih-Fu Chang, James Pustejovsky, Jasmine Rah, David Liem, Ahmed ELsayed, Martha Palmer, Clare Voss, Cynthia Schneider, Boyan Onyshkevych

Dynamic Model-Bank Test-Time Adaptation for Automatic Speech Recognition (2025.emnlp-main)

Challenge:	a new framework to digest relevant biomedical knowledge is needed to combat COVID-19 . quantity of research results is a bottleneck, and false information promoted in publications .
Approach:	a team of researchers has developed a framework to extract multimedia knowledge elements from scientific literature to combat COVID-19.
Outcome:	a new framework extracts fine-grained multimedia knowledge elements from scientific literature . it provides detailed contextual sentences, subfigures, and knowledge subgraphs as evidence . the framework is based on a case study of drug repurposing .

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Yanshuo Wang, Yanghao Zhou, Yukang Lin, Haoxing Chen, Jin Zhang, Wentao Zhu, Jie Hong, Xuesong Li

VideoScore: Building Automatic Metrics to Simulate Fine-grained Human Feedback for Video Generation (2024.emnlp-main)

Challenge:	Existing ASR TTA methods struggle with instability under continual and long-term distribution shifts.
Approach:	They propose a continuous adaptive model-bank framework that adapts to domain shifts in ASR test-time scenarios.
Outcome:	Experiments on diverse, continuously shifting ASR benchmarks show that DMSUTA outperforms existing continual TTA baselines.

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Xuan He, Dongfu Jiang, Ge Zhang, Max Ku, Achint Soni, Sherman Siu, Haonan Chen, Abhranil Chandra, Ziyan Jiang, Aaran Arulraj, Kai Wang, Quy Do, Yuansheng Ni, Bohan Lyu, Yaswanth Narsupalli, Rongqi Fan, Zhiheng Lyu, Bill Yuchen Lin, Wenhu Chen

Activation Reward Models for Few-Shot Model Alignment (2026.findings-acl)

Challenge:	Existing video metrics are lagging behind in providing reliable scores over generated videos due to lack of large-scale human-annotated dataset.
Approach:	They propose to use VideoFeedback to train a human-annotated multi-aspect score over 37.6K synthesized videos from 11 existing video generative models.
Outcome:	The proposed model outperforms the prior best metrics by 50 points in the test.

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Tianning Chai, Chancharik Mitra, Brandon Huang, Gautam Rajendrakumar Gare, Zhiqiu Lin, Assaf Arbelle, Leonid Karlinsky, Rogerio Feris, Trevor Darrell, Deva Ramanan, Roei Herzig

MultiMET: A Multimodal Dataset for Metaphor Understanding (2021.acl-long)

Challenge:	A common approach is to use reward models that enable reinforcement-learning post-training.
Approach:	They propose a method that steers LLM activations to align with few-shot preference data without finetuning.
Outcome:	The proposed method surpasses zero-shot, few-shot and voting-based benchmarks on reward hacking and noise signals.

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Dongyu Zhang, Minghao Zhang, Heting Zhang, Liang Yang, Hongfei Lin

MinTL: Minimalist Transfer Learning for Task-Oriented Dialogue Systems (2020.emnlp-main)

Challenge:	Metaphor is a linguistic phenomenon and a cognitive phenomenon structuring human thought, authors say . previous studies focused on texts, partly due to the unavailability of ground truth labels of multimodal metaphor .
Approach:	They propose a multimodal metaphor dataset that integrates multimodal text and image . it contains 10,437 text-image pairs with multimodal annotations of occurrences .
Outcome:	The proposed dataset examines multimodal cues and their interplay.

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Zhaojiang Lin, Andrea Madotto, Genta Indra Winata, Pascale Fung

Over-Searching in Search-Augmented Large Language Models (2026.eacl-long)

Challenge:	Existing approaches to learn dialogue state tracking and response generation are time-intensive and not transferable between domains.
Approach:	They propose a transfer learning framework that allows efficient dialogue state tracking with a minimal generation length.
Outcome:	The proposed framework improves the inference efficiency and improves state-of-the-art results on multi-domain multi-tasking systems.

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Roy Xie, Deepak Gopinath, David Qiu, Dong Lin, Haitian Sun, Saloni Potdar, Bhuwan Dhingra

A Joint Neural Model for Information Extraction with Global Features (2020.acl-main)

Challenge:	Search-augmented large language models (LLMs) excel at knowledge-intensive tasks by integrating external retrieval.
Approach:	They conduct a systematic evaluation of over-searching across multiple dimensions including query types, model categories, retrieval conditions, and multi-turn conversations.
Outcome:	The proposed model improves answer accuracy on answerable queries but harms abstention on unanswerable ones .

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Ying Lin, Heng Ji, Fei Huang, Lingfei Wu

Rethinking Self-Supervision Objectives for Generalizable Coherence Modeling (2022.acl-long)

Challenge:	Existing joint neural models for Information Extraction use local task-specific classifiers to predict labels for individual instances.
Approach:	They propose a joint neural framework that extracts the optimal IE result as a graph from an input sentence.
Outcome:	The proposed model achieves new state-of-the-art on all subtasks and does not use any language-specific feature.

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Prathyusha Jwalapuram, Shafiq Joty, Xiang Lin

MoDE: Effective Multi-task Parameter Efficient Fine-Tuning with a Mixture of Dyadic Experts (2025.findings-naacl)

Challenge:	Prior work on text generation models focused on new architectures for permuted document tasks.
Approach:	They propose to use a basic model architecture to improve coherence evaluation of machine generated text.
Outcome:	The proposed model improves on a task-independent test set and shows significant improvements in coherence evaluations of downstream tasks.

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Lin Ning, Harsh Lara, Meiqi Guo, Abhinav Rastogi

Reduce Redundancy Then Rerank: Enhancing Code Summarization with a Novel Pipeline Framework (2024.lrec-main)

Challenge:	Recent efforts have explored mixtures of LoRA modules for multi-task settings, but this study reveals redundancy in the down-projection matrix of these architectures.
Approach:	They propose a method to share down-projection matrix across tasks and employ atomic rank-one adapters coupled with routers that allow more sophisticated task-level specialization.
Outcome:	The proposed method outperforms state-of-the-art models on a SNI benchmark and provides a practical solution for deploying lightweight models.

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Xiaoyu Hu, Xu Zhang, Zexu Lin, Deyu Zhou

Multimedia Event Extraction with LLM Knowledge Editing (2025.emnlp-main)

Challenge:	Existing code summarization models lack redundant tokens and are plagued by exposure bias.
Approach:	They propose a pipeline framework to reduce redundancy then rerank that eliminates redundant information in code representation space and a re-ranking model to select more suitable summary candidates.
Outcome:	The proposed framework overrides state-of-the-art approaches on six datasets from the CodeSearchNet benchmark.

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Jiaao Yu, Yijing Lin, Zhipeng Gao, Xuesong Qiu, Lanlan Rui

Event Extraction

Challenge:	Existing multimodal event extraction methods focus on weakly aligning features from wellpretrained unimodal encoders, resulting in redundant feature perception.
Approach:	They propose a multimodal event extraction strategy with a redundant feature selection mechanism that enhances event understanding ability of multimodal large language models.
Outcome:	The proposed method outperforms the state-of-the-art (SOTA) baselines on the M2E2 benchmark.

HyCoRec: Hypergraph-Enhanced Multi-Preference Learning for Alleviating Matthew Effect in Conversational Recommendation (2024.acl-long)

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Yongsen Zheng, Ruilin Xu, Ziliang Chen, Guohua Wang, Mingjie Qian, Jinghui Qin, Liang Lin

MIRACL: A Multilingual Retrieval Dataset Covering 18 Diverse Languages (2023.tacl-1)

Challenge:	Existing methods to study the Matthew effect in Recommender Systems (RSs) however, it is amplified when the user interacts with the system over time.
Approach:	They propose a paradigm to alleviate the Matthew effect in conversational recommendation by learning multi-aspect preferences.
Outcome:	The proposed paradigm achieves state-of-the-art performance and superior of alleviating Matthew effect in conversational recommendation tasks.

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Xinyu Zhang, Nandan Thakur, Odunayo Ogundepo, Ehsan Kamalloo, David Alfonso-Hermelo, Xiaoguang Li, Qun Liu, Mehdi Rezagholizadeh, Jimmy Lin

Knowledge Extraction on Semi-Structured Content: Does It Remain Relevant for Question Answering in the Era of LLMs? (2026.eacl-long)

Challenge:	MIRACL is a multilingual dataset for ad hoc retrieval across 18 languages that collectively encompass over three billion native speakers around the world.
Approach:	They have gathered over 726k high-quality relevance judgments for 78k queries over Wikipedia in these languages, where all annotations have been performed by native speakers hired by their team.
Outcome:	MIRACL covers languages that are typologically close as well as distant from 10 language families and 13 sub-families, associated with varying amounts of publicly available resources.

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Kai Sun, Yin Huang, Srishti Mehra, Mohammad Kachuee, Xilun Chen, Renjie Tao, Zhaojiang Lin, Andrea Jessee, Nirav Shah, Alex L Betty, Yue Liu, Anuj Kumar, Wen-tau Yih, Xin Luna Dong

UniGeo: Unifying Geometry Logical Reasoning via Reformulating Mathematical Expression (2022.emnlp-main)

Challenge:	Existing literature on knowledge extraction for question answering questions whether it is still relevant for question answerrs.
Approach:	They extend an existing benchmark with knowledge extraction annotations and evaluate commercial and open-source LLMs of varying sizes.
Outcome:	The proposed model can achieve high QA accuracy, but can still benefit from knowledge extraction through augmentation with extracted triples and multi-task learning.

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Jiaqi Chen, Tong Li, Jinghui Qin, Pan Lu, Liang Lin, Chongyu Chen, Xiaodan Liang

LLMs-as-Instructors: Learning from Errors Toward Automating Model Improvement (2024.findings-emnlp)

Challenge:	Existing work on geometry problem solving treats calculation and proving as two specific tasks hindering a deep model to unify reasoning ability on multiple math tasks.
Approach:	They propose a large-scale Unified Geometry problem benchmark to unify geometry on multiple math tasks.
Outcome:	The proposed framework outperforms the existing model with 5.6% and 3.2% accuracies on calculation and proving problems.

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Jiahao Ying, Mingbao Lin, Yixin Cao, Wei Tang, Bo Wang, Qianru Sun, Xuanjing Huang, Shuicheng Yan

Challenge:	Using advanced Large Language Models, instructors can improve training of smaller models by analyzing their own model's errors.
Approach:	They propose a framework that leverages advanced Large Language Models to enhance training of smaller target models.
Outcome:	The proposed framework outperforms ChatGPT on multiple benchmarks and shows that it improves on both in-domain and out-of-domain benchmarks.

Varying Sentence Representations via Condition-Specified Routers (2024.emnlp-main)

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Ziyong Lin, Quansen Wang, Zixia Jia, Zilong Zheng

Length is a Curse and a Blessing for Document-level Semantics (2023.emnlp-main)

Challenge:	Existing sentences cannot account for different aspects of semantic similarity between two sentences.
Approach:	They propose a transformer-style framework that generates conditioned sentences . they propose 'conditional' STS, which measures similarity between two sentences based on condition sentences - a task that requires a sentence embedding model capable of generating distinct representations for the same sentence under different conditions.
Outcome:	The proposed framework is superior to existing models on two condition sentences . it can generate conditioned sentences while maintaining model parameters and computational efficiency .

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Chenghao Xiao, Yizhi Li, G Hudson, Chenghua Lin, Noura Al Moubayed

Cross-lingual Lexical Sememe Prediction (D18-1)

Challenge:	In recent years, contrastive learning (CL) has been extensively utilized to recover sentence and document-level encoding capability from pre-trained language models.
Approach:	They propose a document-based contrastive learning framework that is length-agnostic self-reference based on document length.
Outcome:	The proposed framework achieves state-of-the-art on the standard information retrieval benchmark.

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Fanchao Qi, Yankai Lin, Maosong Sun, Hao Zhu, Ruobing Xie, Zhiyuan Liu

Neural Label Search for Zero-Shot Multi-Lingual Extractive Summarization (2022.acl-long)

Challenge:	Sememes are defined as the minimum semantic units of human languages . but most languages do not have sememe-based linguistic knowledge bases . a new framework is proposed to predict sememes for words in other languages based on semems .
Approach:	They propose a framework to model correlations between sememes and multi-lingual words in low-dimensional semantic space for sememe prediction.
Outcome:	The proposed model improves on baseline methods on real-world datasets.

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Ruipeng Jia, Xingxing Zhang, Yanan Cao, Zheng Lin, Shi Wang, Furu Wei

Straight to the Tree: Constituency Parsing with Neural Syntactic Distance (P18-1)

Challenge:	Existing methods to translate sentences to other languages using heuristics are challenging.
Approach:	They propose a model that learns hierarchical weights for different sets of labels and applies them to other languages to translate them.
Outcome:	The proposed model can translate English datasets to other languages and obtain different sets of labels again using heuristics.

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Yikang Shen, Zhouhan Lin, Athul Paul Jacob, Alessandro Sordoni, Aaron Courville, Yoshua Bengio

Words Worth a Thousand Pictures: Measuring and Understanding Perceptual Variability in Text-to-Image Generation (2024.emnlp-main)

Challenge:	Compared to traditional shift-reduce parsing schemes, our approach is free from the potentially disastrous compounding error.
Approach:	They propose a model that predicts a scalar for each split position in a sentence and then determines the topology of grammar tree based on syntactic distances.
Outcome:	The proposed model achieves the state-of-the-art single model F1 score of 92.1 on PTB and 86.4 on CTB dataset, surpassing the previous single model results by a large margin.

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Raphael Tang, Crystina Zhang, Lixinyu Xu, Yao Lu, Wenyan Li, Pontus Stenetorp, Jimmy Lin, Ferhan Ture

Open Hierarchical Relation Extraction (2021.naacl-main)

Challenge:	Current diffusion models do not cover recent models, thus we curate three test sets for evaluation.
Approach:	They propose a human-calibrated measure of variability in a set of images bootstrapped from existing image-pair perceptual distances.
Outcome:	The proposed model outperforms nine baselines by 18 points in accuracy and matches graded human judgements 78% of the time.

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Kai Zhang, Yuan Yao, Ruobing Xie, Xu Han, Zhiyuan Liu, Fen Lin, Leyu Lin, Maosong Sun

MSG-LLM: A Multi-scale Interactive Framework for Graph-enhanced Large Language Models (2025.coling-main)

Challenge:	Existing OpenRE methods cast different relation types in isolation without considering their hierarchical dependency.
Approach:	They propose a framework to establish bidirectional connections between OpenRE and relation hierarchies by integrating hierarchy information into relation representations.
Outcome:	The proposed framework outperforms state-of-the-art models on relation clustering and hierarchy expansion.

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Jiayu Ding, Zhangkai Zheng, Benshuo Lin, Yun Xue, Yiping Song

Rethinking Coherence Modeling: Synthetic vs. Downstream Tasks (2021.eacl-main)

Challenge:	Existing graph-enhanced large language models (LLMs) focus on matching subgraphs between subgraph and candidate subgraph at the same scale, neglecting that subgraph with different scales may also share similar semantics or structures.
Approach:	They propose to use graph kernel search to discover subgraphs from the entire graph to bridge the graph and LLMs, helping with graph retrieval and LRM generation.
Outcome:	The proposed method achieves state-of-the-art on two graph-based tasks and the results are published in the journal Nature.

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Tasnim Mohiuddin, Prathyusha Jwalapuram, Xiang Lin, Shafiq Joty

Expressing Visual Relationships via Language (P19-1)

Challenge:	Coherence models are typically evaluated only on synthetic tasks, which may not be representative of their performance in downstream applications.
Approach:	They compare models' performance on synthetic sentences with those on retrieval-based dialog.
Outcome:	The proposed models perform poorly on synthetic sentences and retrieval-based dialog tasks.

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Hao Tan, Franck Dernoncourt, Zhe Lin, Trung Bui, Mohit Bansal

Select2Reason: Efficient Instruction-Tuning Data Selection for Long-CoT Reasoning (2026.findings-acl)

Challenge:	Current studies on image captioning focus on single image, but there are no effective models for generating relational captions for two images.
Approach:	They propose a language-guided image editing dataset that contains real image pairs with corresponding editing instructions.
Outcome:	The proposed model outperforms baseline and existing methods on two datasets.

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Cehao Yang, Xueyuan Lin, Xiaojun Wu, Chengjin Xu, Xuhui Jiang, Honghao Liu, Hui Xiong, Jian Guo

How to Determine the Most Powerful Pre-trained Language Model without Brute Force Fine-tuning? An Empirical Survey (2023.findings-emnlp)

Challenge:	Large reasoning models exhibit human-like behaviors such as exploration, verification, reflection, and correction.
Approach:	They propose a supervised fine-tuning framework for long chain-of-thoughts reasoning . they leverage a difficulty-aware reward model to estimate the learning value of questions .
Outcome:	The proposed framework performs fine-tuning on large reasoning models on 10% of the data selected.

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Jun Bai, Xiaofeng Zhang, Chen Li, Hanhua Hong, Xi Xu, Chenghua Lin, Wenge Rong

Red-Teaming NSFW Image Classifiers as Text-to-Image Safeguards (2026.findings-acl)

Challenge:	Transferability estimation has been a topic of great interest in computer vision fields . a lack of a comprehensive comparison between these estimation methods is a problem .
Approach:	They conduct a thorough survey of existing methods to find the most suitable model . they also outline difficulties of consideration of training details and applicability to text generation .
Outcome:	The proposed methods perform well with superiorities in effectiveness and efficiency.

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Tinghao Xie, Yueqi Xie, Alireza Zareian, Shuming Hu, Felix Juefei-Xu, Xiaowen Lin, Ankit Jain, Prateek Mittal, Li Chen

QuackIR: Retrieval in DuckDB and Other Relational Database Management Systems (2025.emnlp-industry)

Challenge:	Not Safe for Work (NSFW) image classifiers play a critical role in safeguarding text-to-image systems.
Approach:	They propose an automated red-teaming framework that leverages a set of generative AI tools to uncover NSFW image failures.
Outcome:	The proposed framework uncovers and interprets failure modes and enables it to be applied to real-world T2I and T2V systems.

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Yijun Ge, Zijian Chen, Jimmy Lin

DMRetriever: A Family of Models for Improved Text Retrieval in Disaster Management (2026.acl-long)

Challenge:	Existing vector databases for RAG are needed for large language models, but there are no alternatives.
Approach:	They propose to leverage existing relational databases for retrieval-augmented generation . they use duckDB, SQLite, and PostgreSQL integrations to demonstrate their effectiveness .
Outcome:	The proposed approach is comparable to existing IR tools.

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Kai Yin, Xiangjue Dong, Chengkai Liu, Allen Lin, Lingfeng Shi, Ali Mostafavi, James Caverlee

Breaking the Generator Barrier: Disentangled Representation for Generalizable AI-Text Detection (2026.acl-long)

Challenge:	Existing models fail to handle the varied search intents inherent to disaster management scenarios, resulting in inconsistent and unreliable performance.
Approach:	They propose a new series of dense retrieval models tailored for disaster management that train on a three-stage framework with unsupervised contrastive pre-training and difficulty-aware progressive instruction fine-tuning.
Outcome:	The proposed model outperforms baseline models by 13.3 times and 33 times over baselines with only 7.6% of their parameters.

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Xiao Pu, Zepeng Cheng, Lin Yuan, Yu Wu, Xiuli Bi

Common Sense Beyond English: Evaluating and Improving Multilingual Language Models for Commonsense Reasoning (2021.acl-long)

Challenge:	a rapid proliferation of large language models (LLMs) generate text that increasingly resembles human writing . this makes it difficult to capture subtle cues that distinguish AI-generated content from human-written content .
Approach:	They propose a framework that disentangles AI-detection semantics from generator-aware artifacts by latent encoding and perturbation-based regularization.
Outcome:	The proposed framework disentangles AI-detection semantics from generator-aware artifacts on 20 representative LLMs across 7 categories.

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Bill Yuchen Lin, Seyeon Lee, Xiaoyang Qiao, Xiang Ren

The Linguistic Connectivities Within Large Language Models (2025.findings-acl)

Challenge:	Using multilingual language models, commonsense reasoning research has been limited to English.
Approach:	They propose a Mickey Probe task to evaluate commonsense across languages . they propose X-CSQA and XCODAH datasets to be translated to 14 languages based on the Mickey corpus .
Outcome:	The proposed method significantly improves sentence representations beyond English.

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Dan Wang, Boxi Cao, Ning Bian, Xuanang Chen, Yaojie Lu, Hongyu Lin, Jia Zheng, Le Sun, Shanshan Jiang, Bin Dong, Xianpei Han

UIOrchestra: Generating High-Fidelity Code from UI Designs with a Multi-agent System (2025.findings-emnlp)

Challenge:	Recent studies have discovered notable disparities in their performance across different languages.
Approach:	They conduct a systematic investigation into the behaviors of large language models across 27 different languages on 3 different scenarios and reveals a Linguistic Map correlates with the richness of available resources and linguistic family relations.
Outcome:	The proposed model demonstrates that there are significant disparities in performance across languages across 27 different languages on 3 different scenarios.

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Chuhuai Yue, Jiajun Chai, Yufei Zhang, Zixiang Ding, Xihao Liang, Peixin Wang, Shihai Chen, Wang Yixuan, null Wangyanping, Guojun Yin, Wei Lin

Efficient and Accurate Prompt Optimization: the Benefit of Memory in Exemplar-Guided Reflection (2025.acl-long)

Challenge:	Recent advances in large language models have significantly improved automated code generation . however, the translation of complex mobile UI designs into high-fidelity front-end code remains a challenge .
Approach:	They propose a collaborative multi-agent system to reconstruct static single-page apps from mockups.
Outcome:	The proposed system outperforms existing methods in reconstructing complex app pages . the code and data will be released upon paper acceptance .

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Cilin Yan, Jingyun Wang, Lin Zhang, Ruihui Zhao, Xiaopu Wu, Kai Xiong, Qingsong Liu, Guoliang Kang, Yangyang Kang

FeTaQA: Free-form Table Question Answering (2022.tacl-1)

Challenge:	Recent work utilizes feedbacks generated from erroneous cases to guide prompt optimization . previous methods rely on computational resources and powerful GPUs .
Approach:	They propose an automatic prompt engineering method that leverages feedbacks from erroneous cases to guide prompt optimization.
Outcome:	The proposed method surpasses state-of-the-art methods with less steps and lower computational resources.

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Linyong Nan, Chiachun Hsieh, Ziming Mao, Xi Victoria Lin, Neha Verma, Rui Zhang, Wojciech Kryściński, Hailey Schoelkopf, Riley Kong, Xiangru Tang, Mutethia Mutuma, Ben Rosand, Isabel Trindade, Renusree Bandaru, Jacob Cunningham, Caiming Xiong, Dragomir Radev, Dragomir Radev

On the Helpfulness of Document Context to Sentence Simplification (2020.coling-main)

Challenge:	Existing table-based question answering datasets lack advanced information-based questions that require reasoning and integration of information pieces retrieved from structured knowledge sources.
Approach:	They propose a dataset with 10K Wikipedia-based table, question, free-form answer, supporting table cells pairs that can be used to generate an answer.
Outcome:	The proposed dataset has 10K Wikipedia-based table, question, free-form answer, supporting table cells pairs.

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Renliang Sun, Zhe Lin, Xiaojun Wan

Trustworthy and Explainable Causal Representation Learning in Transformers (2026.findings-acl)

Challenge:	Text simplification is a hot issue in the field of natural language generation (NLG).
Approach:	They propose to use Wikipedia context to improve sentence simplification by using neural networks to learn the effects of preceding and following sentences on current sentences.
Outcome:	The proposed model outperforms the best performing model on the baseline dataset by 2.46 (7.22%).

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Yang Liu, Yinghao Zhang, Lin Liu, Jiuyong Li, Debo Cheng, Zaiwen Feng

PiKGL: Leveraging Pruned Knowledge Graphs for Explainable Stance Detection (2026.tacl-1)

Challenge:	Existing approaches to interpretable representation learning rely on masks that weight the significance of input features, but the origin of these masks is uncertain.
Approach:	They propose a causal framework that directly learns identifiable representations from attention weights rather than relying on importance masks.
Outcome:	The proposed framework learns identifiable and explainable representations from attention weights, rather than masks, and guarantees faithfulness on real-world datasets.

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Bingbing Wang, Jingjie Lin, Zhixin Bai, Xintong Song, Qianlong Wang, Min Yang, Xi Zeng, Jing Li, Ruifeng Xu

How Memory Management Impacts LLM Agents: An Empirical Study of Experience-Following Behavior (2026.acl-long)

Challenge:	Experimental results demonstrate that a Pruned interpretable knowledge Graph Learning framework for explainable stance detection is state-of-the-art for social media stance prediction.
Approach:	They propose a Pruned interpretable knowledge Graph Learning framework for explainable stance detection that incorporates commonsense knowledge and prunes redundant information to ensure precision and minimize noise.
Outcome:	The proposed framework achieves state-of-the-art on three public datasets.

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Zidi Xiong, Yuping Lin, Wenya Xie, Pengfei He, Zirui Liu, Jiliang Tang, Himabindu Lakkaraju, Zhen Xiang

P-MMEval: A Parallel Multilingual Multitask Benchmark for Consistent Evaluation of LLMs (2025.emnlp-main)

Challenge:	In practice, memory designs vary widely across agents due to their diverse objectives and functionalities.
Approach:	They conduct an empirical study on how memory management choices impact the LLM agents’ behavior, especially their long-term performance.
Outcome:	The proposed methods show that LLM agents display an experience-following property, which results in highly similar agent outputs.

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Yidan Zhang, Yu Wan, Boyi Deng, Baosong Yang, Hao-Ran Wei, Fei Huang, Bowen Yu, Dayiheng Liu, Junyang Lin, Fei Huang, Jingren Zhou

Beyond Silent Letters: Amplifying LLMs in Emotion Recognition with Vocal Nuances (2025.findings-naacl)

Challenge:	Recent advances in large language models showcase varied multilingual capabilities across tasks . previous assessments focused on fundamental natural language processing (NLP) or isolated capability-specific tasks.
Approach:	They propose a multilingual multitask benchmark to assess multilingual capabilities . they use a large-scale benchmark covering fundamental and capability-specialized datasets .
Outcome:	The proposed benchmark compares models and tasks across languages and tasks and examines knowledge transfer from English to other languages.

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Zehui Wu, Ziwei Gong, Lin Ai, Pengyuan Shi, Kaan Donbekci, Julia Hirschberg

EduBench: A Comprehensive Benchmarking Dataset for Evaluating Large Language Models in Diverse Educational Scenarios (2026.acl-long)

Challenge:	Recent studies have demonstrated that Large Language Models possess a form of emotional intelligence, capable of interpreting emotional stimuli in text.
Approach:	They propose a method that translates speech characteristics into natural language descriptions and integrates them into LLMs to perform multimodal emotion analysis via text prompts.
Outcome:	The proposed method outperforms baseline models that require structural modifications on two datasets showing significant improvements in emotion recognition accuracy.

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Bin Xu, Yu Bai, Huashan Sun, Yiguan Lin, Siming Liu, Xinyue Liang, Yaolin Li, Zhuangzhi Dong, Jingren Zhang, Yufan Deng, Xinyu Zou, Yang Gao, Heyan Huang

LLM4DistReconfig: A Fine-tuned Large Language Model for Power Distribution Network Reconfiguration (2025.naacl-long)

Challenge:	Existing benchmarks that focus on knowledge-intensive tasks do not reflect diverse educational scenarios.
Approach:	They propose a benchmark that incorporates 9 major scenarios and 4,000 educational contexts.
Outcome:	The proposed model performs comparable to state-of-the-art large models on the test set.

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Panayiotis Christou, Md. Zahidul Islam, Yuzhang Lin, Jingwei Xiong

Editing the Mind of Giants: An In-Depth Exploration of Pitfalls of Knowledge Editing in Large Language Models (2024.findings-emnlp)

Challenge:	Power distribution network reconfiguration is crucial for maintaining operational efficiency, reliability and adaptability in modern power networks.
Approach:	They propose a deep learning-based approach to solve a distribution network reconfiguration problem using inputs from a LLM.
Outcome:	The proposed model generates optimal configurations minimizing system loss for five individual and a combined test dataset.

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Cheng-Hsun Hsueh, Paul Huang, Tzu-Han Lin, Che Liao, Hung-Chieh Fang, Chao-Wei Huang, Yun-Nung Chen

Transferring Textual Preferences to Vision-Language Understanding through Model Merging (2025.acl-short)

Challenge:	Knowledge editing is a promising technique for updating factual knowledge in large language models (LLMs) but studies have identified side effects such as knowledge distortion and the deterioration of general abilities that have emerged after editing.
Approach:	They propose to evaluate the side effects of knowledge editing in large language models using metrics and benchmarks.
Outcome:	The results of the study highlight the limitations of current knowledge editing methods and outline potential research directions.

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Chen-An Li, Tzu-Han Lin, Yun-Nung Chen, Hung-yi Lee

MASPO: Unifying Gradient Utilization, Probability Mass, and Signal Reliability for Robust and Sample-Efficient LLM Reasoning (2026.acl-long)

Challenge:	Large vision-language models (LVLMs) perform outstandingly across multimodal tasks, but training them with preference data is computationally expensive.
Approach:	They propose to merge text-based reward models with LVLMs to create visionlanguage reward models (VLRMs) this approach offers an efficient method for incorporating textual preferences into LVRMs.
Outcome:	The proposed model improves over LVLMs’ scoring and text-based RMs, and offers an efficient method for incorporating textual preferences into LVRMs.

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Xiaoliang Fu, Jiaye Lin, Yangyi Fang, Binbin Zheng, Chaowen Hu, Zekai Shao, Cong Qin, Lu Pan, Ke Zeng, Xunliang Cai

Distilling Rule-based Knowledge into Large Language Models (2025.coling-main)

Challenge:	Existing RLVR algorithms rely on rigid, uniform, and symmetric trust region mechanisms . current algorithms lack robustness, asymmetric signal reliability and inefficient gradient utilization .
Approach:	They propose a framework to harmonize three dimensions of RLVR algorithms, a paper argues . a binary cutoff is used to discard valuable reinforcement signals, they argue .
Outcome:	The proposed framework outperforms baselines in evaluating a robust RLVR solution.

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Wenkai Yang, Yankai Lin, Jie Zhou, Ji-Rong Wen

Introducing Semantics into Speech Encoders (2023.acl-long)

Challenge:	Recent advances in large language models have broadened their applicability across diverse realworld scenarios.
Approach:	They propose to encode rule-based knowledge into large language models by using strong in-context abilities to extract the knowledge from the textual rules and then explicitly encode it into the parameters of LLMs.
Outcome:	The proposed learning paradigm is much more efficient than example-based learning in both sample size and generalization ability.

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Derek Xu, Shuyan Dong, Changhan Wang, Suyoun Kim, Zhaojiang Lin, Bing Liu, Akshat Shrivastava, Shang-Wen Li, Liang-Hsuan Tseng, Guan-Ting Lin, Alexei Baevski, Hung-yi Lee, Yizhou Sun, Wei Wang

ECom-Bench: Can LLM Agent Resolve Real-World E-commerce Customer Support Issues? (2025.emnlp-industry)

Challenge:	Existing self-supervised speech encoders contain primarily acoustic rather than semantic information.
Approach:	They propose a task-agnostic unsupervised way to incorporate semantic information from large language model (LLM) systems into self-supervised speech encoders without labeled audio transcriptions.
Outcome:	The proposed approach improves spoken language understanding (SLU) performance by over 5% on intent classification (IC), with modest gains in named entity resolution (NER) and slot filling (SF), and spoken question answering (SQA) score by over 22%.

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Haoxin Wang, Xianhan Peng, Huang Cheng, Yizhe Huang, Ming Gong, Chenghan Yang, Yang Liu, Jiang Lin

Challenge:	ECom-Bench is a benchmark framework for evaluating LLM agent with multimodal capabilities in e-commerce customer support domain.
Approach:	They introduce a benchmark framework for evaluating LLM agent with multimodal capabilities in the e-commerce customer support domain.
Outcome:	The proposed benchmark features dynamic user simulation based on persona information from real e-commerce customer interactions and a realistic task dataset derived from authentic ecommerce dialogues.

Incorporating Contextual and Syntactic Structures Improves Semantic Similarity Modeling (D19-1)

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Linqing Liu, Wei Yang, Jinfeng Rao, Raphael Tang, Jimmy Lin

MapGPT: Map-Guided Prompting with Adaptive Path Planning for Vision-and-Language Navigation (2024.acl-long)

Challenge:	Semantic similarity modeling is central to many NLP problems such as question answering.
Approach:	They propose a pairwise word interaction model with syntactic structure priors to explore their effectiveness.
Outcome:	Extensive evaluations on eight benchmark datasets show that incorporating structural information improves over strong baselines.

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Jiaqi Chen, Bingqian Lin, Ran Xu, Zhenhua Chai, Xiaodan Liang, Kwan-Yee Wong

Construction of a Chinese Corpus for the Analysis of the Emotionality of Metaphorical Expressions (P18-2)

Challenge:	Embodied agents equipped with GPT as their brains have extraordinary decision-making and generalization abilities across various tasks.
Approach:	They propose a map-based agent that introduces an online linguistic-formed map to encourage global exploration.
Outcome:	The proposed agent achieves state-of-the-art zero-shot performance on R2R and REVERIE simultaneously.

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Dongyu Zhang, Hongfei Lin, Liang Yang, Shaowu Zhang, Bo Xu

Arithmetic Control of LLMs for Diverse User Preferences: Directional Preference Alignment with Multi-Objective Rewards (2024.acl-long)

Challenge:	a corpus of 5,605 manually annotated sentences in Chinese is described . emotion is an abstract and vague conception, which is often described by metaphor .
Approach:	They propose to construct a corpus of metaphors annotated with emotion in Chinese . they use an annotation scheme to include linguistic metaphors, emotional categories and intensity .
Outcome:	The proposed corpus contains 5,605 manually annotated sentences in Chinese . the authors show that the corpus is large enough to analyze emotions .

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Haoxiang Wang, Yong Lin, Wei Xiong, Rui Yang, Shizhe Diao, Shuang Qiu, Han Zhao, Tong Zhang

Challenge:	Reinforcement Learning from Human Feedback (RLHF) relies on scalar rewards to capture user preferences.
Approach:	They propose a framework that integrates multi-objective reward modeling to represent diverse preference profiles.
Outcome:	The proposed method improves performance across reward objectives and targets.

Fortify the Shortest Stave in Attention: Enhancing Context Awareness of Large Language Models for Effective Tool Use (2024.acl-long)

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Yuhan Chen, Ang Lv, Ting-En Lin, Changyu Chen, Yuchuan Wu, Fei Huang, Yongbin Li, Rui Yan

DimABSA: Building Multilingual and Multidomain Datasets for Dimensional Aspect-Based Sentiment Analysis (2026.acl-long)

Challenge:	In this paper, we demonstrate that an inherent waveform pattern in the attention allocation of large language models significantly affects their performance in tasks demanding a high degree of context awareness.
Approach:	They propose a method that compensates an attention trough with an attention peak by a process to enhance the model's awareness to various contextual positions.
Outcome:	The proposed method improves the performance of a 7B model on the largest tool-use benchmark, comparable to that of GPT-4.

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Lung-Hao Lee, Liang-Chih Yu, Natalia V Loukachevitch, Ilseyar Alimova, Alexander Panchenko, Tzu-Mi Lin, Zhe-Yu Xu, Jian-Yu Zhou, Guangmin Zheng, Jin Wang, Sharanya Awasthi, Jonas Becker, Jan Philip Wahle, Terry Ruas, Shamsuddeen Hassan Muhammad, Saif M. Mohammad

Source-free Domain Adaptation for Aspect-based Sentiment Analysis (2024.lrec-main)

Challenge:	Existing ABSA research relies on coarse-grained categorical labels, which limits its ability to capture nuanced affective states.
Approach:	They propose a dimensional approach that represents sentiment with continuous valence–arousal (VA) scores, enabling fine-grained analysis at both the aspect and sentiment levels.
Outcome:	The proposed approach represents sentiment with continuous valence–arousal (VA) scores, enabling fine-grained analysis at both the aspect and sentiment levels.

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Zishuo Zhao, Ziyang Ma, Zhenzhou Lin, Jingyou Xie, Yinghui Li, Ying Shen

MAVEN-ERE: A Unified Large-scale Dataset for Event Coreference, Temporal, Causal, and Subevent Relation Extraction (2022.emnlp-main)

Challenge:	Unsupervised Domain Adaptation (UDA) of the Aspect-based Sentiment Analysis task is a data mining technique that involves aspect extraction and aspect sentiment classification subtasks.
Approach:	They propose a framework that allows model parameter transfer, not data transfer, between different domains.
Outcome:	The proposed framework performs competitively with traditional unsupervised domain adaptation methods under privacy conditions.

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Xiaozhi Wang, Yulin Chen, Ning Ding, Hao Peng, Zimu Wang, Yankai Lin, Xu Han, Lei Hou, Juanzi Li, Zhiyuan Liu, Peng Li, Jie Zhou

Maximum Entropy Loss, the Silver Bullet Targeting Backdoor Attacks in Pre-trained Language Models (2023.findings-acl)

Challenge:	Existing datasets only cover limited relation types at once, which prevents models from taking full advantage of relation interactions.
Approach:	They construct a large-scale human-annotated ERE dataset with improved annotation schemes to address these drawbacks.
Outcome:	The proposed dataset is larger than existing datasets of all the ERE tasks by at least an order of magnitude.

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Zhengxiao Liu, Bowen Shen, Zheng Lin, Fali Wang, Weiping Wang

MorphoBench: A Benchmark with Difficulty Adaptive to Model Reasoning (2026.findings-acl)

Challenge:	Existing backdoor defense paradigms focus on detecting and removing poisoned samples at pre-training or inference time.
Approach:	They propose a new approach where the backdoor attack is directly reversed by incorporating maximum entropy loss into training to neutralize the minimal cross-entropiness loss fine-tuning on poisoned data.
Outcome:	The proposed model significantly lowers the attack success rate on classification tasks and reduces the risk of backdoor attacks on clean data.

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Xukai Wang, Xuanbo Liu, Mingrui Chen, Haitian Zhong, Xuanlin Yang, Bohan Zeng, Jinbo Hu, Hao Liang, Junbo Niu, Xuchen Li, Ruitao Wu, Ruichuan An, Yang Shi, Liu Liu, Qiang Liu, Zhouchen Lin, Xu-Yao Zhang, Wentao Zhang, Bin Dong

From log 𝜋 to 𝜋: Taming Divergence in Soft Clipping via Bilateral Decoupled Decay of Probability Gradient Weight (2026.acl-long)

Challenge:	Existing benchmarks designed to evaluate the reasoning capabilities of large models are limited in scope and lack flexibility to adapt difficulty according to evolving reasoning capacities of models.
Approach:	They propose a benchmark that incorporates multidisciplinary questions to evaluate the reasoning capabilities of large models and can adjust and update question difficulty based on the reasoning abilities of advanced models.
Outcome:	The proposed benchmark incorporates multidisciplinary questions to evaluate the reasoning capabilities of large models and can adjust and update question difficulty based on the reasoning abilities of advanced models.

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Xiaoliang Fu, Jiaye Lin, Yangyi Fang, Chaowen Hu, Cong Qin, Zekai Shao, Binbin Zheng, Lu Pan, Ke Zeng

DiffusEmp: A Diffusion Model-Based Framework with Multi-Grained Control for Empathetic Response Generation (2023.acl-long)

Challenge:	Standard algorithms for Large Language Models (LLMs) enforce stability via "hard clipping" but relying on log-probability gradient yields divergent weights as probabilities vanish, destabilizing LLM training.
Approach:	They propose a decoupled gradient policy optimization that uses a decay mechanism to decouple the probability of a boundary token.
Outcome:	The proposed algorithm outperforms baselines on various mathematical benchmarks.

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Guanqun Bi, Lei Shen, Yanan Cao, Meng Chen, Yuqiang Xie, Zheng Lin, Xiaodong He

FVQA 2.0: Introducing Adversarial Samples into Fact-based Visual Question Answering (2023.findings-eacl)

Challenge:	Existing methods to generate empathetic responses are monotonous and generic, resulting in shallow empathy and few connections to the context.
Approach:	They propose to use explicit control to guide the empathy expression and a framework DiffusEmp to unify the utilization of dialogue context and attribute-oriented control signals.
Outcome:	The proposed framework outperforms baselines on EmpatheticDialogue in terms of controllability, informativeness, diversity, and diversity without the loss of context-relatedness.

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Weizhe Lin, Zhilin Wang, Bill Byrne

Pre-training Multilingual Neural Machine Translation by Leveraging Alignment Information (2020.emnlp-main)

Challenge:	Fact-based Visual Question Answering (FVQA) is a visual question answering task that requires information retrieval using common sense knowledge graphs to answer.
Approach:	They propose a new test question with adversarial variants to address this imbalance by using a KB-VQA dataset that is small and contains only one answer per question.
Outcome:	The proposed version reduces the vulnerability of the original FVQA dataset without human annotations.

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Zehui Lin, Xiao Pan, Mingxuan Wang, Xipeng Qiu, Jiangtao Feng, Hao Zhou, Lei Li

READoc: A Unified Benchmark for Realistic Document Structured Extraction (2025.findings-acl)

Challenge:	Existing pre-training methods are not effective for machine translation tasks.
Approach:	They propose a method to pre-train a universal multilingual neural machine translation model . they use random aligned substitution technique to bring words and phrases with similar meanings closer in the representation space.
Outcome:	The proposed approach improves translation quality on low, medium, rich resource languages.

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Zichao Li, Aizier Abulaiti, Yaojie Lu, Xuanang Chen, Jia Zheng, Hongyu Lin, Xianpei Han, Shanshan Jiang, Bin Dong, Le Sun

ALGOGEN: Tool-Generated Verifiable Traces for Reliable Algorithm Visualization (2026.findings-acl)

Challenge:	Document Structured Extraction (DSE) is a field of document structure analysis that aims to extract structured content from raw documents.
Approach:	They propose a benchmark to evaluate document structured extraction systems by converting unstructured PDFs into semantically rich Markdown.
Outcome:	The proposed benchmark is based on 3,576 diverse and real-world documents from arXiv, GitHub, and Zenodo.

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null Liaokunpeng, Yuexiao Ma, Yisheng Lin, Hualin Zeng, Xiawu Zheng, Rongrong Ji

Can’t Hide Behind the API: Stealing Black-Box Commercial Embedding Models (2025.findings-naacl)

Challenge:	Recent LLM-based systems require simulation of algorithm flow and video rendering constraints.
Approach:	They propose a paradigm that decouples algorithm execution from rendering.
Outcome:	The proposed paradigm reduces execution success rates, element overlap, and inter-frame inconsistencies.

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Manveer Singh Tamber, Jasper Xian, Jimmy Lin

CNNs for NLP in the Browser: Client-Side Deployment and Visualization Opportunities (N18-5)

Challenge:	a new study examines the retrieval effectiveness of commercial embedding models . robert mcgahey: can commercial embeds be "stolen" using distillation techniques? he says stealing models can offer benefits to different actors, including reduced costs and security .
Approach:	They propose to "steal" embedding models by training thief models on text–embedding pairs . they replicate retrieval effectiveness of commercial embeddable models with a cost of under $300 .
Outcome:	The proposed methods replicate retrieval effectiveness of commercial embedding models with under $300 . authors suggest measures to mitigate risk of model theft.

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Yiyun Liang, Zhucheng Tu, Laetitia Huang, Jimmy Lin

A Span-based Dynamic Local Attention Model for Sequential Sentence Classification (2021.acl-short)

Challenge:	a JavaScript implementation of a convolutional neural network performs feedforward inference completely in the browser.
Approach:	They propose a JavaScript implementation that performs feedforward inference completely in the browser.
Outcome:	The proposed model performs feedforward inference completely in the browser without server requests . the proposed model is useful for applications with stringent latency requirements or low connectivity .

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Xichen Shang, Qianli Ma, Zhenxi Lin, Jiangyue Yan, Zipeng Chen

CLARity: Reasoning Consistency Alone Can Teach Reinforced Experts (2026.acl-long)

Challenge:	Existing methods for sentence classification ignore latent segment structure of document, in which contiguous sentences have coherent semantics.
Approach:	They propose a span-based dynamic local attention model that captures structural information by supervised dynamic local focus.
Outcome:	The proposed model outperforms state-of-the-art models on two benchmark datasets.

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Jiuheng Lin, Cong Jiang, Zirui Wu, Jiarui Sun, Yansong Feng

Operation-guided Neural Networks for High Fidelity Data-To-Text Generation (D18-1)

Challenge:	Existing solutions to supervise the reasoning process are prohibitively expensive.
Approach:	They propose a cost-effective reinforcement learning framework that enhances reasoning quality using a small, general-purpose LLM only.
Outcome:	Experiments show that CLARity improves reasoning quality by 16.5% over standard outcome-based reinforcement learning (RL) human evaluations confirm substantial gains in factual correctness and reasoning coherence, leading to more trustworthy model outputs.

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Feng Nie, Jinpeng Wang, Jin-Ge Yao, Rong Pan, Chin-Yew Lin

Enhancing Biomedical Lay Summarisation with External Knowledge Graphs (2023.emnlp-main)

Challenge:	Recent neural models for data-to-text generation generate descriptions that are not consistent with structured data.
Approach:	They propose a framework for data-to-text generation that uses symbolic operations to generate texts from structured data.
Outcome:	The proposed framework improves the fidelity of the generated texts to the input structured data.

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Tomas Goldsack, Zhihao Zhang, Chen Tang, Carolina Scarton, Chenghua Lin

Knowledge Representation

Challenge:	Existing approaches to lay summarisation are reliant on the source article, which is unlikely to include all the information necessary for a lay audience.
Approach:	They augment existing biomedical lay summarisation dataset with article-specific knowledge graphs that contain detailed information on relevant biomedically related concepts.
Outcome:	The proposed methods improve readability and explanation of technical concepts by integrating graph-based domain knowledge within lay summarisation models.

ICL-Bandit: Relevance Labeling in Advertisement Recommendation Systems via LLM (2025.findings-emnlp)

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Lu Wang, Chiming Duan, Pu Zhao, Fangkai Yang, Yong Shi, Xuefeng Luo, Bingjing Xu, Weiwei Deng, Qingwei Lin, Dongmei Zhang

Video Caption Dataset for Describing Human Actions in Japanese (2020.lrec-1)

Challenge:	In-context learning (ICL) is a common practice to enhance LLM performance on domain-specific tasks.
Approach:	They propose a method that leverages large language models to enhance query-ad relevance labeling . they identify and provide superior demonstrations for ICL, thereby improving labeling performance .
Outcome:	The proposed method improves query-ad relevance labeling performance by providing demonstrations.

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Yutaro Shigeto, Yuya Yoshikawa, Jiaqing Lin, Akikazu Takeuchi

GAMEBoT: Transparent Assessment of LLM Reasoning in Games (2025.acl-long)

Challenge:	Existing video caption datasets for English have no equivalent for Japanese . authors evaluated two methods to obtain benchmark results .
Approach:	They propose to use Japanese video captions to describe human actions . they evaluated two different methods to obtain benchmark results .
Outcome:	The proposed dataset evaluates two different methods to obtain benchmark results . it shows that the generation methods can specify "who does what and where"

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Wenye Lin, Jonathan Roberts, Yunhan Yang, Samuel Albanie, Zongqing Lu, Kai Han

TIARA: Multi-grained Retrieval for Robust Question Answering over Large Knowledge Base (2022.emnlp-main)

Challenge:	Existing efforts to create benchmarks that move beyond superficial pattern recognition to delve into the profound reasoning skills required for problemsolving face challenges such as insufficient interpretability, performance saturation or data contamination.
Approach:	They propose a gaming arena designed for rigorous assessment of LLM reasoning capabilities.
Outcome:	The proposed framework decomposes complex reasoning into predefined modular subproblems and generates ground truth for these subproblem types.

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Yiheng Shu, Zhiwei Yu, Yuhan Li, Börje Karlsson, Tingting Ma, Yuzhong Qu, Chin-Yew Lin

MoEfication: Transformer Feed-forward Layers are Mixtures of Experts (2022.findings-acl)

Challenge:	KBQA is a challenging area for pre-trained language models due to its extensive space and complexity.
Approach:	They propose a model that uses multi-grained retrieval to focus on most relevant KB contexts . constrained decoding is used to control output space and reduce generation errors .
Outcome:	The proposed model outperforms existing models on GrailQA and WebQuestionsSP.

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Zhengyan Zhang, Yankai Lin, Zhiyuan Liu, Peng Li, Maosong Sun, Jie Zhou

Retrieval-Augmented Defense: Adaptive and Controllable Jailbreak Prevention for Large Language Models (2026.acl-long)

Challenge:	Recent work has shown that feed-forward networks (FFNs) in pre-trained Transformers are a key component, storing various linguistic and factual knowledge.
Approach:	They propose to convert a model into its MoE version with the same parameters and build expert routers to decide which experts will be used for each input.
Outcome:	The proposed model can use 10% to 30% of FFN parameters while maintaining over 95% original performance.

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Guangyu Yang, Jinghong Chen, Jingbiao Mei, Weizhe Lin, Bill Byrne

AutoEvolve: Automatically Evolving Queries for Applicable and Scalable Retrieval-Augmented Generation Benchmarking (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) remain vulnerable to jailbreak attacks due to evolving nature and diversity of attack strategies.
Approach:	They propose a framework for jailbreak detection that integrates a database of known attack examples into Retrieval-Augmented Generation to infer the underlying, malicious user query and jailbreak strategy used to attack the system.
Outcome:	The proposed framework reduces the effectiveness of strong jailbreak attacks while maintaining low rejection rates for benign queries.

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Ding-Chu Zhang, Xiaowen Zhang, Yue Fei, Renjun Hu, Xiao-Wen Yang, Zhi Zhou, Baixuan Li, Yu-Feng Li, Xing Shi, Wei Lin

MARCH: Multi-Agent Radiology Clinical Hierarchy for CT Report Generation (2026.acl-short)

Challenge:	Existing automated generation methods exhibit Weak Applicability and Weak Scalability . existing methods are limited by their reliance on metadata from specific corpora .
Approach:	They propose an approach to generate scalable RAG benchmarks using corpus-agnostic methods . they propose a difficulty-guided metric that directs query evolution process .
Outcome:	The proposed approach evolves queries significantly more challenging than existing methods . it is able to dynamically increase difficulty, limiting scalability of the query .

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Yi Lin, Yihao Ding, Yonghui Wu, Yifan Peng

Document-Level Multi-Event Extraction with Event Proxy Nodes and Hausdorff Distance Minimization (2023.acl-long)

Challenge:	Automated 3D radiology report generation suffers from clinical hallucinations and lacks the iterative verification characteristic of clinical workflows.
Approach:	They propose a multi-agent framework that emulates the professional hierarchy of radiology departments and assigns specialized roles to distinct agents.
Outcome:	The proposed framework outperforms state-of-the-art models in clinical fidelity and linguistic accuracy on the RadGenome-ChestCT dataset.

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Xinyu Wang, Lin Gui, Yulan He

On the Effectiveness of Sentence Encoding for Intent Detection Meta-Learning (2022.naacl-main)

Challenge:	Document-level multi-event extraction aims to extract the structural information from a given document automatically.
Approach:	They propose an alternative approach for document-level multi-event extraction with event proxy nodes and Hausdorff distance minimization.
Outcome:	The proposed method outperforms state-of-the-art methods on two datasets with only a fraction of training time.

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Tingting Ma, Qianhui Wu, Zhiwei Yu, Tiejun Zhao, Chin-Yew Lin

Global Encoding for Abstractive Summarization (P18-2)

Challenge:	Recent studies on few-shot intent detection have attempted to formulate the task as a meta-learning problem.
Approach:	They propose to modify a few-shot intent detection task to produce a non-trivially strong performance without further domain-specific adaptation.
Outcome:	The proposed model improves on the prototypical network variants with task-specific fine-tuning.

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Junyang Lin, Xu Sun, Shuming Ma, Qi Su

MathBench: Evaluating the Theory and Application Proficiency of LLMs with a Hierarchical Mathematics Benchmark (2024.findings-acl)

Challenge:	Existing models for abstractive summarization suffer from repetition and semantic irrelevance.
Approach:	They propose a global encoding framework which controls the information flow from the encoder to the decoder based on the global information of the source context.
Outcome:	The proposed model outperforms baseline models on the LCSTS and English Gigaword and can generate summary of higher quality and reduce repetition.

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Hongwei Liu, Zilong Zheng, Yuxuan Qiao, Haodong Duan, Zhiwei Fei, Fengzhe Zhou, Wenwei Zhang, Songyang Zhang, Dahua Lin, Kai Chen

Numerical Reasoning

Challenge:	Recent advances in large language models have showcased significant improvements in mathematics, but traditional benchmarks like GSM8k offer a unidimensional perspective.
Approach:	MathBench is a benchmark that rigorously assesses the mathematical capabilities of large language models.
Outcome:	MathBench spans a wide range of mathematical disciplines, offering a detailed evaluation of both theoretical understanding and practical problem-solving skills.

Pruning Redundant Mappings in Transformer Models via Spectral-Normalized Identity Prior (2020.findings-emnlp)

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Zi Lin, Jeremiah Liu, Zi Yang, Nan Hua, Dan Roth

Concept rather than Document: Context Compression via AMR-based Conceptual Entropy (2026.findings-acl)

Challenge:	Spectral-normalized identity priors (SNIP) is a structured pruning approach for a Transformer model.
Approach:	They propose a structured pruning approach which penalizes an entire residual module toward an identity mapping.
Outcome:	The proposed method improves on 5 GLUE benchmark tasks while maintaining comparable performance.

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Kaize Shi, Xueyao Sun, Xiaohui Tao, Lin Li, Qika Lin, Guandong Xu

Retrieve-and-Sample: Document-level Event Argument Extraction via Hybrid Retrieval Augmentation (2023.acl-long)

Challenge:	Existing methods rely on lexical or token-level features that fragment semantic units and fail to capture conceptually essential content.
Approach:	They propose an unsupervised framework leveraging Abstract Meaning Representation to preserve essential information while filtering irrelevant text.
Outcome:	The proposed framework outperforms RAG and existing baselines while preserving essential information while filtering irrelevant text.

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Yubing Ren, Yanan Cao, Ping Guo, Fang Fang, Wei Ma, Zheng Lin

OpenFMNav: Towards Open-Set Zero-Shot Object Navigation via Vision-Language Foundation Models (2024.findings-naacl)

Challenge:	Recent studies show the effectiveness of retrieval augmentation in many generative NLP tasks.
Approach:	They investigate retrieval settings from the input and label distribution views . they further augment document-level EAE with pseudo demonstrations sampled from event semantic regions .
Outcome:	The proposed methods can augment document-level EAE with pseudo demonstrations . the methods can be used in generative NLP tasks such as dialogue response generation .

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Yuxuan Kuang, Hai Lin, Meng Jiang

ChatMusician: Understanding and Generating Music Intrinsically with LLM (2024.findings-acl)

Challenge:	Existing methods for object navigation are limited to household datasets with close-set objects, and they lack the ability to generalize to new environments in a zero-shot manner.
Approach:	They propose a framework that leverages reasoning abilities of large vision language models to extract proposed objects from natural language instructions that meet the user’s demand.
Outcome:	The proposed framework surpasses baselines on all metrics and can be used in a HM3D ObjectNav benchmark.

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Ruibin Yuan, Hanfeng Lin, Yi Wang, Zeyue Tian, Shangda Wu, Tianhao Shen, Ge Zhang, Yuhang Wu, Cong Liu, Ziya Zhou, Liumeng Xue, Ziyang Ma, Qin Liu, Tianyu Zheng, Yizhi Li, Yinghao Ma, Yiming Liang, Xiaowei Chi, Ruibo Liu, Zili Wang, Chenghua Lin, Qifeng Liu, Tao Jiang, Wenhao Huang, Wenhu Chen, Jie Fu, Emmanouil Benetos, Gus Xia, Roger Dannenberg, Wei Xue, Shiyin Kang, Yike Guo

Beyond Static Artifacts: An Evolutionary Framework for Synthetic Claim Generation (2026.acl-long)

Challenge:	Despite LLMs' impressive capabilities in musical knowledge, music reasoning remains an unsolved task.
Approach:	They propose an open-source large language model (LLM) that integrates intrinsic musical abilities into LLaMA2 and GPT-3.5.
Outcome:	The proposed model can understand and generate music with a pure text tokenizer without external multi-modal neural structures or tokenizers.

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Yeqing Teng, Jiasheng Si, Shuxia Lin, Linhai Zhang, Weiyu Zhang, Wenpeng Lu, Deyu Zhou, Xiaoming Wu

Challenge:	Existing claim detection benchmarks treat claims as static textual artifacts . current research ignores sociological etiology of how information naturally emerges and mutates .
Approach:	They propose a socially generative framework for synthetic claim generation . they propose utterance, proposition and context-based simulations to capture truth decay .
Outcome:	The proposed paradigm models claims as socially evolving entities . it allows precise simulation of truth decay and intervened propagation with multi-auditor oversight .

Element Intervention for Open Relation Extraction (2021.acl-long)

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Fangchao Liu, Lingyong Yan, Hongyu Lin, Xianpei Han, Le Sun

Automatic Scene-based Topic Channel Construction System for E-Commerce (2022.emnlp-industry)

Challenge:	Current OpenRE models are often trained on the datasets generated from distant supervision, which often results in instability and makes the model easily collapsed.
Approach:	They propose to use a causal model to identify relation instances referring to the same relation . they propose to perform Element Interventions on context and entities respectively .
Outcome:	The proposed method outperforms existing methods and is robust across datasets.

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Peng Lin, Yanyan Zou, Lingfei Wu, Mian Ma, Zhuoye Ding, Bo Long

EfficientRAG: Efficient Retriever for Multi-Hop Question Answering (2024.emnlp-main)

Challenge:	Recent scene marketing has proved effective for offline shopping.
Approach:	They propose a novel product form, scene-based topic channel, which consists of a list of diverse products belonging to the same usage scenario and a topic title that describes the scenario with marketing words.
Outcome:	The proposed system can be automated and tested on a real-world e-commerce recommendation platform.

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Ziyuan Zhuang, Zhiyang Zhang, Sitao Cheng, Fangkai Yang, Jia Liu, Shujian Huang, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang, Qi Zhang

CATCH: A Novel Data Synthesis Framework for High Therapy Fidelity and Memory-Driven Planning Chain of Thought in AI Counseling (2025.findings-emnlp)

Challenge:	Existing retrieval-augmented generation methods rely on multiple calls of large language models (LLMs) Large-language models lack knowledge underrepresented in training data and still face hallucinations.
Approach:	They propose an efficient retriever for multi-hop question answering that generates new queries iteratively without the need for LLM calls.
Outcome:	The proposed method surpasses existing methods on three open-domain multi-hop question-answering datasets.

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Mingyu Chen, Jingkai Lin, Zhaojie Chu, Xiaofen Xing, Yirong Chen, Xiangmin Xu

WebUIBench: A Comprehensive Benchmark for Evaluating Multimodal Large Language Models in WebUI-to-Code (2025.findings-acl)

Challenge:	Existing studies employ a one-time generation approach to synthesize multi-turn dialogue samples, resulting in low therapy fidelity and failing to capture decision-making rationale behind each response.
Approach:	They propose a data synthesis framework that synthesizes multi-turn dialogue samples and incrementally generates stage-aligned counseling dialogues.
Outcome:	The proposed framework significantly improves therapy fidelity and logical coherence in AI counseling.

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Zhiyu Lin, Zhengda Zhou, Zhiyuan Zhao, Tianrui Wan, Yilun Ma, Junyu Gao, Xuelong Li

Cogito: A Cognitive Agentic Framework Driven by Dynamic Graph of Thoughts for Financial Report Generation (2026.findings-acl)

Challenge:	Existing benchmarks for large language models focus on webpage generation outcomes.
Approach:	They propose a multi-view evaluation framework to evaluate MLLMs in four key areas: WebUI Perception, HTML Programming, WebUI-HTML Understanding, and WebUI to code.
Outcome:	The proposed framework evaluates MLLMs in four key areas: WebUI Perception, HTML Programming, WebUI-HTML Understanding, and WebUI to code.

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Chen Lifan, Wei Ding, Jingwen Yang, Xiuze Zhou, Jingan Chen, Fan Lin

E2E-GMNER: End-to-End Generative Grounded Multimodal Named Entity Recognition (2026.findings-acl)

Challenge:	Existing approaches to financial report generation are insufficient to handle dynamic uncertainties of real-world financial environments.
Approach:	They propose a cognitively grounded agentic framework for professional financial report generation that is driven by Dynamic Graph of Thoughts and a social collaboration mechanism to facilitate coordinated agent interaction.
Outcome:	The proposed framework is based on a dynamic reasoning model and social collaboration mechanism.

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Meng Zhang, Jinzhong Ning, Xiaolong Wu, Hongfei Lin, Yijia Zhang

Training Language Models to Critique With Multi-agent Feedback (2025.findings-emnlp)

Challenge:	Existing approaches decouple textual entity recognition and visual grounding, leading to error accumulation and suboptimal joint optimization.
Approach:	They propose a fully end-to-end generative framework that unifies recognition, semantic typing, visual grounding and implicit knowledge reasoning within a single multimodal large language model.
Outcome:	The proposed framework achieves highly competitive performance compared with state-of-the-art methods.

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Tian Lan, Wenwei Zhang, Chengqi Lyu, Shuaibin Li, Chen Xu, Heyan Huang, Dahua Lin, Xian-Ling Mao, Kai Chen

Learning to Detect Noisy Labels Using Model-Based Features (2022.findings-emnlp)

Challenge:	utilizing human annotations can enhance critique ability, but model-generated critiques suffer from inherent flaws due to complexity of critique . a new framework that leverages multi-agent feedback improves critique ability .
Approach:	They propose a framework that leverages multi-agent feedback to improve critique ability . they propose to use supervised fine-tuning and reinforcement learning to improve this capability .
Outcome:	The proposed framework improves critique ability in both supervised fine-tuning and reinforcement learning stages.

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Zhihao Wang, Zongyu Lin, Junjie Wen, Xianxin Chen, Peiqi Liu, Guidong Zheng, Yujun Chen, Zhilin Yang

R-Tuning: Instructing Large Language Models to Say ‘I Don’t Know’ (2024.naacl-long)

Challenge:	Existing approaches to reduce label noise rely on heuristics and sample losses.
Approach:	They propose a method that transfers the noise distribution to a clean set and trains a model to distinguish noisy labels from clean ones using model-based features.
Outcome:	Empirically, the proposed approach improves over strong baselines on a wide range of tasks including text classification and speech recognition.

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Hanning Zhang, Shizhe Diao, Yong Lin, Yi Fung, Qing Lian, Xingyao Wang, Yangyi Chen, Heng Ji, Tong Zhang

APT: Improving Specialist LLM Performance with Weakness Case Acquisition and Iterative Preference Training (2025.findings-acl)

Challenge:	Existing methods for instruction tuning force the model to complete a sentence no matter whether it knows the knowledge or not.
Approach:	They propose a new approach to tuning large language models to refrain from answering questions beyond its parametric knowledge by identifying the disparity in parametric and parametric information.
Outcome:	The proposed approach improves a model’s ability to answer known questions and refrain from answering unknown questions.

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Jun Rao, Zepeng Lin, Xuebo Liu, Xiaopeng Ke, Lian Lian, Dong Jin, Shengjun Cheng, Jun Yu, Min Zhang

A3: Android Agent Arena for Mobile GUI Agents with Essential-State Procedural Evaluation (2026.findings-acl)

Challenge:	Large Language Models often require domain-specific fine-tuning to address targeted tasks, which risks degrading their general capabilities.
Approach:	They propose to use self-generated dis-preferred weakness data to enhance model performance with a targeted training approach that minimizes interference with existing knowledge base.
Outcome:	The proposed approach ensures no reduction in generic capacity and achieves superior performance on downstream tasks compared to existing methods.

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Yuxiang Chai, Shunye Tang, Han Xiao, Weifeng Lin, Hanhao Li, Jiayu Zhang, Liang Liu, Pengxiang Zhao, Guangyi Liu, Guozhi Wang, Shuai Ren, Rongduo Han, Haining Zhang, Siyuan Huang, Hongsheng Li

A Text-Centered Shared-Private Framework via Cross-Modal Prediction for Multimodal Sentiment Analysis (2021.findings-acl)

Challenge:	Existing evaluation methods for mobile GUI agents rely on static frame assessments or offline static apps.
Approach:	They propose an evaluation system that leverages large language models as reward models to verify task completion and process achievement.
Outcome:	The proposed system addresses the limitations of traditional function based evaluation methods on online dynamic apps.

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Yang Wu, Zijie Lin, Yanyan Zhao, Bing Qin, Li-Nan Zhu

A Practical Analysis of Human Alignment with *PO (2025.findings-naacl)

Challenge:	Existing studies treat all three modal features equally and implicitly explore the interactions between different modalities.
Approach:	They propose a text-centered shared-private framework for multimodal fusion . they propose modalities that can provide shared and private semantics .
Outcome:	The proposed framework outperforms baselines on the MOSEI and MOSI datasets.

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Kian Ahrabian, Xihui Lin, Barun Patra, Vishrav Chaudhary, Alon Benhaim, Jay Pujara, Xia Song

Challenge:	Prior research focused on identifying the best-performing method to varying hyperparameters . prior research focused primarily on a grid search, which can be impractical for general practitioners .
Approach:	They propose a preference optimization method that is more stable across hyperparameters and reduces the average response length.
Outcome:	The proposed method increases likelihood of achieving better results through various metrics, such as KL divergence and response length.

Neural Math Word Problem Solver with Reinforcement Learning (C18-1)

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Danqing Huang, Jing Liu, Chin-Yew Lin, Jian Yin

Connecting the Dots: Event Graph Schema Induction with Path Language Modeling (2020.emnlp-main)

Challenge:	Existing models for solving math word problems rely on predefined rules or feature engineering.
Approach:	They propose to incorporate copy and alignment mechanism into the sequence-to-sequence model to address two shortcomings . they use model output as a feature and incorporate it into the feature-based model to explore the effectiveness .
Outcome:	The proposed model outperforms the state-of-the-art models on the problem solving task.

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Manling Li, Qi Zeng, Ying Lin, Kyunghyun Cho, Heng Ji, Jonathan May, Nathanael Chambers, Clare Voss

Distantly Supervised Relation Extraction using Multi-Layer Revision Network and Confidence-based Multi-Instance Learning (2021.emnlp-main)

Challenge:	Existing methods to automate event extraction focus on uncertainty, re-occurring events and multiple hypotheses.
Approach:	They propose a new Event Graph Schema where two event types are connected through multiple paths involving entities that fill important roles in a coherent story.
Outcome:	The proposed model is highly effective at inducing salient and coherent schemas.

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Xiangyu Lin, Tianyi Liu, Weijia Jia, Zhiguo Gong

Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction (2021.acl-long)

Challenge:	Distantly supervised relation extraction is used in knowledge bases but its low quality and noisy sentences are present in sentence bags.
Approach:	They propose a multi-layer revision network which emphasizes inner-sentence correlations before extracting relevant information within sentences.
Outcome:	The proposed method improves on two New York Times datasets.

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Yaojie Lu, Hongyu Lin, Jin Xu, Xianpei Han, Jialong Tang, Annan Li, Le Sun, Meng Liao, Shaoyi Chen

The Lessons of Developing Process Reward Models in Mathematical Reasoning (2025.findings-acl)

Challenge:	Existing methods to extract event records from text decompose complex structure prediction task into multiple subtasks.
Approach:	They propose a sequence-to-structure generation paradigm that can extract events from text . they propose unified event extraction, constrained decoding algorithm and curriculum learning algorithm .
Outcome:	The proposed method can achieve competitive performance using record-level annotations in both supervised learning and transfer learning settings.

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Zhenru Zhang, Chujie Zheng, Yangzhen Wu, Beichen Zhang, Runji Lin, Bowen Yu, Dayiheng Liu, Jingren Zhou, Junyang Lin

Adjusting Image Attributes of Localized Regions with Low-level Dialogue (2020.lrec-1)

Challenge:	a recent study shows that process reward models can make mistakes, leading to wrong conclusions.
Approach:	They propose a consensus filtering mechanism that integrates MC estimation with LLM-as-a-judge to improve model performance and data efficiency.
Outcome:	The proposed model outperforms existing open-source alternatives and provides practical guidelines for future research.

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Tzu-Hsiang Lin, Alexander Rudnicky, Trung Bui, Doo Soon Kim, Jean Oh

Sketch and Refine: Towards Faithful and Informative Table-to-Text Generation (2021.findings-acl)

Challenge:	Image editing is time-consuming and requires a wide assortment of features and combinations of these features to achieve a desired effect.
Approach:	They propose a task-oriented dialogue system to investigate low-level instructions for NLIE . 25% of users found the system easy-to-use, resonating with their motivation .
Outcome:	The proposed system is easy-to-use and user-friendly.

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Peng Wang, Junyang Lin, An Yang, Chang Zhou, Yichang Zhang, Jingren Zhou, Hongxia Yang

AutoRAG-HP: Automatic Online Hyper-Parameter Tuning for Retrieval-Augmented Generation (2024.findings-emnlp)

Challenge:	Existing methods for table-to-text generation suffer from poor faithfulness and low coverage.
Approach:	They propose a method that combines Autoregressive and Non-Autoregressive generation to generate a table-to-text from a key-value table using a skeleton and an edit-based non-autoregressively generation model.
Outcome:	The proposed method outperforms the existing methods on WikiPerson and WikiBio datasets on coverage and faithfulness.

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Jia Fu, Xiaoting Qin, Fangkai Yang, Lu Wang, Jue Zhang, Qingwei Lin, Yubo Chen, Dongmei Zhang, Saravan Rajmohan, Qi Zhang

Reasoning Graph Enhanced Exemplars Retrieval for In-Context Learning (2025.coling-main)

Challenge:	Recent advances in Large Language Models have transformed ML/AI development . a reevaluation of AutoML principles for Retrieval-Augmented Generation (RAG) systems is needed.
Approach:	They propose a framework for hyper-parameter tuning and a hierarchical MAB method for efficient exploration of large search spaces.
Outcome:	The proposed framework outperforms baseline methods in more challenging optimization scenarios.

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Yukang Lin, Bingchen Zhong, Shuoran Jiang, Joanna Siebert, Qingcai Chen

Just Ask One More Time! Self-Agreement Improves Reasoning of Language Models in (Almost) All Scenarios (2024.findings-acl)

Challenge:	Existing methods focus on semantic similarity between queries and candidate exemplars, while logical connections between reasoning steps can be beneficial to depict problem-solving process.
Approach:	They propose a method to retrieve exemplars with semantic and structural similarity using a graph kernel.
Outcome:	The proposed method is superior to state-of-the-art retrieval-based approaches on mathematics and logical reasoning tasks.

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Lei Lin, Jiayi Fu, Pengli Liu, Qingyang Li, Yan Gong, Junchen Wan, Fuzheng Zhang, Zhongyuan Wang, Di Zhang, Kun Gai

Open Relation Extraction: Relational Knowledge Transfer from Supervised Data to Unsupervised Data (D19-1)

Challenge:	chain-of-thought (CoT) prompting has been shown to be effective on complex reasoning tasks, but the naive greedy decoding used in CoT prompting causes the repetitiveness and local optimality.
Approach:	They propose a generalizable ensemble-optimization method that uses a set of reasoning paths to prompt a language model one more time to determine the optimal answer.
Outcome:	The proposed method can be generalized to almost all scenarios where the type of input questions and answer format of reasoning paths may be unknown.

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Ruidong Wu, Yuan Yao, Xu Han, Ruobing Xie, Zhiyuan Liu, Fen Lin, Leyu Lin, Maosong Sun

BBScoreV2: Learning Time-Evolution and Latent Alignment from Stochastic Representation (2025.emnlp-main)

Challenge:	Existing methods to extract relational facts from open domain corpora are time-consuming and human-intensive.
Approach:	They propose a framework to learn similarity metrics of relations from labeled data . they propose to transfer relational knowledge to identify novel relations in unlabeled data.
Outcome:	Experiments on two real-world datasets show that the proposed framework improves compared with state-of-the-art methods.

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Tianhao Zhang, Zhecheng Sheng, Zhexiao Lin, Chen Jiang, Dongyeop Kang

Sibyl: Empowering Empathetic Dialogue Generation in Large Language Models via Sensible and Visionary Commonsense Inference (2025.coling-main)

Challenge:	Autoregressive generative models are gaining traction in language tasks such as text generation and machine translation.
Approach:	They propose a likelihood-based evaluation metric that fits transformer-based model embeddings into a stochastic process and propose it as a probability-based metric.
Outcome:	The proposed model embeddings induce a "clustered-to-temporal ordered" mapping of language model representations in high-dimensional space, and this structure enhances performance on tasks such as temporal consistency evaluation and AI-generated content detection.

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Lanrui Wang, Jiangnan Li, Chenxu Yang, Zheng Lin, Hongyin Tang, Huan Liu, Yanan Cao, Jingang Wang, Weiping Wang

Visual Enhanced Entity-Level Interaction Network for Multimodal Summarization (2024.findings-naacl)

Challenge:	Recent studies have focused on integrating commonsense knowledge into chatbots to enhance their ability to understand and generate dialogue responses.
Approach:	They propose a framework that integrates commonsense knowledge into chatbots to enable them to elicit more empathetic responses.
Outcome:	The proposed framework enables LLMs to uncover the implicit requirements of the conversation, aiming to elicit more empathetic responses.

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Haolong Yan, Binghao Tang, Boda Lin, Gang Zhao, Si Li

Neural-Symbolic Solver for Math Word Problems with Auxiliary Tasks (2021.acl-long)

Challenge:	Existing methods to generate concise summarizations rely on coarse-grained textual and visual information, but they are underutilized.
Approach:	They propose a Visual Enhanced Entity-Level Interaction Network to address underutilization of multimodal inputs at a fine-grained level.
Outcome:	The proposed model outperforms existing models on two MMS datasets and proposes new metrics to measure factual consistency of entities in the output.

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Jinghui Qin, Xiaodan Liang, Yining Hong, Jianheng Tang, Liang Lin

WordArt Designer: User-Driven Artistic Typography Synthesis using Large Language Models (2023.emnlp-industry)

Challenge:	Existing solutions for math word problems lack explicit integration of math symbolic constraints, leading to unexplainable and unreasonable predictions.
Approach:	They propose a novel mathematical model that explicitly incorporates symbolic constraints by auxiliary tasks to enforce different symbolic reasoning.
Outcome:	The proposed solver incorporates symbolic constraints by auxiliary tasks to enforce different symbolic reasoning.

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Jun-Yan He, Zhi-Qi Cheng, Chenyang Li, Jingdong Sun, Wangmeng Xiang, Xianhui Lin, Xiaoyang Kang, Zengke Jin, Yusen Hu, Bin Luo, Yifeng Geng, Xuansong Xie

Learning Like Humans: Advancing LLM Reasoning Capabilities via Adaptive Difficulty Curriculum Learning and Expert-Guided Self-Reformulation (2025.emnlp-main)

Challenge:	Existing typography solutions lack adaptability, creativity, and computational efficiency.
Approach:	They propose a user-driven framework for artistic typography synthesis based on the Large Language Model (LLM) the LLM Engine interprets user inputs and generates actionable prompts for the other modules, transforming abstract concepts into tangible designs.
Outcome:	The proposed framework incorporates four key modules: the LLM Engine, SemTypo, StyTyPo, and TexTyPO.

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Enci Zhang, Xingang Yan, Wei Lin, Tianxiang. Zhang, Lu Qianchun

RelCLIP: Adapting Language-Image Pretraining for Visual Relationship Detection via Relational Contrastive Learning (2022.emnlp-main)

Challenge:	Extensive experiments on challenging mathematical reasoning benchmarks demonstrate that these human-inspired strategies synergistically and significantly enhance performance.
Approach:	They propose to use Adaptive Difficulty Curriculum Learning and Expert-Guided Self-Reformulation to improve model performance.
Outcome:	Extensive experiments on mathematical reasoning benchmarks show that the proposed strategies synergistically and significantly improve performance over the baseline model.

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Yi Zhu, Zhaoqing Zhu, Bingqian Lin, Xiaodan Liang, Feng Zhao, Jianzhuang Liu

A Confidence-based Acquisition Model for Self-supervised Active Learning and Label Correction (2025.tacl-1)

Challenge:	Existing visual relationship detection models only use numeric ids of relation labels for training, but ignore semantic correlation between labels.
Approach:	They propose a visual Relationship prediction framework that transfers natural language knowledge from Contrastive Language-Image Pre-training models to enhance the relationship prediction.
Outcome:	The proposed framework improves visual relationship prediction by matching semantic correlations with relation triplets.

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Carel van Niekerk, Christian Geishauser, Michael Heck, Shutong Feng, Hsien-chin Lin, Nurul Lubis, Benjamin Ruppik, Renato Vukovic, Milica Gašić

Stable Language Guidance for Vision–Language–Action Models (2026.acl-long)

Challenge:	Existing approaches to training deep neural networks require large amounts of meticulously annotated data.
Approach:	They propose a pool-based active learning framework that requires expert annotators to label only a fraction of a sequence and facilitates self-supervision for the remainder of the sequence.
Outcome:	The proposed model outperforms baselines on dialogue belief tracking tasks.

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Zhihao Zhan, Yuhao Chen, Jiaying Zhou, Qinhan Lyu, Hao Liu, Keze Wang, Liang Lin, Guangrun Wang

ConvLab-3: A Flexible Dialogue System Toolkit Based on a Unified Data Format (2023.emnlp-demo)

Challenge:	Existing vision-Language-Action models are notoriously brittle to linguistic perturbations.
Approach:	They propose a probabilistic framework that disentangles physical affordance from semantic execution.
Outcome:	The proposed framework disentangles physical affordance from semantic execution.

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Qi Zhu, Christian Geishauser, Hsien-chin Lin, Carel van Niekerk, Baolin Peng, Zheng Zhang, Shutong Feng, Michael Heck, Nurul Lubis, Dazhen Wan, Xiaochen Zhu, Jianfeng Gao, Milica Gasic, Minlie Huang