Papers by Lu Lu
ITERATE: Image-Text Enhancement, Retrieval, and Alignment for Transmodal Evolution with LLMs (2025.coling-main)
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| Challenge: | a new framework for visual annotation of text-based questions is needed to improve performance . obtaining corresponding images through manual annotation often entails high costs . |
| Approach: | They propose a framework that uses visual modality to enhance the performance of text-based questions. |
| Outcome: | The proposed framework improves the alignment between text and images by using search engines or web scraping techniques. |
Progressive Tuning: Towards Generic Sentiment Abilities for Large Language Models (2024.findings-acl)
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| Challenge: | Existing models of sentiment understanding do not consider interrelated sentiment knowledge . et al., 2023; Zhao e.t., 20, 21; Shu e t. 2021) focus on individual sentiment subtasks . |
| Approach: | They propose an open-source large language model specific to the sentiment domain that explores hierarchical relationships between subtasks. |
| Outcome: | The proposed model performs well across all datasets in the progressive sentiment reasoning benchmark. |
Monitoring Decoding: Mitigating Hallucination via Evaluating the Factuality of Partial Response during Generation (2025.findings-acl)
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| 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. |
Implicit n-grams Induced by Recurrence (2022.naacl-main)
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| Challenge: | Recent studies show that self-attention based models have limitations on modeling sequential transformations. |
| Approach: | They propose to extract some explainable features from trained RNNs that are reminiscent of classical n-grams features. |
| Outcome: | The proposed models can model interesting linguistic phenomena such as negation and intensification. |
Parameter-Efficient Domain Knowledge Integration from Multiple Sources for Biomedical Pre-trained Language Models (2021.findings-emnlp)
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| Challenge: | Existing domain-specific pre-trained language models (PLMs) rely on self-supervised learning over large amounts of domain text, without explicitly integrating domain- specific knowledge. |
| Approach: | They propose to integrate domain knowledge from diverse sources into PLMs by using adapters that are pre-trained for individual domain knowledge sources and integrated via an attention-based knowledge controller. |
| Outcome: | The proposed architecture integrates domain knowledge from diverse sources into PLMs in a parameter-efficient way. |
Multi-label Few/Zero-shot Learning with Knowledge Aggregated from Multiple Label Graphs (2020.emnlp-main)
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| Challenge: | Few/zero-shot learning is a big challenge of many classification tasks, where a classifier is required to recognise instances of classes that have very few or even no training samples. |
| Approach: | They propose a multi-graph aggregation model that fuses knowledge from multiple label graphs encoding different semantic label relationships to improve multi-label zero/few-shot document classification. |
| Outcome: | The proposed model improves on two large clinical datasets and the EU legislation dataset on few/zero-shot labels. |
Conundrums in Event Coreference Resolution: Making Sense of the State of the Art (2021.emnlp-main)
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| Challenge: | Recent years have seen the successful application of span-based neural models to entity-based information extraction tasks such as entity coreference resolution (CR) Existing event coreference resolvers focused on feature engineering are few and far between, let alone event corefers. |
| Approach: | They propose to adapt existing span-based event reference systems to event coreference by adapting the models originally developed for entity coreference to event CR. |
| Outcome: | The proposed model improves the representations of entity mentions in entity-based IE tasks compared to non-span models . |
TeleMelody: Lyric-to-Melody Generation with a Template-Based Two-Stage Method (2022.emnlp-main)
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Zeqian Ju, Peiling Lu, Xu Tan, Rui Wang, Chen Zhang, Songruoyao Wu, Kejun Zhang, Xiang-Yang Li, Tao Qin, Tie-Yan Liu
| Challenge: | a new lyric-to-melody generation system bridges the gap between lyrics and melodies . previous generation systems lack paired data and lack of control on generated melodie. |
| Approach: | They develop a lyric-to-melody generation system with music template to bridge the gap between lyrics and melodies. |
| Outcome: | The proposed system bridges the gap between lyrics and melodies by using music template. |
Visually Guided Generative Text-Layout Pre-training for Document Intelligence (2024.naacl-long)
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| Challenge: | Prior work shows that pre-training techniques can boost the performance of visual document understanding (VDU) . Xu et al., 2020;; Gu e t al, 2021;; Appalaraju e al. 2022) |
| Approach: | They propose a visually guided generative text-layout pre-training method that optimizes hierarchical language and layout modeling objectives to generate interleaved text and layout sequences. |
| Outcome: | The proposed model can process word-intensive documents of any length and achieves competitive performance over baselines on VDU tasks. |
Enhance Robustness of Language Models against Variation Attack through Graph Integration (2024.lrec-main)
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| Challenge: | Pre-trained language models (PLMs) are used in many NLP applications but their vulnerability to adversarial attacks can lead to false or misleading information being distributed. |
| Approach: | They propose a method to incorporate a Chinese character variation graph into pre-trained language models to increase their robustness against character variation attacks in Chinese content. |
| Outcome: | The proposed method outperforms existing language models in combating adversarial attacks in Chinese content. |
Optimizing Code Retrieval: High-Quality and Scalable Dataset Annotation through Large Language Models (2024.emnlp-main)
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| Challenge: | Existing methods for code retrieval struggle to balance scalability and annotation quality. |
| Approach: | They propose a method that integrates functions called within the repository and information on third-party APIs to enhance the annotation context. |
| Outcome: | The proposed method improves the annotation context by incorporating functions called within the repository and information on third-party API functionalities. |
Lifelong Learning of Topics and Domain-Specific Word Embeddings (2021.findings-acl)
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| Challenge: | Existing lifelong topic models focus on indomain text streams in which each chunk only contains documents from a single domain. |
| Approach: | They develop a lifelong collaborative model that uses non-negative matrix factorization to learn topics and domain-specific word embeddings. |
| Outcome: | The proposed model can learn topics and domain-specific word embeddings from a lifelong collaborative model. |
Neural Topic Modeling with Large Language Models in the Loop (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) have demonstrated promising capabilities in topic discovery, but their direct application to topic modeling suffers from issues such as incomplete topic coverage, misalignment of topics, and inefficiency. |
| Approach: | They propose a novel LLM-in-the-loop framework that integrates Large Language Models with Neural Topic Models (NTMs) global topics and document representations are learned through the NTM, while an LLM refines these topics using an Optimal Transport (OT)-based alignment objective. |
| Outcome: | The proposed framework improves topic interpretability while preserving the efficiency of existing NTMs. |
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
| 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. |
Transparent Reference-free Automated Evaluation of Open-Ended User Survey Responses (2025.emnlp-industry)
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| Challenge: | Existing methods to evaluate open-ended survey responses are expensive and lack ground-truth reference for comparison. |
| Approach: | They propose a two-stage evaluation framework specifically designed for human survey responses that uses gibberish filtering to remove nonsensical responses. |
| Outcome: | The proposed evaluation framework outperforms existing metrics on English and Korean datasets and shows strong correlations with expert assessment. |
WhiteningBERT: An Easy Unsupervised Sentence Embedding Approach (2021.findings-emnlp)
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| Challenge: | Pre-trained language models perform well on learning sentence semantics when fine-tuned with supervised data. |
| Approach: | They conduct a thorough examination of pretrained model based unsupervised sentence embeddings. |
| Outcome: | The proposed approach improves on whitening-based vector normalization with less than 10 lines of code. |
Med-VRAgent: A Framework for Medical Visual Reasoning-Enhanced Agents (2025.emnlp-main)
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| Challenge: | Visual Language Models (VLMs) have shown strong performance in tasks like radiology report generation but struggle with hallucinations, vague descriptions, Inconsistent logic and poor localization. |
| Approach: | They propose a framework for medical visual reasoning based on Visual Guidance and Self-Reward paradigms and Monte Carlo Tree Search to improve the model's visual reasoning capabilities. |
| Outcome: | The proposed framework outperforms existing models on multiple medical VQA benchmarks. |
Two Heads Are Better Than One: Dual-Model Verbal Reflection at Inference-Time (2025.emnlp-main)
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| 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. |
Pretrain-Finetune Based Training of Task-Oriented Dialogue Systems in a Real-World Setting (2021.naacl-industry)
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| Challenge: | a challenge in building task-oriented dialogue systems is the limited amount of supervised training data available. |
| Approach: | They propose a method for training retrieval-based dialogue systems using annotated data and a larger, unlabeled dataset. |
| Outcome: | The proposed method improves model performance offline and online compared with no pretraining . the model is deployed in an agent-support application and evaluated on live customer service contacts . |
Are U a Joke Master? Pun Generation via Multi-Stage Curriculum Learning towards a Humor LLM (2024.findings-acl)
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Yang Chen, Chong Yang, Tu Hu, Xinhao Chen, Man Lan, Li Cai, Xinlin Zhuang, Xuan Lin, Xin Lu, Aimin Zhou
| 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. |
AGD: Adversarial Game Defense Against Jailbreak Attacks in Large Language Models (2025.acl-long)
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Shilong Pan, Zhiliang Tian, Zhen Huang, Wanlong Yu, Zhihua Wen, Xinwang Liu, Kai Lu, Minlie Huang, Dongsheng Li
| Challenge: | Existing defenses, including post-training alignment and prompt engineering, struggle with adaptability to out-of-distribution (OOD) attacks. |
| Approach: | They propose an adversarial game-based defense method that dynamically adjusts LLMs’ internal representations to achieve a balanced trade-off between helpfulness and harmlessness. |
| Outcome: | The proposed method improves LLMs’ safety over all baselines. |
ScaleBox: Enabling High-Fidelity and Scalable Code Verification for Large Language Models (2026.acl-demo)
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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
| 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. |
Controllable Summarization with Constrained Markov Decision Process (2021.tacl-1)
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| Challenge: | Existing controllable summarization models do not allow users to specify their preference for a particular attribute of the generated summaries. |
| Approach: | They propose a novel training framework based on Constrained Markov Decision Process (CMDP) that includes a reward function and constraints to facilitate better summarization control. |
| Outcome: | The proposed model can be applied to control important attributes of summarization, including length, covered entities, and abstractiveness, while complying with a given attribute’s requirement. |
Less is More: Pretrain a Strong Siamese Encoder for Dense Text Retrieval Using a Weak Decoder (2021.emnlp-main)
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Shuqi Lu, Di He, Chenyan Xiong, Guolin Ke, Waleed Malik, Zhicheng Dou, Paul Bennett, Tie-Yan Liu, Arnold Overwijk
| Challenge: | Dense retrieval requires high-quality text sequence embeddings to support effective search in the representation space. |
| Approach: | They propose a self-learning method that pre-trains the autoencoder using a weak decoder to push the encoder to provide better sequence representations. |
| Outcome: | The proposed model significantly boosts the effectiveness and few-shot ability of dense retrieval models on web search, news recommendation, and open domain question answering. |
Discrete Optimization for Unsupervised Sentence Summarization with Word-Level Extraction (2020.acl-main)
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| Challenge: | Sentence summarization systems that use latent space to reconstruct the source sentence are unwillingly exploited. |
| Approach: | They propose a method that uses language modeling and semantic similarity metrics to find a high-scoring summary. |
| Outcome: | The proposed method achieves state-of-the-art for unsupervised sentence summarization according to ROUGE scores. |
RRHF-V: Ranking Responses to Mitigate Hallucinations in Multimodal Large Language Models with Human Feedback (2025.coling-main)
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| 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. |
Resonance RoPE: Improving Context Length Generalization of Large Language Models (2024.findings-acl)
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| Challenge: | Recent advances in Large Language Models (LLMs) have demonstrated their potential across a wide spectrum of natural language processing tasks. |
| Approach: | They propose a novel approach to narrow the generalization gap in TSTL scenarios by refining the interpolation of RoPE features for OOD positions. |
| Outcome: | The proposed approach improves performance without additional online computational costs on train-short-test-long scenarios. |
MM-StanceDet: Retrieval-Augmented Multi-modal Multi-agent Stance Detection (2026.acl-long)
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| Challenge: | Existing methods for multimodal stance detection face contextual grounding, cross-modal interpretation ambiguity, and single-pass reasoning fragility. |
| Approach: | They propose a multi-agent framework that integrates Retrieval Augmentation for contextual grounding, specialized Multimodal Analysis agents for nuanced interpretation, Reasoning-Enhanced Debate stage and Self-Reflection for robust adjudication. |
| Outcome: | Extensive experiments on five datasets show that the proposed framework outperforms state-of-the-art methods. |
From Isolated Scoring to Collaborative Ranking: A Comparison-Native Framework for LLM-Based Paper Evaluation (2026.findings-acl)
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Pujun Zheng, Jiacheng Yao, Jinquan Zheng, Chenyang Gu, Guoxiu He, Jiawei Liu, Yong Huang, Tianrui Guo, Wei Lu
| Challenge: | Large language models (LLMs) are currently used to evaluate scientific papers by assigning an absolute score to each paper independently. |
| Approach: | They propose a comparison-native framework for paper evaluation that integrates comparison into both data construction and model learning. |
| Outcome: | The proposed framework achieves an average relative improvement of 21.8% over the strong baseline DeepReview-14B, while exhibiting robust generalization to five previously unseen datasets. |
META-GUI: Towards Multi-modal Conversational Agents on Mobile GUI (2022.emnlp-main)
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| Challenge: | Current task-oriented dialogue systems focus on multi-turn text/speech interaction, then call back-end APIs to perform task. |
| Approach: | They propose a GUI-based task-oriented dialogue system that can perform GUI operations on real APPs without invoking TOD-specific backend APIs. |
| Outcome: | The proposed GUI-based task-oriented dialogue system can perform GUI operations on real APPs and execute tasks without invoking TOD-specific backend APIs. |
ARise: Towards Knowledge-Augmented Reasoning via Risk-Adaptive Search (2025.acl-long)
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Yize Zhang, Tianshu Wang, Sirui Chen, Kun Wang, Xingyu Zeng, Hongyu Lin, Xianpei Han, Le Sun, Chaochao Lu
| 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%. |
SymPyBench: A Dynamic Benchmark for Scientific Reasoning with Executable Python Code (2026.eacl-industry)
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| Challenge: | Existing benchmarks do not capture the complexity of structured, step-by-step reasoning essential in physics and related domains. |
| Approach: | They propose a large-scale synthetic benchmark of 15K university-level physics problems . they use structured, step-by-step reasoning and executable Python code to produce the ground-truth solution. |
| Outcome: | The proposed model is based on a set of 15K university-level physics problems with three question types. |
Partner Personas Generation for Dialogue Response Generation (2022.naacl-main)
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| Challenge: | Existing frameworks that focus on self personas ignore the value of partner persona . experimental results show that our framework generates relevant, interesting, coherent and informative partner personages even compared to ground truth partner personagers. |
| Approach: | They propose a framework that leverages automatic partner personas generation to enhance dialogue response generation. |
| Outcome: | The proposed framework generates relevant, interesting, coherent and informative partner personas even compared to ground truth partner person . it surpasses baselines that condition on ground truth persona . |
D2-RAG: Dual-Decision Retrieval-Augmented Generation via Multi-Dimensional Uncertainty and Utility-Aware Decoding (2026.findings-acl)
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Jinshuo Zhang, Xiaoding Zhou, Weiyu Zhang, Guoqiang Chen, Ying Lian, Xiaoyang Meng, Yonghe Chen, Hongjiao Guan, Jiasheng Si, Wenpeng Lu
| Challenge: | Retrieval-Augmented Generation (RAG) mitigates hallucinations in large language models by incorporating external knowledge. |
| Approach: | They propose a dual-decision retrieval-augmented generation that integrates multi-dimensional uncertainty estimation to decide whether to retrieve and employs adaptive contrastive decoding to handle retrieved contexts of varying quality. |
| Outcome: | The proposed model outperforms baselines on four medical question-answering datasets while suppressing interference from noisy contexts. |
Cross-media Structured Common Space for Multimedia Event Extraction (2020.acl-main)
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| Challenge: | We propose a new task to extract events and their arguments from multimedia documents . traditional methods target text, images or videos, but multimedia content is distributed via multimedia . |
| Approach: | They propose a method that encodes structured representations of semantic information from textual and visual data into a common embedding space. |
| Outcome: | The proposed method achieves 4.0% and 9.8% absolute gains on text event argument role labeling and visual event extraction. |
TreeBoN: Enhancing Inference-Time Alignment with Speculative Tree-Search and Best-of-N Sampling (2025.findings-emnlp)
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Jiahao Qiu, Yifu Lu, Yifan Zeng, Jiacheng Guo, Jiayi Geng, Chenhao Zhu, Xinzhe Juan, Ling Yang, Huazheng Wang, Kaixuan Huang, Yue Wu, Mengdi Wang
| Challenge: | Best-of-N (BoN) sampling generates multiple responses and selects the best one, achieving improved performance but with a high computational cost. |
| Approach: | They propose a framework that integrates a speculative tree-search strategy into Best-of-N (BoN) Sampling. |
| Outcome: | The proposed framework outperforms Best-of-N (BoN) sampling but has high computational cost . tree-search strategy reduces computational overhead while maintaining high output quality . |
InfiniteWeb: Scalable Web Environment Synthesis for GUI Agent Training (2026.acl-long)
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| Challenge: | Existing GUI agent benchmarks are manually constructed and lack scale and diversity as training environments. |
| Approach: | They propose a GUI agent training system that automatically generates web environments at scale. |
| Outcome: | The proposed system outperforms commercial GUI agents at realistic website construction and improves on OSWorld and Online-Mind2Web. |
Cross-lingual Structure Transfer for Relation and Event Extraction (D19-1)
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| Challenge: | Existing approaches to identify complex semantic structures are difficult to train from under-annotated sources. |
| Approach: | They exploit relation- and event-relevant language-universal features to train relation or event extractors from source annotations and apply them to target languages. |
| Outcome: | The proposed approach achieves comparable performance to state-of-the-art models trained on 3,000 manually annotated mentions. |
Modality Matching Matters: Calibrating Language Distances for Cross-Lingual Transfer in URIEL+ (2026.eacl-srw)
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York Hay Ng, Aditya Khan, Xiang Lu, Matteo Salloum, Michael Zhou, Phuong Hanh Hoang, A. Seza Doğruöz, En-Shiun Annie Lee
| Challenge: | Existing linguistic knowledge bases such as URIEL+ lack a principled method for aggregating these signals into a single, comprehensive score. |
| Approach: | They propose a framework for type-matched language distances that unifies these signals into a robust, task-agnostic composite distance. |
| Outcome: | The proposed representations improve transfer performance when the distance type is relevant to the task, while yielding gains in most tasks. |
All Languages Matter: Understanding and Mitigating Language Bias in Multilingual RAG (2026.acl-long)
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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
| 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. |
MathGenie: Generating Synthetic Data with Question Back-translation for Enhancing Mathematical Reasoning of LLMs (2024.acl-long)
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| Challenge: | Existing models have demonstrated outstanding capabilities in mathematical reasoning, but there is a performance gap between open-source models and closed-source ones. |
| Approach: | They propose a method for generating diverse and reliable math problems by leveraging the ground-truth solutions of the seed data. |
| Outcome: | The proposed model outperforms open-source models across five representative mathematical reasoning datasets. |
Improving End-to-End Speech Processing by Efficient Text Data Utilization with Latent Synthesis (2023.findings-emnlp)
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| Challenge: | Latent Synthesis is an efficient textual data utilization framework for end-to-end speech processing models . labeled speech data are scarcer and more expensive for collection compared to textual ones . |
| Approach: | They propose a textual data utilization framework for E2E speech processing models . they train a latent synthesizer to convert textual information into an intermediate latent representation . |
| Outcome: | The proposed framework improves on low-resource speech recognition and spoken language understanding tasks. |
RSMeM: Knowledge-Enhanced Memory Evolution for Remote Sensing Agents with Systematic Evaluation (2026.acl-long)
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Bingxian Wu, Yu Zhang, Zonghao Guo, Tang Liu, Chen Qian, Yuxiang Lu, Xingbo Du, Yanghao Li, Yidan Zhang, Chi Chen, Ling Yao, Chenghu Zhou, Maosong Sun
| Challenge: | Existing RS agents built on general-purpose LLMs are domain-agnostic, resulting in brittle and error-prone workflows. |
| Approach: | They propose a knowledge-enhanced memory evolution mechanism that bootstraps RS agents with pre-distilled domain knowledge and iteratively integrates online experience for robust multi-step tool execution. |
| Outcome: | Experiments show that the new model improves tool-use performance and accuracy . iteratively, iteration of the model integrates online experience for robust multi-step tool execution . |
MusicAgent: An AI Agent for Music Understanding and Generation with Large Language Models (2023.emnlp-demo)
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| Challenge: | MusicAgent integrates numerous music-related tools and an autonomous workflow to address user requirements. |
| Approach: | a new system is built to integrate music-related tools and an autonomous workflow . the system is based on large language models (LLMs) that can be used to organize and decompose requests . |
| Outcome: | the proposed system integrates numerous music-related tools and an autonomous workflow to address user requirements. |
Exploiting Curriculum Learning in Unsupervised Neural Machine Translation (2021.findings-emnlp)
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| Challenge: | Experimental results show that the proposed method achieves consistent improvements with faster convergence speed. |
| Approach: | They propose a curriculum learning method to gradually utilize pseudo bi-texts based on their quality from multiple granularities. |
| Outcome: | The proposed method achieves consistent improvements with faster convergence speed on WMT 14 En-Fr, WMT14 En-De, and LDC En-Zh translation tasks. |
SmartBench: Is Your LLM Truly a Good Chinese Smartphone Assistant? (2025.emnlp-main)
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| Challenge: | Existing evaluation benchmarks for Large Language Models focus on objective tasks like mathematics and coding in English, which do not reflect the practical use cases of on-device LLMs in real-world mobile scenarios. |
| Approach: | They propose a benchmark to evaluate the capabilities of on-device Large Language Models in Chinese mobile contexts. |
| Outcome: | The proposed framework evaluates on-device LLMs and MLLMs in Chinese . it provides a standardized framework for evaluating LLM performance on real smartphones . |
BOLT: Fast Energy-based Controlled Text Generation with Tunable Biases (2023.acl-short)
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| Challenge: | Energy-based models (EBMs) have gained popularity for controlled text generation due to their high applicability to a wide range of constraints. |
| Approach: | They propose a language model with tunable biases to adjust the language model’s output logits. |
| Outcome: | The proposed model maintains the generator’s autoregressive nature to assert a strong control on token-wise conditional dependencies and overall fluency, and converges faster. |
LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing (2024.emnlp-main)
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Jiangshu Du, Yibo Wang, Wenting Zhao, Zhongfen Deng, Shuaiqi Liu, Renze Lou, Henry Zou, Pranav Narayanan Venkit, Nan Zhang, Mukund Srinath, Haoran Zhang, Vipul Gupta, Yinghui Li, Tao Li, Fei Wang, Qin Liu, Tianlin Liu, Pengzhi Gao, Congying Xia, Chen Xing, Cheng Jiayang, Zhaowei Wang, Ying Su, Raj Shah, Ruohao Guo, Jing Gu, Haoran Li, Kangda Wei, Zihao Wang, Lu Cheng, Surangika Ranathunga, Meng Fang, Jie Fu, Fei Liu, Ruihong Huang, Eduardo Blanco, Yixin Cao, Rui Zhang, Philip Yu, Wenpeng Yin
| Challenge: | a comparative analysis of paper (meta-)reviews by large language models (LLMs) aims to identify and distinguish LLMs from human activities . |
| Approach: | They present a comparative analysis to identify and distinguish LLM activities from human activities. |
| Outcome: | The proposed analysis aims to improve recognition of instances when someone implicitly uses LLMs for reviewing activities. |
SPARTA: Efficient Open-Domain Question Answering via Sparse Transformer Matching Retrieval (2021.naacl-main)
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| Challenge: | SPARTA is a novel neural retrieval method for open-domain question answering . it learns a sparse representation that can be efficiently implemented as an Inverted Index . |
| Approach: | They propose a method that learns a sparse representation that can be implemented as an Inverted Index. |
| Outcome: | The proposed method achieves state-of-the-art results on 4 open-domain question answering tasks and 11 retrieval question answering (ReQA) tasks. |
How are Prompts Different in Terms of Sensitivity? (2024.naacl-long)
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| Challenge: | In-context learning (ICL) has become one of the most popular learning paradigms due to the rapid development of large language models (LLMs). |
| Approach: | They propose a prompt analysis based on sensitivity and introduce sensitivity-aware decoding which incorporates sensitivity estimation as a penalty term in the standard greedy decoding. |
| Outcome: | The proposed approach is particularly useful when information in the input is scarce. |
FinToolSyn: A forward synthesis Framework for Financial Tool-Use Dialogue Data with Dynamic Tool Retrieval (2026.findings-acl)
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Caishuang Huang, Yang Qiao, Rongyu Zhang, Junjie Ye, Pu Lu, null Wuwenxi, Meng Zhou, Xiku Du, Qi Zhang, Tao Gui, Xuanjing Huang
| Challenge: | Existing data synthesis methods rely on static tools to generate queries . this approach fails to capture the implicit, event-driven nature of real-world needs . |
| Approach: | They propose a forward synthesis framework to generate high-quality financial dialogues . they construct a repository of 43,066 tools and synthesize over 148k dialogue instances . |
| Outcome: | Experiments show that models trained on FinToolSyn achieve a 21.06% improvement . the framework is designed to generate high-quality financial dialogues . |
Symbol tuning improves in-context learning in language models (2023.emnlp-main)
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Jerry Wei, Le Hou, Andrew Lampinen, Xiangning Chen, Da Huang, Yi Tay, Xinyun Chen, Yifeng Lu, Denny Zhou, Tengyu Ma, Quoc Le
| Challenge: | Language models are sensitive to the way that prompts are given, indicating that they are not reasoning in a robust manner. |
| Approach: | They propose to fine tune language models on in-context input-label pairs where natural language labels are replaced with arbitrary symbols. |
| Outcome: | The proposed model is much stronger at reasoning tasks and more robust to underspecified prompts than the standard model. |
EvoEdit: Evolving Null-space Alignment for Robust and Efficient Knowledge Editing (2026.findings-acl)
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| Challenge: | Existing approaches to modifying large language models require continual updates to rectify outdated or erroneous knowledge. |
| Approach: | They propose a model editing strategy that mitigates catastrophic interference through sequential null-space alignment. |
| Outcome: | EvoEdit achieves better or comparable performance than prior state-of-the-art techniques with up to 3.53 speedup. |
Learning In-context Learning for Named Entity Recognition (2023.acl-long)
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Jiawei Chen, Yaojie Lu, Hongyu Lin, Jie Lou, Wei Jia, Dai Dai, Hua Wu, Boxi Cao, Xianpei Han, Le Sun
| 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. |
Prophecy Distillation for Boosting Abstractive Summarization (2024.lrec-main)
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| Challenge: | Abstractive summarization models with maximum likelihood estimation generate unfaithful facts alongside ambiguous focus. |
| Approach: | They propose a framework which learns a regular summarization model to mimic the behavior of being guided by prophecy for boosting abstractive summaries. |
| Outcome: | The proposed model achieves new or matched state-of-the-art on four well-known datasets. |
Long-Range Modeling of Source Code Files with eWASH: Extended Window Access by Syntax Hierarchy (2021.emnlp-main)
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Colin Clement, Shuai Lu, Xiaoyu Liu, Michele Tufano, Dawn Drain, Nan Duan, Neel Sundaresan, Alexey Svyatkovskiy
| Challenge: | Statistical language modeling and translation with transformers have found many successful applications in program understanding and generation tasks. |
| Approach: | They propose an architecture-independent approach for leveraging syntactic hierarchies of source code . they use syntax trees to extract syntak hierarchical structures and integrate them into context window . |
| Outcome: | The proposed approach achieves state-of-the-art in code completion and summarization for Python in the CodeXGLUE benchmark. |
Mitigating Uncertainty in Document Classification (N19-1)
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| Challenge: | Existing models for uncertainty measurement are time-consuming and unable to handle large-scale data sets. |
| Approach: | They propose a new dropout-entropy method for uncertainty measurement and a metric learning method on feature representations to boost the performance of dropout based uncertainty methods. |
| Outcome: | The proposed method improves accuracy from 0.78 to 0.92 when 30% of the most uncertain predictions were handed over to human experts in “20NewsGroup” data. |
Towards Alleviating the Object Bias in Prompt Tuning-based Factual Knowledge Extraction (2023.findings-acl)
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| Challenge: | Existing methods to optimize prompts for factual knowledge extraction are undesirable object bias. |
| Approach: | They propose a prompt tuning method that reduces object bias and improves factual knowledge extraction. |
| Outcome: | The proposed method reduces object bias and improves accuracy of factual knowledge extraction. |
X-LXMERT: Paint, Caption and Answer Questions with Multi-Modal Transformers (2020.emnlp-main)
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| Challenge: | Recent work has adapted vision-and-language models to generative tasks like image captioning. |
| Approach: | They propose an extension to LXMERT with training refinements to generate images from text. |
| Outcome: | The proposed model can generate images from pieces of text while still being comparable to existing models. |
ChunkAttention: Efficient Self-Attention with Prefix-Aware KV Cache and Two-Phase Partition (2024.acl-long)
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| Challenge: | Experiments show that ChunkAttention can speed up the self-attention kernel by 3.2-4.8 compared to the start-of-the-art implementation. |
| Approach: | They propose a prefix-aware self-attention module that can detect matching prompt prefixes across multiple requests and share their key/value tensors in memory at runtime. |
| Outcome: | The proposed module can speed up the self-attention kernel by 3.2-4.8 compared to the start-of-the-art implementation, with the length of the system prompt ranging from 1024 to 4096. |
Consecutive Batch Model Editing with HooK Layers (2024.emnlp-main)
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| Challenge: | Existing models that retrain are time- and resource-consuming, but they lack the memory to support sequential and batch editing. |
| Approach: | They propose a model editing method that supports sequential and batch editing . they use a small amount of memory to store several hook layers that remain unchanged over time . |
| Outcome: | The proposed method is memory-friendly and can store hook layers that remain unchanged over time. |
PVTNL: Prompting Vision Transformers with Natural Language for Generalizable Person Re-identification (2025.findings-emnlp)
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| Challenge: | Domain generalization person re-identification (DG-ReID) aims to train models on source domains and generalize to unseen target domains. |
| Approach: | They propose a framework to generalize person re-identification using a vision-language model . body-part cues are used to segment images into semantically coherent regions . |
| Outcome: | The proposed framework can generalize to unseen domains and generalize semantics to people . it leverages the pre-trained vision-language model BLIP to extract aligned visual and textual embeddings. |
Distantly Supervised Course Concept Extraction in MOOCs with Academic Discipline (2023.acl-long)
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| Challenge: | Existing methods to extract knowledge concepts from MOOCs are noisy and incomplete because of the limited dictionary and diverse MOOC. |
| Approach: | They propose to automatically extract course concepts using distant supervision to eliminate the heavy work of human annotations. |
| Outcome: | The proposed framework outperforms state-of-the-art methods with 7% absolute improvement in F1 score. |
Teach Small Models to Reason by Curriculum Distillation (2025.emnlp-main)
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| 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. |
Norm-based Noisy Corpora Filtering and Refurbishing in Neural Machine Translation (2022.emnlp-main)
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| Challenge: | Existing noisy corpora filtering methods are insufficient to solve this problem, requiring multiple scorers trained on clean bitexts. |
| Approach: | They propose to use the information ratio from the source to the target side to distinguish unparallel sentence pairs by using norms of context vectors. |
| Outcome: | The proposed method performs comparably with state-of-the-art noisy corpora filtering techniques but is more efficient and easier to operate. |
Scalable Fine-tuning from Multiple Data Sources: A First-Order Approximation Approach (2024.findings-emnlp)
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| Challenge: | a new algorithm to estimate fine-tuning performance for a target task is proposed . conventional subset selection methods require repeated training on subsets of auxiliary tasks . |
| Approach: | They propose an algorithm to fine-tune a language model for a target task by optimally using auxiliary tasks' information. |
| Outcome: | The proposed method can estimate fine-tuning performance on CPUs in seconds. |
CARO: Chain-of-Analogy Reasoning Optimization for Robust Content Moderation (2026.findings-acl)
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| Challenge: | Current large language models struggle with ambiguous content moderation cases due to misleading "decision shortcuts" . authors propose a two-stage training framework to induce robust analogical reasoning in LLMs . |
| Approach: | They propose a two-stage training framework to induce robust analogical reasoning in LLMs . they bootstrap analogy reasoning chains via retrieval-augmented generation and SFT . |
| Outcome: | The proposed framework outperforms state-of-the-art reasoning models and specialized moderation models on ambiguous moderation benchmarks. |
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. |
EVA: Evolving Semantic Adversaries for Red-Teaming GUI Agents Against Environmental Injection Attacks (2026.findings-acl)
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Yijie Lu, Manman Zhao, Tianjie Ju, Zihe Yan, Xinbei Ma, Yuan Guo, Daizong Ding, Gongshen Liu, Zhuosheng Zhang
| Challenge: | Existing methods for red-teaming face a trade-off between requiring target-specific knowledge and incurring prohibitive computational costs. |
| Approach: | They propose a framework that evolves payloads exclusively on the semantic dimension via a discovery-deployment pipeline. |
| Outcome: | Experiments show that EVA outperforms baselines in terms of attack success rate while evolving benign seeds into successful attacks within 1.18 to 1.71 iterations. |
Hit the Nail on the Head: Parameter-Efficient Multi-task Tuning via Human Language Intervention (2024.findings-emnlp)
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| Challenge: | Recent studies show that PEFT on small pre-trained language models improves multitasking capabilities. |
| Approach: | They propose a multi-task learning framework that enables transfer of prior knowledge across tasks . they attach task descriptions to input samples and map them to task embeddings . |
| Outcome: | The proposed method improves performance on a T5 model and in decoder-only models . |
Enhancing Language Model Factuality via Activation-Based Confidence Calibration and Guided Decoding (2024.emnlp-main)
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| Challenge: | Existing methods to calibrate language models are limited in inference-time efficiency or fail to provide informative signals. |
| Approach: | They propose an activation-based calibration method, ActCab, which trains a linear layer on top of the LM’s last-layer activations. |
| Outcome: | The proposed method improves on five popular QA benchmarks and reduces the average expected calibration error (ECE) score by up to 39%. |
PrivacyRestore: Privacy-Preserving Inference in Large Language Models via Privacy Removal and Restoration (2025.acl-long)
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| Challenge: | Existing privacy protection methods for large language models suffer from performance degradation or large inference time overhead. |
| Approach: | They propose a plug-and-play method to protect the privacy of user inputs during LLM inference . they use offline restoration vectors to train restoration vector for each privacy span type . |
| Outcome: | The proposed method can prevent the linear growth of the privacy budget. |
Entropy Scheduling in Reinforcement Learning for Large Language Models (2026.findings-acl)
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| Challenge: | entropy in reinforcement learning functions analogously to the learning rate in LLMs. |
| Approach: | They propose an entropy scheduling system that optimizes different pre-set goals by controlling and scheduling entropicy at each step of the RL process. |
| Outcome: | The proposed method improves AIME2024 from 50.9 to 54.9 within 40 training steps. |
Maximizing the Effectiveness of Larger BERT Models for Compression (2025.acl-long)
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| Challenge: | Existing methods for capturing large BERT models as teachers do not fully exploit the potential advantages of larger teachers. |
| Approach: | They propose a method that leverages a pretrained teacher model to guide the training of a lightweight student model to enhance knowledge transfer. |
| Outcome: | The proposed method enhances knowledge transfer by leveraging a pretrained teacher model to guide the training of a lightweight student model. |
GRACE: Discriminator-Guided Chain-of-Thought Reasoning (2023.findings-emnlp)
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| Challenge: | Existing language models (LMs) can assign a high likelihood to incorrect steps . Existing models (LLMs), however, struggle with complex multi-step reasoning. |
| Approach: | They propose a stepwise decoding approach that steers the decoding process towards producing correct reasoning steps. |
| Outcome: | The proposed approach outperforms existing methods on math and symbolic reasoning tasks. |
LANID: LLM-assisted New Intent Discovery (2024.lrec-main)
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| Challenge: | Data annotation is expensive in Task-Oriented Dialogue systems. |
| Approach: | They propose a framework that leverages Large Language Models' zero-shot capability to enhance the performance of a smaller text encoder on the NID task. |
| Outcome: | The proposed framework surpasses all strong baselines in both unsupervised and semi-supervised settings. |
TrInk: Ink Generation with Transformer Network (2025.emnlp-main)
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Zezhong Jin, Shubhang Desai, Xu Chen, Biyi Fang, Zhuoyi Huang, Zhe Li, Chong-Xin Gan, Xiao Tu, Man-Wai Mak, Yan Lu, Shujie Liu
| Challenge: | Existing methods for handwriting generation capture global dependencies and can generate high-quality handwritten samples. |
| Approach: | They propose a Transformer-based model for ink generation, TrInk, which captures global dependencies. |
| Outcome: | The proposed model reduces character error rate and word error rate by 35.56% on the IAM-OnDB dataset compared to previous models. |
CoE-SQL: In-Context Learning for Multi-Turn Text-to-SQL with Chain-of-Editions (2024.naacl-long)
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| Challenge: | Recent studies have demonstrated that Large Language Models (LLMs) have impressive capabilities in a variety of domains and tasks. |
| Approach: | They propose a method which prompts LLMs to generate SQL queries based on the previously generated SQL query with an edition chain. |
| Outcome: | The proposed method outperforms different in-context learning baselines and achieves state-of-the-art performance on two benchmarks SParC and CoSQL using LLMs. |
GCML: Gradient Coherence Guided Meta-Learning for Cross-Domain Emerging Topic Rumor Detection (2025.emnlp-main)
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| Challenge: | Existing domain adaptation rumor detection methods ignore the data generalization differences and rely on a large amount of unlabeled target domain samples to achieve domain adaptation. |
| Approach: | They propose a Gradient Coherence guided Meta-Learning approach for emerging topics rumor detection that selectively learns more "generalizable" tasks that are more beneficial in adapting to the target domain. |
| Outcome: | The proposed method outperforms baselines on real-world datasets and significantly outperformed traditional methods on the in-domain condition. |
Twist Decoding: Diverse Generators Guide Each Other (2022.emnlp-main)
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Jungo Kasai, Keisuke Sakaguchi, Ronan Le Bras, Hao Peng, Ximing Lu, Dragomir Radev, Yejin Choi, Noah A. Smith
| Challenge: | Using a variety of language generation models, ensembling models is challenging during inference. |
| Approach: | They propose a method that decodes text models that do not assume a shared vocabulary, tokenization or generation order. |
| Outcome: | The proposed method outperforms models decoded in isolation over various scenarios. |
HalluAudio: A Comprehensive Benchmark for Hallucination Detection in Large Audio-Language Models (2026.acl-long)
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| Challenge: | Existing studies on hallucination focus on text or vision, while few audio-oriented studies are limited in scale, modality coverage, and diagnostic depth. |
| Approach: | They propose a large-scale benchmark for evaluating hallucinations across speech, sound, and music. |
| Outcome: | The proposed model improves hallucination rate, yes/no bias, error-type analysis, and refusal rate. |
AgentGym2: Benchmarking Large Language Model Agents in De-Idealized Real-World Environments (2026.acl-long)
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Zhiheng Xi, Dingwen Yang, Jiaqi Liu, Jixuan Huang, Honglin Guo, Baodai Huang, Tinggang Chen, Qi Zhang, Zhonghang Lu, Chenyu Liu, Jiajun Sun, Jiazheng Zhang, Dingwei Zhu, Xin Guo, Junzhe Wang, Zhihao Zhang, Yuming Yang, Junjie Ye, Minghe Gao, Dongrui Liu, Jiaming Ji, Guohao Li, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Existing benchmarks evaluate agents in simplified, idealized settings, relying on pre-packaged tool interfaces, overlooking critical steps, and assume inputs are clean and fully specified. |
| Approach: | They propose a framework that evaluates language agents in simplified, idealized settings . they show that even SOTA systems like Gemini and GPT-5 struggle on AgentGym2 . |
| Outcome: | Experiments on 15 proprietary and open-source models show that even SOTA systems like Gemini and GPT-5 struggle on AgentGym2 . |
Distilling Discrimination and Generalization Knowledge for Event Detection via Delta-Representation Learning (P19-1)
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| 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. |
Aegis:An Advanced LLM-Based Multi-Agent for Intelligent Functional Safety Engineering (2024.emnlp-industry)
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| 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. |
Interpretable Safety Alignment via SAE-Constructed Low-Rank Subspace Adaptation (2026.acl-long)
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| Challenge: | Prior work has shown that safety behaviors are governed by low-rank structures . Low-Rank Adaptation (LoRA) consistently underperforms full fine-tuning and reinforcement learning on safety benchmarks . |
| Approach: | They propose a safety alignment system that disentangles safety-relevant directions into monosemantic features and constructs an interpretable safety subspace from SAE directions. |
| Outcome: | Empirically, the proposed model achieves 99.6% safety rates across multiple model families and scales . low-rank Adaptation consistently underperforms full fine-tuning and reinforcement learning on safety benchmarks compared with previous methods . |
AHP-Powered LLM Reasoning for Multi-Criteria Evaluation of Open-Ended Responses (2024.findings-emnlp)
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| Challenge: | Question answering (QA) tasks have been extensively studied in the field of natural language processing. |
| Approach: | They propose a method that leverages large language models and the analytic hierarchy process to assess open-ended questions. |
| Outcome: | The proposed method more closely aligns with human judgment compared to baselines on four datasets. |
Experience-driven Multi-turn Reinforcement Learning for GUI Agents (2026.acl-long)
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Zhengxi Lu, Jiabo Ye, Fei Tang, Yongliang Shen, Haiyang Xu, Ziwei Zheng, Weiming Lu, Ming Yan, Fei Huang, Jun Xiao, Yueting Zhuang
| Challenge: | GUI agents have demonstrated remarkable progress in automating complex user interface interactions . training such agents for long-horizon tasks remains challenging due to limited rewards and prohibitive costs. |
| Approach: | They propose a method that leverages expert trajectories as environment experiences for on-policy multi-turn training. |
| Outcome: | The proposed method achieves significant gains over the base model with 1K public trajectories as RL experiences . it achieves competitive performance against strong baselines such as UI-TARS-7B and GPT-4o . |
ChatMap: Mining Human Thought Processes for Customer Service Chatbots via Multi-Agent Collaboration (2025.findings-acl)
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Xinyi Jiang, Tianyi Hu, Yuheng Qin, Guoming Wang, Zhou Huan, Kehan Chen, Gang Huang, Rongxing Lu, Siliang Tang
| Challenge: | Existing methods for enhancing dialogue performance rely on summarizing behavior . e-commerce chatbots need to align their dialogue strategies with human behavior to achieve coherent, human-like conversations with customers. |
| Approach: | They propose a method to extract core patterns from dialogue data and integrate them into models by mining service thought processes using a multi-agent aPproach. |
| Outcome: | The proposed method outperforms manual methods and outperfies baselines on Taobao in China. |
From Off-Policy to On-Policy: Enhancing GUI Agents via Bi-level Expert-to-Policy Assimilation (2026.acl-long)
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| Challenge: | Vision-language models are increasingly deployed as computer-use agents that operate desktops and browsers. |
| Approach: | They propose a method that turns static expert traces into policy-aligned guidance . they propose RLVR with a per-task, dynamically updated cache to decompose planning and execution . |
| Outcome: | The proposed model improves UITARS1.5-7B success from 22.87% to 32.13% on OSWorld-Verified and raises a held-out split from 5.74% to 10.30% on MMBench-GUI and Online-Mind2Web. |
Adversarial Learning with Contextual Embeddings for Zero-resource Cross-lingual Classification and NER (D19-1)
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| Challenge: | Contextual word embeddings have demonstrated state-of-the-art performance on various NLP tasks. |
| Approach: | They propose to use adversarial learning to improve upon multilingual BERT's zero-resource cross-lingual performance by aligning embeddings of English documents and their translations. |
| Outcome: | The multilingual version of BERT performs surprisingly well in cross-lingual settings, even when only labeled English data is used to finetune the model. |
Reflection on Knowledge Graph for Large Language Models Reasoning (2025.findings-acl)
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Yigeng Zhou, Wu Li, Yifan Lu, Jing Li, Fangming Liu, Meishan Zhang, Yequan Wang, Daojing He, Honghai Liu, Min Zhang
| Challenge: | Existing methods for supplementing Large Language Models (LLMs) with knowledge graphs often introduce noise in the retrieval and reasoning pipeline, hindering their ability to integrate external knowledge for complex multi-hop question answering. |
| Approach: | They propose a framework to enhance LLMs' reasoning capabilities through reflective engagement with knowledge graphs by Query Decoupling, LLM-Driven Knowledge Graph Exploration, and Inference with Knowledge Reconstruction. |
| Outcome: | The proposed framework integrates external knowledge into LLMs and trains them to leverage this knowledge for answering questions. |
Benchmarking Retrieval-Augmented Generation for Medicine (2024.findings-acl)
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| Challenge: | Large language models (LLMs) have state-of-the-art performance on a wide range of medical question answering tasks, but they still face challenges with hallucinations and outdated knowledge. |
| Approach: | They propose a benchmark to evaluate medical RAG systems using large-scale experiments with over 1.8 trillion prompt tokens. |
| Outcome: | The proposed benchmark improves accuracy of six different LLMs by up to 18% over chain-of-thought prompting. |
AdDriftBench: A Benchmark for Detecting Data Drift and Label Drift in Short Video Advertising (2025.findings-emnlp)
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Yinghao Song, Xiangji Zeng, Shuai Cui, Lu Sun, Zhaowei Liu, Yuan Yuan, Yulu Wang, Hai Zhou, Zhaohan Gong
| Challenge: | Short video advertising scenarios present unique challenges due to data drift (DD) and label drift (LD). |
| Approach: | They propose to use data drift and label drift to evaluate models under rapidly shifting content distributions and labeling scenarios to assess their generalization capabilities. |
| Outcome: | The proposed model performs moderately in short video advertising contexts, particularly in handling fine-grained semantics and adapting to shifting instructions. |
Unified Contextual Query Rewriting (2023.acl-industry)
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Yingxue Zhou, Jie Hao, Mukund Rungta, Yang Liu, Eunah Cho, Xing Fan, Yanbin Lu, Vishal Vasudevan, Kellen Gillespie, Zeynab Raeesy
| Challenge: | Large-scale conversational AI agents such as Alexa, Siri, and Google Assistant are becoming increasingly popular in real-world applications to assist users in daily life. |
| Approach: | They propose a unified contextual query rewriting model that unifies QR for friction reduction and contextual carryover . they leverage the text-to-text unified framework which uses independent tasks with weighted loss to account for task importance . |
| Outcome: | The proposed model reduces friction and contextual carryover by using multiple auxiliary tasks. |
SF-QA: Simple and Fair Evaluation Library for Open-domain Question Answering (2021.eacl-demos)
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| Challenge: | Open-domain question answering (QA) requires large amounts of resources and is difficult to reproduce results due to complex configurations. |
| Approach: | They propose a simple and fair evaluation framework for open-domain question answering (QA) it modularizes the pipeline open- domain QA system, making it easily accessible . |
| Outcome: | The proposed evaluation framework is publicly available and anyone can contribute to the code and evaluations. |
REInstruct: Building Instruction Data from Unlabeled Corpus (2024.findings-acl)
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| 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. |
Exploiting Node Content for Multiview Graph Convolutional Network and Adversarial Regularization (2020.coling-main)
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Qiuhao Lu, Nisansa de Silva, Dejing Dou, Thien Huu Nguyen, Prithviraj Sen, Berthold Reinwald, Yunyao Li
| Challenge: | Existing graph autoencoders and its variants have been used for node embedding . a new method is proposed to model consistency across different views of networks . |
| Approach: | They propose a network embedding method which enforces latent representations to be consistent across different views of networks by incorporating a multiview adversarial regularization module. |
| Outcome: | The proposed method compares favorably with the state-of-the-art methods on benchmark datasets and on a real-world application. |
Temporal Token Matters: Investigating and Interpreting the Consistency of Temporal Ordering in Large Language Models (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) exhibit notable deficiencies in temporal reasoning . phrasing changes can lead LLMs to produce inconsistent outputs . |
| Approach: | They investigate the mechanistic interpretability of temporal ordering within event temporal reasoning . they identify a sparse subset of attention heads that are causally responsible for reasoning outcomes . |
| Outcome: | The proposed model outperforms other models in a variety of tasks and is validated by intervention-based experiments. |
TextMixer: Mixing Multiple Inputs for Privacy-Preserving Inference (2023.findings-emnlp)
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| Challenge: | Pre-trained language models (PLMs) are often deployed as cloud services, enabling users to upload textual data and perform inference remotely. |
| Approach: | They propose a privacy-preserving inference framework called MixPi which aims to obfuscate a user's private input by mixing it with multiple other inputs. |
| Outcome: | The proposed framework surpasses existing privacy-preserving methods on token and sentence classification tasks. |
KARPA: A Training-free Method of Adapting Knowledge Graph as References for Large Language Model’s Reasoning Path Aggregation (2025.findings-acl)
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| Challenge: | Existing methods for large language models (LLMs) are limited by step-by-step decision-making on KGs, or require fine-tuning or pre-training on specific KG. |
| Approach: | They propose a framework that harnesses the global planning abilities of large language models (LLMs) for efficient and accurate KG reasoning. |
| Outcome: | Extensive experiments show that the proposed framework achieves state-of-the-art performance in KGQA tasks, delivering both high efficiency and accuracy. |
MemSearcher: Iterative Memory Integration for Search Agent via End-to-End Reinforcement Learning (2026.findings-acl)
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Qianhao Yuan, Jie Lou, Zichao Li, Jiawei Chen, Yaojie Lu, Hongyu Lin, Le Sun, Debing Zhang, Xianpei Han
| 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. |
Unsupervised Non-transferable Text Classification (2022.emnlp-main)
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| Challenge: | Existing methods to train a good deep learning model require labeled data for the target domain which can be difficult to obtain. |
| Approach: | They propose an unsupervised non-transferable learning method that does not require annotated target domain data and introduce a secret key component for recovering the model’s access to the target domain. |
| Outcome: | The proposed method reduces model generalization ability in specific target domains while still recovering access to the target domain. |
ACBQ: Adaptive Cross-Block Quantization of Large Language Models (2026.acl-long)
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| Challenge: | Existing methods for post-training quantization struggle to support weight–activation joint quantization and extreme low-bit weight quantization. |
| Approach: | They propose a framework that addresses weight–activation joint quantization and extreme weight quantization. |
| Outcome: | The proposed framework achieves superior performance under both W4A4 and highly aggressive W2 settings while incurring negligible additional computational overhead. |
Inference-Time Policy Adapters (IPA): Tailoring Extreme-Scale LMs without Fine-tuning (2023.emnlp-main)
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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
| 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. |
Immediate Inference: The Missing Foundation in Large Language Model Logical Reasoning (2026.acl-long)
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| Challenge: | Recent work on LLMs has focused on fine-grained skill decomposition and consistency probing at the propositional level. |
| Approach: | They propose a benchmark evaluating immediate inference that evaluates elemental operations over categorical propositions and proposes a model that uses immediate inferential reasoning. |
| Outcome: | The proposed benchmark demonstrates that models lack robust operator grounding, oscillating between structural reasoning and surface pattern matching, inconsistent handling of quantifiers and negation. |
Lightweight, Dynamic Graph Convolutional Networks for AMR-to-Text Generation (2020.emnlp-main)
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| Challenge: | AMR-to-text generation is used to transduce Abstract Meaning Representation structures (AMRs) Graph Convolution Networks (GCNs) are not able to capture non-local information and follow a local (first-order) information aggregation scheme. |
| Approach: | They propose a dynamic fusion mechanism that captures richer non-local interactions . they propose weight tied convolutions and group graph convolution to reduce memory usage . |
| Outcome: | The proposed model outperforms state-of-the-art models on two benchmark datasets with significantly fewer parameters while maintaining the model capacity. |
Knowledge Graph-Augmented Abstractive Summarization with Semantic-Driven Cloze Reward (2020.acl-main)
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| Challenge: | Abstractive summarization models for document encoders suffer from fabricated content and are often near-extractive. |
| Approach: | They propose a framework for abstractive summarization with Graph-Augmentation and semantic-driven RewarD that uses a sequential document encoder and a graph-structured encoder to maintain the global context and local characteristics of entities. |
| Outcome: | The proposed framework produces higher ROUGE scores than a variant without knowledge graph on New York Times and CNN/Daily Mail datasets. |
GMSA: Enhancing Context Compression via Group Merging and Layer Semantic Alignment (2026.acl-long)
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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
| 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. |
The ApposCorpus: a new multilingual, multi-domain dataset for factual appositive generation (2020.coling-main)
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| Challenge: | appositives are phrases that appear next to a noun phrase and serve an explicative function. |
| Approach: | They propose a more realistic end-to-end definition of appositive generation with a dataset that spans four languages and two entity types. |
| Outcome: | The proposed model is non-trivial and leaves plenty of room for improvement. |
SocAoG: Incremental Graph Parsing for Social Relation Inference in Dialogues (2021.acl-long)
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| Challenge: | Existing studies focus on identifying entities' relations from the semantics of dialogues-they utilize either the attention mechanism or a refined token graph to locate informative words. |
| Approach: | They propose a sequential structure prediction task to incrementally parse SocAoG for dynamic inference upon any incoming utterance. |
| Outcome: | Empirical results show that the proposed model infers social relations more accurately than the state-of-the-art methods. |
Towards Knowledge Checking in Retrieval-augmented Generation: A Representation Perspective (2025.naacl-long)
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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
| 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. |
Learning Cross-lingual Distributed Logical Representations for Semantic Parsing (P18-2)
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| Challenge: | Recent research efforts have looked into the problem of learning semantic parsers in a multilingual setup, but how to improve the performance of a monolingual semantic parsed system remains a research question that is under-explored. |
| Approach: | They propose to use data annotated in different languages to learn distributed representations of logical forms for improving a monolingual semantic parser. |
| Outcome: | The proposed method improves on the standard multilingual GeoQuery dataset. |
CREAD: Combined Resolution of Ellipses and Anaphora in Dialogues (2021.naacl-main)
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Bo-Hsiang Tseng, Shruti Bhargava, Jiarui Lu, Joel Ruben Antony Moniz, Dhivya Piraviperumal, Lin Li, Hong Yu
| 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. |
Curriculum-RLAIF: Curriculum Alignment with Reinforcement Learning from AI Feedback (2026.findings-acl)
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| 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. |
mCLIP: Multilingual CLIP via Cross-lingual Transfer (2023.acl-long)
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Guanhua Chen, Lu Hou, Yun Chen, Wenliang Dai, Lifeng Shang, Xin Jiang, Qun Liu, Jia Pan, Wenping Wang
| Challenge: | Existing multilingual vision-language pretrained models are biased towards English due to the lack of sufficient non-English image-text pairs. |
| Approach: | They propose to train a retrieval-efficient dual-stream multilingual VLP model by aligning CLIP model and a multilingual text encoder through a novel Triangle Cross-modal Knowledge Distillation method. |
| Outcome: | Empirical results show that mCLIP achieves new state-of-the-art performance for both zero-shot and finetuned multilingual image-text retrieval tasks. |
An Evaluation Mechanism of LLM-based Agents on Manipulating APIs (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have remarkable capabilities across a variety of tasks, such as language, mathematics, coding, and etc. |
| Approach: | They propose to decompose tool use capability into seven aspects and form a thorough evaluation schema for generic agents. |
| Outcome: | The proposed agent acts like a super-APP and can manipulate API-based tools. |
MergeME: Model Merging Techniques for Homogeneous and Heterogeneous MoEs (2025.naacl-long)
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Yuhang Zhou, Giannis Karamanolakis, Victor Soto, Anna Rumshisky, Mayank Kulkarni, Furong Huang, Wei Ai, Jianhua Lu
| Challenge: | State-of-the-art methods for merging expert models with different architectures do not address parameter interference and require extensive fine-tuning to restore performance. |
| Approach: | They propose a method for merging experts with different architectures into a unified Mixture-of-Experts model with a goal of enhancing performance in each domain while retaining effectiveness on general tasks. |
| Outcome: | Experiments across multiple domains show that the proposed methods reduce fine-tuning costs and improve performance over state-of-the-art methods. |
Unsupervised Dual Paraphrasing for Two-stage Semantic Parsing (2020.acl-main)
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| Challenge: | Existing semantic parsing frameworks rely on nontrivial human labor to generate canonical utterances. |
| Approach: | They propose a framework that uses an unsupervised paraphrase model to parse canonical utterances. |
| Outcome: | The proposed framework is effective and compatible with supervised training. |
Mining the Past with Dual Criteria: Integrating Three types of Historical Information for Context-aware Event Forecasting (2025.emnlp-main)
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Rong Ma, Lei Wang, Yating Yang, Bo Ma, Rui Dong, Fengyi Yang, Ahtamjan Ahmat, Kaiwen Lu, Xinyue Wang
| Challenge: | Existing methods focus on entities and structural dependencies but overlook implicitly relevant information. |
| Approach: | They propose a method that leverages event semantics for relevance modeling and incorporates a self-supervised semantic filter based on factual event associations to capture implicitly relevant historical information. |
| Outcome: | The proposed method outperforms existing methods on three public benchmark datasets and is highly effective on two structured temporal knowledge graph forecasting datasets. |
HYRR: Hybrid Infused Reranking for Passage Retrieval (2024.lrec-main)
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| Challenge: | Existing passage retrieval systems typically adopt a two-stage retrieve-then-rerank pipeline. |
| Approach: | They propose a framework for training robust reranking models using hybrid retrievers . they propose HYRR framework that allows users to select training data using hybrids . |
| Outcome: | The proposed framework is robust to different first-stage retrieval settings. |
NeuroLogic Decoding: (Un)supervised Neural Text Generation with Predicate Logic Constraints (2021.naacl-main)
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| Challenge: | Conditional text generation often requires lexical constraints, i.e., which words should or shouldn't be included in the output text. |
| Approach: | They propose an algorithm that enables neural language models to generate fluent text while satisfying complex lexical constraints. |
| Outcome: | The proposed algorithm outperforms existing methods on four benchmarks and shows that it handles any set of lexical constraints expressible under predicate logic while its asymptotic runtime is equivalent to conventional beam search. |
Improved Neural Protoform Reconstruction via Reflex Prediction (2024.lrec-main)
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| Challenge: | comparative method allows linguists to infer protoforms from their reflexes based on sound change . authors argue that this approach ignores one of the most important aspects of the comparative approach . |
| Approach: | They propose a comparative method that allows linguists to infer protoforms from their reflexes . they propose to use a system where candidate protoform from a reconstruction model are reranked by a reflex prediction model. |
| Outcome: | The comparative method surpasses state-of-the-art methods on Chinese and Romance datasets. |
CrossGuard: Safeguarding MLLMs against Joint-Modal Implicit Malicious Attacks (2026.acl-long)
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| Challenge: | Existing methods for MLLMs are weak on explicit attacks, but weak on implicit ones. |
| Approach: | They propose an automated red-teaming pipeline that leverages reinforcement learning with tailored reward modules to generate diverse implicit samples across 14 domains. |
| Outcome: | The proposed method outperforms existing methods in implicit and explicit attacks while maintaining high utility. |
Perception Compressor: A Training-Free Prompt Compression Framework in Long Context Scenarios (2025.findings-naacl)
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| Challenge: | Long prompts contain redundant information and are sensitive to the position of key information in long context scenarios. |
| Approach: | They propose a training-free prompt compression framework that retains key information at token level while removing distracting tokens. |
| Outcome: | The proposed framework outperforms existing methods on long context benchmarks. |
In Plain Sight: Media Bias Through the Lens of Factual Reporting (D19-1)
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| Challenge: | lexical bias stems from content realization, or how things are said, but other forms of bias stem from content selection and organization. |
| Approach: | They use a dataset to analyze news articles annotated with 1,727 bias spans to investigate informational bias. |
| Outcome: | The proposed model shows that informational bias appears more frequently than lexical bias. |
Synergizing Stylometrics with Semantics: Dual-Path Framework for LLM Detection and Attribution (2026.findings-acl)
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| Challenge: | Existing methods for identifying MGTs rely on statistical likelihood or deep embeddings. |
| Approach: | They propose a framework that extracts model-specific stylistic fingerprints across lexical, syntactic, and structural dimensions. |
| Outcome: | The proposed framework achieves a Macro-F1 score of 95.6% on the Wikipedia dataset. |
Few-Shot Multimodal Named Entity Recognition Based on Mutlimodal Causal Intervention Graph (2024.lrec-main)
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| Challenge: | Existing methods for multimodal named entity recognition are limited due to limited resources. |
| Approach: | They propose a Few-shot Multimodal Named Entity Recognition task to address these relation types by constructing a multimodal graph and a new multimodal causal intervention strategy. |
| Outcome: | The proposed model improves on two multimodal named entity recognition datasets. |
Supervised Seeded Iterated Learning for Interactive Language Learning (2020.emnlp-main)
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| Challenge: | Recent work has focused on word-based conversational agents that tend to invent their language rather than leveraging natural language. |
| Approach: | They propose two methods to counter language drift by combining S2P and Seeded Iterated Learning to minimize their weaknesses. |
| Outcome: | The proposed methods reduce late-stage training collapses and higher negative likelihood when evaluated on human corpus. |
Fact-Checking Complex Claims with Program-Guided Reasoning (2023.acl-long)
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| Challenge: | Fact-checking real-world claims often requires collecting multiple pieces of evidence and complex multi-step reasoning. |
| Approach: | They propose a novel fact-checking model that decomposes complex claims into simpler sub-tasks that can be solved using a shared library of specialized functions. |
| Outcome: | The proposed model outperforms seven baselines on two fact-checking datasets and has explicit output programs that benefit human debugging. |
DynaMaR: Dynamic Prompt with Mask Token Representation (2022.emnlp-industry)
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Xiaodi Sun, Sunny Rajagopalan, Priyanka Nigam, Weiyi Lu, Yi Xu, Iman Keivanloo, Belinda Zeng, Trishul Chilimbi
| Challenge: | Recent research shows that large language models pretrained using unsupervised approaches can achieve significant performance improvement on many downstream tasks. |
| Approach: | They propose an unsupervised approach to fine-tuning large language models using unsupervised approaches to many downstream tasks. |
| Outcome: | The proposed approach improves on four e-commerce applications and can achieve an average improvement of 10% in few-shot settings and 3.7% in data-rich settings over the standard approach. |
RepoAgent: An LLM-Powered Open-Source Framework for Repository-level Code Documentation Generation (2024.emnlp-demo)
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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
| 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. |
Investigating the Multilingual Calibration Effects of Language Model Instruction Tuning (2026.eacl-short)
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Jerry Huang, Peng Lu, Qiuhao Zeng, Yusuke Iwasawa, Yutaka Matsuo, Sarath Chandar, Edison Marrese-Taylor, Irene Li
| Challenge: | despite advances in foundation model research, the relationship between large language models and their calibration remains an open area of research. |
| Approach: | They examine a gap in the calibration of large language models within multilingual settings to better understand how data scarcity can potentially lead to different calibration effects. |
| Outcome: | The proposed calibration gap is found in two multilingual benchmarks over 29 and 42 languages. |
Discourse as a Function of Event: Profiling Discourse Structure in News Articles around the Main Event (2020.acl-main)
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| Challenge: | a recent study shows that news articles report context-informing content that is not necessarily relevant to main events. |
| Approach: | They propose to use a functional discourse structure for news articles to model news content structures . they propose to integrate system predicted news structures into the annotations . |
| Outcome: | The proposed model outperforms existing models in event coreference resolution. |
SDiaReward: Modeling and Benchmarking Spoken Dialogue Rewards with Modality and Colloquialness (2026.acl-long)
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Jingyu Lu, Yuhan Wang, Fan Zhuo, Xize Cheng, Changhao Pan, Xueyi Pu, Yifu Chen, Chenyuhao Wen, Tianle Liang, Zhou Zhao
| Challenge: | SDiaReward is an end-to-end spoken dialogue system that integrates paralinguistic nuances and spontaneous nature of human conversation. |
| Approach: | They propose an end-to-end multi-turn reward model trained on SDiaReward-Dataset . it is a collection of episode-level preference pairs targeting modality and colloquiality gaps . |
| Outcome: | The proposed model outperforms general-purpose audio LLMs in episode-level evaluation. |
MalURLBench: A Benchmark Evaluating Agents’ Vulnerabilities When Processing Web URLs (2026.findings-acl)
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Dezhang Kong, Zhuxi Wu, Shiqi Liu, ZhiCheng Tan, Kuichen Lu, Minghao Li, Qichen Liu, Shengyu Chu, Zhenhua Xu, Xuan Liu, Meng Han
| Challenge: | Existing models struggle to detect elaborately disguised malicious URLs, despite their ability to process malicious URL's. |
| Approach: | They propose a benchmark to evaluate LLMs’ vulnerabilities to malicious URLs and a lightweight defense module to mitigate the vulnerability. |
| Outcome: | The proposed framework analyzes 61,845 attack instances spanning 10 real-world scenarios and 7 categories of real malicious websites. |
NapSS: Paragraph-level Medical Text Simplification via Narrative Prompting and Sentence-matching Summarization (2023.findings-eacl)
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| Challenge: | a recent study shows that accessing medical literature is difficult for laypeople because it is written for specialists and contains medical jargon. |
| Approach: | They propose a two-stage strategy to identify relevant content to be simplified . they first generate reference summaries via sentence matching between the original and simplified abstracts . |
| Outcome: | The proposed approach improves on a seq2seq-based test set on an English medical corpus . it also improves the SARI score by 1.1% . |
Mixture of Soft Prompts for Controllable Data Generation (2023.findings-emnlp)
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| Challenge: | Large language models (LLMs) generate fluent text when the target output follows natural language patterns. |
| Approach: | They propose a method that uses large language models to generate fluent text from a limited ontology rather than direct prediction by using soft prompts. |
| Outcome: | The proposed method produces diverse and natural text while preserving label semantics. |
Hierarchical Topic Modeling via Contrastive Learning and Hyperbolic Embedding (2024.lrec-main)
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| 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. |
Not All Errors are Equal: Learning Text Generation Metrics using Stratified Error Synthesis (2022.findings-emnlp)
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| Challenge: | Existing learning metrics are limited to tasks where large human ratings are available. |
| Approach: | They propose a model-based natural language generation (NLG) evaluation metric that is highly correlated with human judgements without requiring human annotation. |
| Outcome: | The proposed metric outperforms all prior unsupervised metrics on multiple NLG tasks including translation, image captioning, and WebNLG text generation. |
SingaKids: A Multilingual Multimodal Dialogic Tutor for Language Learning (2025.acl-industry)
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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
| 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. |
A Chain-of-Task Framework for Instruction Tuning of LLMs Based on Chinese Grammatical Error Correction (2025.coling-main)
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| Challenge: | Existing approaches to address Grammatical Error Correction (GEC) tasks are based on large scale labeled data, which leads to extremely high data annotation costs. |
| Approach: | They propose a Chain-of-Task framework to reduce over-correction in large language models . they propose supervised fine-tuning strategy and an algorithm for automatic dataset annotation . |
| Outcome: | The proposed framework achieves state-of-the-art on both FCGEC (in-domain) and NaCGEC (out-of domain) test sets. |
ULN: Towards Underspecified Vision-and-Language Navigation (2022.emnlp-main)
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| Challenge: | Existing vision-and-language navigation models are brittle to multi-level language underspecification. |
| Approach: | They propose to use multi-level underspecified instructions to guide agents . they propose to learn GSS for navigation agent to ground multi- level instructions . experimental results show existing VLN models are still brittle to multi-language underspecification . |
| Outcome: | Experimental results show that the proposed framework outperforms baselines on ULN by 10% relative success rate across all levels. |
DeFine: Decision-Making with Analogical Reasoning over Factor Profiles (2025.findings-acl)
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| Challenge: | Large language models are ideal for decision-making, but they can be difficult to process when they are verbose and include repetition, hedging, and vagueness. |
| Approach: | They propose a framework that constructs probabilistic factor profiles from complex scenarios and integrates them with analogical reasoning to guide LLMs in making decisions in new situations. |
| Outcome: | The proposed framework separates the tasks of quantifying uncertainty and incorporating it into LLM decision-making. |
Multilingual Brain Surgeon: Large Language Models Can Be Compressed Leaving No Language behind (2024.lrec-main)
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| Challenge: | Existing methods for MC focus on quantization and network pruning. |
| Approach: | They propose a calibration method that samples calibration data from various languages proportionally to the language distribution of the model training datasets. |
| Outcome: | The proposed method improves the performance of existing English-centric compression methods on the BLOOM multilingual LLM. |
Impossible Distillation for Paraphrasing and Summarization: How to Make High-quality Lemonade out of Small, Low-quality Model (2024.naacl-long)
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Jaehun Jung, Peter West, Liwei Jiang, Faeze Brahman, Ximing Lu, Jillian Fisher, Taylor Sorensen, Yejin Choi
| Challenge: | Impossible Distillation is a framework for paraphrasing and sentence summarization that can be trained from a low-quality teacher model. |
| Approach: | They propose a framework that distills a high-quality dataset from a low-quality teacher . they hypothesize and verify the paraphrastic proximity intrinsic to pre-trained LMs . |
| Outcome: | The proposed framework outperforms baseline models on unconstrained paraphrase generation and sentence summarization benchmarks. |
VeriFact: Enhancing Long-Form Factuality Evaluation with Refined Fact Extraction and Reference Facts (2025.emnlp-main)
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| Challenge: | Prior work focuses on accuracy and precision, but factuality evaluation is difficult due to inter-sentence dependencies. |
| Approach: | They introduce a factuality evaluation framework to enhance fact extraction . they also introduce 'factRBench' that evaluates both precision and recall . |
| Outcome: | The proposed framework enhances fact extraction by identifying incomplete and missing facts . it also evaluates precision and recall in long-form models, whereas prior work focuses on precision. |
Platforms for Non-speakers Annotating Names in Any Language (P18-4)
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| 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
| 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. |
CrowdAgent: Multi-Agent Managed Multi-Source Annotation System (2025.emnlp-demos)
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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
| 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. |
AWESOME: GPU Memory-constrained Long Document Summarization using Memory Mechanism and Global Salient Content (2024.naacl-long)
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| Challenge: | Existing solutions focus on efficient attentions or divide-and-conquer strategies, but these methods sacrifice global context, leading to incoherent and uninformative summaries. |
| Approach: | They propose to leverage the memory-efficient nature of divide-and-conquer methods while preserving global context. |
| Outcome: | The proposed framework improves informativeness, faithfulness, and coherence over baselines on government reports, meeting transcripts, screenplays, scientific papers, and novels. |
TPA: Next Token Probability Attribution for Detecting Hallucinations in RAG (2026.acl-long)
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| Challenge: | Existing approaches attribute hallucinations to a binary conflict between internal knowledge stored in FFNs and the retrieved context. |
| Approach: | They propose a framework which mathematically attributes each next-token probability to seven distinct sources and aggregates source attributions by POS tags to quantify contribution of each model component to the generation of specific linguistic categories within a response. |
| Outcome: | Extensive experiments show that the proposed framework achieves state-of-the-art performance. |
A Closer Look at Few-Shot Out-of-Distribution Intent Detection (2022.coling-1)
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| Challenge: | Existing methods for few-shot out-of-distribution (OOD) intent detection are not adequate . despite its importance, few- shot OOD intent detection is a challenging problem . |
| Approach: | They propose a latent representation generation and self-supervision approach to solve few-shot OOD intent detection problem. |
| Outcome: | The proposed approach is highly effective and could improve state-of-the-art methods for few-shot OOD intent detection. |
LiteVL: Efficient Video-Language Learning with Enhanced Spatial-Temporal Modeling (2022.emnlp-main)
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| Challenge: | Recent large-scale video-language pre-trained models have shown appealing performance on downstream tasks. |
| Approach: | They propose a video-text model that adapts a pre-trained image-language model into a text-based model without heavy pre-training. |
| Outcome: | The proposed model outperforms existing models on video-text retrieval and video question answering tasks without heavy pre-training. |
TaCIE: Enhancing Instruction Comprehension in Large Language Models through Task-Centred Instruction Evolution (2025.coling-main)
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| Challenge: | Existing methods for fine-tuning Large Language Models (LLMs) encounter performance limitations, impeding further enhancements in code generation tasks. |
| Approach: | They propose to combine two distinct prompts through a hybridization process to enhance the evolution of training prompts for code LLMs. |
| Outcome: | The proposed method significantly improves the performance of Code LLMs across five code generation benchmarks. |
The Side Effects of Being Smart: Safety Risks in MLLMs’ Multi-Image Reasoning (2026.acl-long)
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Renmiao Chen, Yida Lu, Shiyao Cui, Xuan Ouyang, Victor Shea-Jay Huang, Shumin Zhang, Chengwei Pan, Han Qiu, Minlie Huang
| Challenge: | Recent advances in multimodal reasoning may pose new safety risks . evaluators neglect reasoningbased safety, where harm emerges only through MLLMs . |
| Approach: | They introduce a benchmark for multi-image reasoning safety that includes 2,676 instances . they find that models with more advanced multi- image reasoning are more vulnerable . |
| Outcome: | The proposed benchmark consists of 2,676 instances covering 9 multi-image relations . the results show that models with more advanced multi- image reasoning are more vulnerable . |
Learn and Unlearn: Addressing Misinformation in Multilingual LLMs (2025.emnlp-main)
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| Challenge: | Existing methods to unlearning large language models (LLMs) focus on English data, but they ignore multilingual contexts and can produce misleading, offensive, or otherwise fake content. |
| Approach: | They investigate the propagation of information in multilingual large language models and evaluate unlearning methods to address harmful content in multi-lingual contexts. |
| Outcome: | The proposed methods can effectively eliminate harmful content for all languages by addressing both English and the original language of the harmful data. |
MOKA: Moral Knowledge Augmentation for Moral Event Extraction (2024.naacl-long)
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| Challenge: | Existing methods for discerning moral values are limited due to lack of context, lack of moral reasoning capabilities and complexity of moral stances. |
| Approach: | They propose a framework for moral event extraction using moral words and moral scenarios. |
| Outcome: | The proposed framework outperforms baselines across three moral event understanding tasks. |
HIBRIDS: Attention with Hierarchical Biases for Structure-aware Long Document Summarization (2022.acl-long)
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| Challenge: | Document structure is critical for efficient information consumption, but it is difficult to encode it efficiently into the modern Transformer architecture. |
| Approach: | They propose a task which injects Hierarchical Biases foR Incorporating Document Structure into attention score calculation. |
| Outcome: | The proposed model produces better question-summary hierarchies than comparisons on hierarchy quality and content coverage, the authors show . |
RW-KD: Sample-wise Loss Terms Re-Weighting for Knowledge Distillation (2021.findings-emnlp)
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| Challenge: | Knowledge Distillation (KD) is used to compress the pre-training and task-specific fine-tuning phases of large neural language models. |
| Approach: | They propose a sample-wise loss weighting method that re-weights the two losses for each sample. |
| Outcome: | The proposed method outperforms existing methods on 7 datasets of the GLUE benchmark. |
Adversarial Textual Robustness on Visual Dialog (2023.findings-acl)
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| Challenge: | a recent study evaluated the robustness of visual dialog models against textual attacks. |
| Approach: | They aim to understand how multimodal input components contribute to robustness . they also evaluate how to generate adversarial test examples which fool the model . |
| Outcome: | The proposed model is more robust when it encodes dialog history than when it does not. |
MCTS: A Multi-Reference Chinese Text Simplification Dataset (2024.lrec-main)
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Ruining Chong, Luming Lu, Liner Yang, Jinran Nie, Zhenghao Liu, Shuo Wang, Shuhan Zhou, Yaoxin Li, Erhong Yang
| Challenge: | Existing studies on text simplification systems have focused on unsupervised methods due to the limited evaluation data in language and domain. |
| Approach: | They propose a Chinese text simplification dataset that provides a detailed analysis and an annotation process. |
| Outcome: | The proposed dataset evaluates the performance of unsupervised methods and advanced large language models. |
Exploring Schema Generalizability of Text-to-SQL (2023.findings-acl)
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| Challenge: | Existing text-to-SQL models are limited in their generalizability, despite their performance being over-estimated. |
| Approach: | They propose a framework to generate novel text-to-SQL data via automatic and synchronous (DS, SQL) pair altering. |
| Outcome: | The proposed framework generates text-to-SQL data via automatic and synchronous (DS, SQL) pair altering. |
Executing Natural Language-Described Algorithms with Large Language Models: An Investigation (2024.lrec-main)
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| 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. |
GEAR: Augmenting Language Models with Generalizable and Efficient Tool Resolution (2024.eacl-long)
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| Challenge: | Recent work on Augmented Language Models (LLMs) over-rely on task-specific demonstrations that limits their generalizability and computational cost. |
| Approach: | They propose a query-tool grounding algorithm that is generalizable to various tasks . they delegate tool grounding and execution to small language models and LLMs . |
| Outcome: | The proposed algorithm outperforms baselines on 14 datasets and shows it can be generalized to different tasks. |
Can Post-Training Transform LLMs into Causal Reasoners? (2026.findings-acl)
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| Challenge: | Causal inference is a core component of human cognition and requires decision-makers to distinguish between causation and association. |
| Approach: | They propose a dataset comprising seven core causal tasks for training and five diverse test sets and evaluate five different post-training approaches. |
| Outcome: | The proposed model achieves 93.5% accuracy on the CaLM benchmark, compared to 55.4% by OpenAI o3. |
No Black Boxes: Interpretable and Interactable Predictive Healthcare with Knowledge-Enhanced Agentic Causal Discovery (2025.findings-emnlp)
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| Challenge: | Deep learning models lacking interpretability and interactivity, authors say . lack of interactive mechanisms prevents clinicians from incorporating their own knowledge into decision-making process. |
| Approach: | a new deep learning model is proposed to improve interpretability and interactivity . authors propose a knowledge-enhanced agent-driven causal discovery framework . |
| Outcome: | a new model improves interpretability and interactivity on EHR data . the proposed model improve interpretability through explicit reasoning and causal analysis . |
Pre-training for Abstractive Document Summarization by Reinstating Source Text (2020.emnlp-main)
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| Challenge: | Abstractive document summarization models are often trained on limited supervised data . authors present three objectives for pretraining abstractive summarizing models . |
| Approach: | They propose to pre-train a SEQ2SEQ based abstractive summarization model on unlabeled text. |
| Outcome: | The proposed method improves on two benchmark summarization datasets with 19GB of text . the goal is sentence reordering, next sentence generation and masked document generation . |
Taking Notes Brings Focus? Towards Multi-Turn Multimodal Dialogue Learning (2025.emnlp-main)
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| Challenge: | Existing multimodal large language models are trained on single-turn vision question-answering tasks, which do not accurately reflect real-world human conversations. |
| Approach: | They propose a large-scale multi-turn multimodal dialogue dataset that uses rules and GPT assistance to generate a multi-turned multimodal dialog dataset. |
| Outcome: | The proposed dataset is a strong benchmark for multi-turn multimodal dialogue learning . it features complex dialogues with contextual dependencies that force models to track, ground, and recall information across multiple turns and disparate visual regions. |
Neo-Classic: A Benchmark for Evaluating Linguistic-Aesthetic Reasoning in Classical Chinese Poetry (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) achieve high accuracy on established Classical Chinese Poetry benchmarks, but it remains challenging to distinguish transferable Linguistic-Aesthetic Reasoning from reliance on familiar pre-training patterns. |
| Approach: | They propose a benchmark that combines a constructionist Out-of-Sample dataset with reverse understanding probes to evaluate large-scale large-format models. |
| Outcome: | The proposed model performs well on classical Chinese poetry benchmarks, but a performance gap persists . the model can complete famous couplets and can be used to understand a variety of texts. |
SynET: Synonym Expansion using Transitivity (2020.findings-emnlp)
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| Challenge: | Existing approaches to find synonyms from text corpora are distributed and pattern based, but they suffer from low precision and low recall. |
| Approach: | They propose a task of synonym expansion using transitivity and propose auxiliary task to reduce the impact of noisy sentences. |
| Outcome: | The proposed approach reduces the impact of noisy sentences and reduces noise in a real-world dataset. |
When Long Helps Short: How Context Length in Supervised Fine-tuning Affects Behavior of Large Language Models (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) have achieved impressive performance across NLP tasks. |
| Approach: | They propose to use long-context SFT to improve short-contemporary performance . they also decouple and analyze two key components, Multi-Head Attention and Feed-Forward Network . |
| Outcome: | The proposed model improves short-context performance, contrary to pretraining. |
CSS: A Large-scale Cross-schema Chinese Text-to-SQL Medical Dataset (2023.findings-acl)
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| Challenge: | a cross-domain text-to-SQL task aims to parse user questions into SQL on complete unseen databases . a single-domain task evaluates the performance on identical databases based on the same domain . |
| Approach: | They propose a cross-domain text-to-SQL task that parses user questions into SQL on unseen databases. |
| Outcome: | The proposed system can parse user questions into SQL on complete unseen databases. |
Uncertainty-Aware Cross-Lingual Transfer with Pseudo Partial Labels (2022.findings-naacl)
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| Challenge: | Existing methods to train pre-trained language models for zero-shot cross-lingual tasks are noisy and lack confidence. |
| Approach: | They propose an uncertainty-aware cross-lingual transfer framework with pseudo-partial-label to maximize the utilization of unlabeled data by reducing noise. |
| Outcome: | The proposed framework outperforms baselines on named entity recognition and natural language inference tasks on 40 languages. |
MathCoder-VL: Bridging Vision and Code for Enhanced Multimodal Mathematical Reasoning (2025.findings-acl)
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Ke Wang, Junting Pan, Linda Wei, Aojun Zhou, Weikang Shi, Zimu Lu, Han Xiao, Yunqiao Yang, Houxing Ren, Mingjie Zhan, Hongsheng Li
| Challenge: | Large Language Models (LMMs) struggle with simple tasks such as geometry, e.g., arithmetic, and reasoning. |
| Approach: | They propose to leverage code as supervision for cross-modal alignment . they propose to use FigCodifier and ImgCode-8.6M to synthesize novel mathematical figures . |
| Outcome: | The proposed model surpasses GPT-4o and Claude 3.5 Sonnet in the geometry problem-solving subset of MathVista, achieving improvements of 8.9% and 9.2%. |
TART: Improved Few-shot Text Classification Using Task-Adaptive Reference Transformation (2023.acl-long)
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| Challenge: | Existing methods for fewshot text classification depend on inter-class variance . Existing approaches suffer from MLADA, which performs poorly on tasks with high inter- class variance whereas it fails to distinguish samples from tasks with low inter-group variance. |
| Approach: | They propose a task-adaptive reference transformation network to transform class prototypes to per-class fixed reference points in task-adapted metric spaces. |
| Outcome: | The proposed method surpasses state-of-the-art methods in 1-shot and 5-shot classifications on the 20 Newsgroups dataset. |
SPARD: Self-Paced Curriculum for RL Alignment via Integrating Reward Dynamics and Data Utility (2026.acl-long)
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Xuyang Zhi, Peilun Zhou, Chengqiang Lu, Hang Lv, Yiwei Liang, Rongyang Zhang, Yan Gao, null Yiwu, Yao Hu, Hongchao Gu, Defu Lian, Hao Wang, Enhong Chen
| Challenge: | Large Language Models (LLMs) are shifting the focus from single verifiable tasks toward complex, open-ended real-world scenarios. |
| Approach: | They propose a framework that automatically adjusts reward weights and data importance to synchronize learning intent with data utility for optimal performance. |
| Outcome: | The proposed framework improves model capabilities across all domains and scales. |
Evolutionary Strategies at Scale lead to Catastrophic Forgetting (2026.acl-short)
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| Challenge: | ES has been shown to improve performance on specific tasks, but it is accompanied by significant forgetting of prior abilities. |
| Approach: | They propose to use Evolutionary Strategies to train gradient-free algorithms to improve performance. |
| Outcome: | The proposed algorithm achieves performance numbers closer to GRPO for math and reasoning tasks, but forgets prior abilities. |
Improving Discriminative Capability of Reward Models in RLHF Using Contrastive Learning (2024.emnlp-main)
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Lu Chen, Rui Zheng, Binghai Wang, Senjie Jin, Caishuang Huang, Junjie Ye, Zhihao Zhang, Yuhao Zhou, Zhiheng Xi, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Current methods rely on ranking losses to teach reward model to assess preferences, but they are susceptible to noise and ambiguous data, often failing to deeply understand human intentions. |
| Approach: | They propose a method that incorporates contrastive learning into the reward modeling process to enhance generalization and stabilize the reinforcement learning training process. |
| Outcome: | The proposed method enhances generalization of the reward model, stabilizes the reinforcement learning training process, and improves the final alignment with human preferences. |
Data Interpreter: An LLM Agent for Data Science (2025.findings-acl)
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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
| 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. |
PclGPT: A Large Language Model for Patronizing and Condescending Language Detection (2024.findings-emnlp)
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| 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. |
Weakly-supervised Text Classification Based on Keyword Graph (2021.emnlp-main)
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| Challenge: | Existing methods for text classification ignore keyword correlation, thus ignoring it . existing methods treat keywords independently, thus not exploiting correlation between them . |
| Approach: | They propose a framework to explore keyword-keyword correlation on keyword graph by GNN . they use a self-supervised task to pretrain annotators and fine-tune them . |
| Outcome: | The proposed method outperforms existing methods on long- and short-text datasets. |
Unified Structure Generation for Universal Information Extraction (2022.acl-long)
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| 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. |
DeepPresenter: Environment-Grounded Reflection for Agentic Presentation Generation (2026.findings-acl)
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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. |
Automatic rubric-based content grading for clinical notes (D19-62)
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| Challenge: | Clinical notes are important documentation critical to medical care, as well as billing and legal needs. |
| Approach: | They propose to evaluate clinical note creation using rubric-based content grading . they build a feature-based system and a neural network-based baseline system . |
| Outcome: | The proposed system can be used to evaluate clinical notes for a rubric-based content grading system . the proposed system has content point accuracy and kappa values at 0.86 and 0.71 on the test set . |
Demystifying Small Language Models for Edge Deployment (2025.acl-long)
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Zhenyan Lu, Xiang Li, Dongqi Cai, Rongjie Yi, Fangming Liu, Wei Liu, Jian Luan, Xiwen Zhang, Nicholas D. Lane, Mengwei Xu
| Challenge: | Small language models (SLMs) are a promising solution for resource-constrained devices such as smartphones and the Web of Things. |
| Approach: | They propose to use SLMs to build and optimize a set of small language models that are publicly accessible. |
| Outcome: | The proposed models outperform 7B models in general tasks, while their in-context learning capabilities remain limited and their efficiency has significant optimization potential. |
Controlling Risk of Retrieval-augmented Generation: A Counterfactual Prompting Framework (2024.findings-emnlp)
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| Challenge: | Existing studies on retrieval-augmented generation (RAG) rarely address the issue of predictive uncertainty, i.e., how likely it is that a RAG model’s prediction is incorrect. |
| Approach: | They propose a framework that induces RAG models to alter latent factors and analyzes the effect on their answers. |
| Outcome: | The proposed framework identifies two critical factors affecting RAG models' confidence in their answers and analyzes the effect on their answers. |
SDA: Simple Discrete Augmentation for Contrastive Sentence Representation Learning (2024.lrec-main)
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| Challenge: | Existing methods for data augmentation have not been well explored. |
| Approach: | They propose to use punctuation insertion, modal verbs, and double negation to produce diverse forms of sentences. |
| Outcome: | The proposed methods perform better on diverse datasets with semantic similarity and standard negation. |
Factorized Learning Assisted with Large Language Model for Gloss-free Sign Language Translation (2024.lrec-main)
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| Challenge: | Previous Sign Language Translation methods have relied on gloss annotations to improve performance, but labeling high-quality glosses is labor-intensive and inefficient. |
| Approach: | They propose to integrate Large Language Model (LLM) into SLT by factorizing learning into two stages to improve the learning curve. |
| Outcome: | The proposed approach improves on three SLT datasets conducted under the gloss-free setting. |
Code-Switching Information Retrieval: Benchmarks, Analysis, and the Limits of Current Retrievers (2026.findings-acl)
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Qingcheng Zeng, Yuheng Lu, Zeqi Zhou, Heli Qi, Puxuan Yu, Fuheng Zhao, Hitomi Yanaka, Weihao Xuan, Naoto Yokoya
| Challenge: | a new study examines the performance of code-switching IR in monolingual contexts . code-witching is a pervasive linguistic phenomenon in global communication . |
| Approach: | They propose a benchmark to evaluate code-switching IR in monolingual contexts . they propose CS-MTEB, which measures performance declines of up to 27% . |
| Outcome: | The proposed benchmark shows that code-switching performance is degraded by 27% . the proposed benchmark is based on a dataset of mixed-language queries . |
DB-Explore: Automated Database Exploration and Instruction Synthesis for Text-to-SQL (2025.findings-emnlp)
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| Challenge: | Recent text-to-SQL systems that use large language models struggle with complex database structures and domain-specific queries. |
| Approach: | a framework that aligns large language models with database knowledge is proposed . DB-Explore constructs database graphs to capture complex relational schemas . |
| Outcome: | a new framework outperforms existing text-to-SQL systems by outperforming existing systems. |
CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarization (2021.emnlp-main)
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| Challenge: | Existing methods for generating abstractive summarization are inconsistent and rely on heuristically created data for error handling. |
| Approach: | They propose a contrastive learning formulation that leverages both positive and negative summaries to train summarization systems that are better at distinguishing between them. |
| Outcome: | The proposed learning framework produces more factual summaries than strong comparisons with post error correction, entailment-based reranking, and unlikelihood training. |
A Prism Module for Semantic Disentanglement in Name Entity Recognition (P19-1)
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| Challenge: | Xu et al., 2015) proposed a noise reduction mechanism to disentangle semantics of words . hard and soft attention mechanisms are used to reduce noise in NLP tasks . |
| Approach: | They propose a prism module to disentangle semantic aspects of words and reduce noise . they propose combining prism modules with downstream models to improve model performance . |
| Outcome: | The proposed method significantly improves the performance of baselines on named entity recognition (NER) tasks. |
X-Instruction: Aligning Language Model in Low-resource Languages with Self-curated Cross-lingual Instructions (2024.findings-acl)
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| Challenge: | Large language models respond well in high-resource languages but struggle in low-resourced languages. |
| Approach: | They propose a method to construct cross-lingual instruction following samples with instruction in English and response in low-resource languages. |
| Outcome: | The proposed method builds a large-scale cross-lingual instruction tuning dataset on 10 languages. |
Entity-aware Image Caption Generation (D18-1)
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| Challenge: | Existing image captioning approaches generate generic descriptions of visual content and ignore background information. |
| Approach: | They propose a task which generates informative image captions using images and hashtags as input. |
| Outcome: | The proposed model outperforms unimodal baselines significantly with evaluation metrics on a dataset from Flickr. |
EPiDA: An Easy Plug-in Data Augmentation Framework for High Performance Text Classification (2022.naacl-main)
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| Challenge: | Existing methods for data augmentation do not fully exploit the potential of DA in NLP. |
| Approach: | They propose an easy and plug-in framework for data augmentation to support effective text classification. |
| Outcome: | The proposed framework outperforms existing methods in most cases, but not using agent networks or pre-trained generation networks. |
A Rationale-Centric Framework for Human-in-the-loop Machine Learning (2022.acl-long)
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| Challenge: | Experimental results show that RDL leads to significant prediction benefits on both in-distribution and out-of-district tests, especially for few-shot learning scenarios. |
| Approach: | They propose a rational-centric framework with human-in-the-loop to exploit spurious associations and bias models towards generally applicable underlying distributions. |
| Outcome: | The proposed framework leads to significant prediction benefits on in-distribution and out-of-district tests, compared to state-of the-art benchmarks. |
LaMP-Val: Large Language Models Empower Personalized Valuation in Auction (2025.findings-emnlp)
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Jie Sun, Tianyu Zhang, Houcheng Jiang, Kexin Huang, Xiang Shu, Zhibo Zhu, Lintao Ma, Xingyu Lu, Jun Zhou, Junkang Wu, Chi Luo, An Zhang, Jiancan Wu, Xiang Wang
| Challenge: | Currently, most research focuses on the bidding algorithms used within auction mechanisms. |
| Approach: | They propose a personalized valuation framework that integrates Large Language Models to incorporate personalized semantic preference into users valuation process. |
| Outcome: | The proposed framework incorporates Large Language Models to incorporate personalized semantic preference into users valuation process. |
Unsupervised Learning of Hierarchical Conversation Structure (2022.findings-emnlp)
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| Challenge: | Goal-oriented conversations often have sub-dialogue structure, but it can be domain-dependent . Increasingly, language understanding applications involve conversational speech and text . |
| Approach: | They propose an unsupervised approach to learning hierarchical conversation structure . they use turn and sub-dialogue segment labels to decode the structure based on dialogue acts and subtasks . |
| Outcome: | The proposed approach improves neural models for three conversation-level understanding tasks. |
SeqVAT: Virtual Adversarial Training for Semi-Supervised Sequence Labeling (2020.acl-main)
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| Challenge: | Empirical studies show that virtual adversarial training (VAT) significantly improves the sequence labeling performance over baselines under supervised and semi-supervised settings. |
| Approach: | They propose a method which naturally applies VAT to sequence labeling models with conditional random field (CRF) Empirical studies show that SeqVAT significantly improves the sequence labelling performance over baselines under supervised settings, and outperforms state-of-the-art approaches under semi-supervised settings. |
| Outcome: | Empirical results show that the proposed method outperforms state-of-the-art approaches under semi-supervised settings. |
Heterogeneous Graph Neural Networks for Concept Prerequisite Relation Learning in Educational Data (2021.naacl-main)
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| Challenge: | Existing methods to learn prerequisite relations among concepts are lacking . concepts are crucial for learning, organizing, applying and generating knowledge . |
| Approach: | They propose a concept prerequisite relation learning approach which combines concept representation and concept pairwise features to make it more practical. |
| Outcome: | The proposed method achieves state-of-the-art results on four datasets. |
AgentFactory: A Self-Evolving Framework Through Executable Subagent Accumulation and Reuse (2026.acl-demo)
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| Challenge: | Existing frameworks for building LLM-based agents treat agent behavior as static-knowledge gained during execution is not preserved for future use. |
| Approach: | They propose a new paradigm that preserves successful task solutions as executable subagent code rather than textual experience. |
| Outcome: | The proposed agent-based agent-driven paradigm preserves successful tasks as executable subagent code rather than textual experience. |
Progressive Self-Supervised Attention Learning for Aspect-Level Sentiment Analysis (P19-1)
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| Challenge: | Experimental results show that our proposed approach yields better attention mechanisms . dominant ASC models are mostly discriminative classifiers based on manual feature engineering . |
| Approach: | They propose a self-supervised approach to aspect-level sentiment classification that mines useful attention supervision information from a training corpus to refine attention mechanisms. |
| Outcome: | The proposed approach yields better attention mechanisms on multiple datasets. |
Interpretable Composition Attribution Enhancement for Visio-linguistic Compositional Understanding (2024.emnlp-main)
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| Challenge: | Despite promising progress, vision-language models still exhibit significant challenges in understanding visio-linguistic concepts beyond object terms. |
| Approach: | They propose a framework that encourages the model to pay greater attention to composition words denoting relationships and attributes within the text. |
| Outcome: | The proposed framework improves the ability to discern intricate details and construct more sophisticated interpretations of combined visual and linguistic elements. |
CKnowEdit: A New Chinese Knowledge Editing Dataset for Linguistics, Facts, and Logic Error Correction in LLMs (2025.acl-long)
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| Challenge: | CKnowEdit is the first-ever knowledge editing dataset designed to correct linguistic, factual, and logical errors in Large Language Models. |
| Approach: | They propose a Chinese knowledge editing dataset to correct linguistic, factual, and logical errors in Large Language Models. |
| Outcome: | The proposed dataset highlights the challenges that LLMs face in mastering Chinese . CKnowEdit can correct linguistic, factual, and logical errors in Chinese, the authors show . |
ALCAP: Alignment-Augmented Music Captioner (2023.emnlp-main)
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| Challenge: | Traditional approaches to music captioning ignore the intricate interplay between the two . however, a comprehensive understanding of music necessitates the integration of both these elements. |
| Approach: | They propose a method to learn multimodal alignment between audio and lyrics through contrastive learning. |
| Outcome: | The proposed method achieves new state-of-the-art on two music captioning datasets. |
DExperts: Decoding-Time Controlled Text Generation with Experts and Anti-Experts (2021.acl-long)
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Alisa Liu, Maarten Sap, Ximing Lu, Swabha Swayamdipta, Chandra Bhagavatula, Noah A. Smith, Yejin Choi
| Challenge: | Decoding-time Experts is a decoding- time method for controlled text generation . it combines a pretrained language model with "expert" LMs and/or "anti-expert" experts . |
| Approach: | They propose a decoding-time method that combines a pretrained language model with "expert" LMs and/or "anti-expert" experts to generate controlled text. |
| Outcome: | The proposed method outperforms existing controllable generation methods on automatic and human evaluations. |
ShieldLM: Empowering LLMs as Aligned, Customizable and Explainable Safety Detectors (2024.findings-emnlp)
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Zhexin Zhang, Yida Lu, Jingyuan Ma, Di Zhang, Rui Li, Pei Ke, Hao Sun, Lei Sha, Zhifang Sui, Hongning Wang, Minlie Huang
| Challenge: | Existing tools for detecting safety issues in LLMs are expensive and inefficient. |
| Approach: | They propose an LLM-based safety detector which annotates the safety of queries and provides explanations for its decisions. |
| Outcome: | The proposed detector outperforms baselines on four sets of query-response pairs and is effective as a safety evaluator for advanced LLMs. |
Extending Phrase Grounding with Pronouns in Visual Dialogues (2022.emnlp-main)
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| Challenge: | Conventional phrase grounding aims to localize noun phrases mentioned in a caption to their corresponding image regions. |
| Approach: | They extend the task by considering pronouns to include noun phrases and pronounos . they construct a dataset of phrase grounding with noun and pronom phrases to image regions . |
| Outcome: | Experiments show that pronouns are easier to ground than noun phrases . a baseline model with coreference information can significantly boost the grounding performance . |
Retrieve, Discriminate and Rewrite: A Simple and Effective Framework for Obtaining Affective Response in Retrieval-Based Chatbots (2021.findings-emnlp)
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| Challenge: | Existing work on retrieval-based chatbots has low-quality affect response . Existing frameworks for obtaining affective response are based on Retrieve-and-Rerank . |
| Approach: | They propose a retrieval-based framework which provides affective response for retrieval chatbots by using a new discriminate-and-rewrite mechanism. |
| Outcome: | The proposed framework outperforms existing baselines and can guarantee the quality of the response and satisfy the affect label. |
Prompt Space Optimizing Few-shot Reasoning Success with Large Language Models (2024.findings-naacl)
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| 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. |
Robust Uncertainty Quantification for Self-Evolving Large Language Models via Continual Domain Pretraining (2026.findings-acl)
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| Challenge: | Conformal prediction (CP) has shown promise in offering correctness guarantees for LLMs, but it faces major challenges in continual domain pretraining (CDP). |
| Approach: | They propose an adaptive rejection and non-exchangeable CP framework that allows the LLM to selectively abstain from answering when its confidence or competence shifts significantly. |
| Outcome: | Experiments show that the proposed framework improves performance under continuous domain pretraining scenarios. |
Can Large Language Models Understand Context? (2024.findings-eacl)
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Yilun Zhu, Joel Moniz, Shruti Bhargava, Jiarui Lu, Dhivya Piraviperumal, Site Li, Yuan Zhang, Hong Yu, Bo-Hsiang Tseng
| Challenge: | Existing evaluation methodologies for Large Language Models (LLMs) have been inadequate to evaluate their ability to understand contextual features. |
| Approach: | They propose a benchmark to assess large language models' ability to understand context by adapting existing datasets to suit their evaluation. |
| Outcome: | The proposed model performs better under the in-context learning pretraining scenario than state-of-the-art models. |
Revamping Multilingual Agreement Bidirectionally via Switched Back-translation for Multilingual Neural Machine Translation (2024.findings-eacl)
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| Challenge: | Current multilingual agreement (MA) methods require parallel data between multiple language pairs, which is not always realistic and optimize the agreement in an ambiguous direction, which hampers the translation performance. |
| Approach: | They propose a novel multilingual agreement framework that optimizes agreement bidirectionally with the Kullback-Leibler Divergence loss. |
| Outcome: | The proposed method improves strong baselines on the task of multilingual neural machine translation with three benchmarks: TED Talks, News, and Europarl. |
Efficient Knowledge Editing via Minimal Precomputation (2025.acl-short)
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| Challenge: | Knowledge editing methods like MEMIT require a one-time but significant computational cost. |
| Approach: | They propose to pre-compute 44 million hidden vectors per edited layer . authors show that this precomputation step is unnecessary . |
| Outcome: | The proposed methods can be performed by pre-computing a small portion of 44 million hidden vectors. |
Paths Not Taken: Understanding and Mending the Multilingual Factual Recall Pipeline (2025.emnlp-main)
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| Challenge: | Multilingual large language models (LLMs) exhibit factual inconsistencies across languages . authors identify two primary sources of error: insufficient engagement of reliable English-centric mechanism for factual recall, and incorrect translation from English back into the target language for the final answer. |
| Approach: | They propose two vector interventions to redirect the model toward better internal paths for higher factual consistency. |
| Outcome: | The proposed interventions increase the recall accuracy by over 35 percent for the lowest-performing language. |
A Neural Transition-based Model for Nested Mention Recognition (D18-1)
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| Challenge: | Existing methods to recognize nested mentions are based on Stack-LSTM . nesting mentions can be used for downstream tasks like question answering and relation extraction. |
| Approach: | They propose a scalable transition-based method to model the nested structure of mentions. |
| Outcome: | The proposed method gets the state-of-the-art performance in ACE datasets showing its effectiveness in detecting nested mentions. |
PreCo: A Large-scale Dataset in Preschool Vocabulary for Coreference Resolution (D18-1)
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| Challenge: | Existing methods for coreference resolution are based on word2vec-like representations of entities. |
| Approach: | They propose a large-scale English dataset for coreference resolution . they use 38K documents and 12.5M words from English-speaking preschoolers . |
| Outcome: | The proposed dataset is more efficient with higher training-test overlap than OntoNotes . the study also shows that mention detection and clustering are more efficient on PreCo . |
Efficient Attentions for Long Document Summarization (2021.naacl-main)
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| Challenge: | Existing models that use full attentions have quadratic computational and memory complexities, and are too costly for long documents. |
| Approach: | They propose an efficient encoder-decoder attention with head-wise positional strides to effectively pinpoint salient information from the source. |
| Outcome: | The proposed model can process ten times more tokens than current models that use full attentions. |
Semi-Supervised Learning for Neural Keyphrase Generation (D18-1)
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| Challenge: | Existing models for keyphrase generation only use labeled data, which is limited to resource-rich domains. |
| Approach: | They propose semi-supervised keyphrase generation methods by leveraging labeled data and large-scale unlabeled samples for learning. |
| Outcome: | The proposed methods outperform state-of-the-art models trained with labeled data and large-scale unlabeled samples for learning. |
ReACC: A Retrieval-Augmented Code Completion Framework (2022.acl-long)
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| Challenge: | Recent work has shown that statistical language modeling with transformers can greatly improve the performance in code completion tasks. |
| Approach: | They propose a retrieval-augmented code completion framework that combines a source code retriever and an auto-regressive language model for programming language. |
| Outcome: | The proposed framework achieves state-of-the-art on CodeXGLUE benchmark. |
LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relations (2021.acl-long)
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| Challenge: | Existing methods to encode text-to-SQL data are node-centric and ignore semantics embedded in the topological structure of edges. |
| Approach: | They propose a Line Graph Enhanced Text-to-SQL model to mine relational features without constructing meta-paths. |
| Outcome: | The proposed model achieves state-of-the-art on the cross-domain text-to-SQL benchmark Spider at the time of writing. |
Attention-Enhancing Backdoor Attacks Against BERT-based Models (2023.findings-emnlp)
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| Challenge: | Existing textual backdoor attacks focus on generating stealthy triggers or modifying model weights. |
| Approach: | They propose a Trojan Attention Loss (TAL) which enhances the Trojan behavior by directly manipulating attention patterns. |
| Outcome: | The proposed method improves the effectiveness of the backdoor attacks on different backbone models and tasks. |
RATE-Nav: Region-Aware Termination Enhancement for Zero-shot Object Navigation with Vision-Language Models (2025.findings-acl)
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| Challenge: | Object navigation is a fundamental task in embodied artificial intelligence. |
| Approach: | They propose a region-aware Termination-Enhanced method that incorporates visual language models and exploration rates to enable efficient termination. |
| Outcome: | The proposed method achieves a success rate of 67.8% and an SPL of 31.3% on the HM3D dataset. |
Ciron: a New Benchmark Dataset for Chinese Irony Detection (2020.lrec-1)
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| Challenge: | Automatic Chinese irony detection often lacks labeled benchmark datasets . despite its pervasive nature, irony is a trope whose actual meaning differs from what is literally enunciated. |
| Approach: | They propose to use a Chinese benchmark dataset for automatic Chinese irony detection to provide a benchmark for machine learning models. |
| Outcome: | The proposed dataset includes more than 8.7K posts, collected from Weibo, a micro blogging platform. |
Automatic Learning of Modality Exclusivity Norms with Crosslingual Word Embeddings (2020.starsem-1)
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| Challenge: | Normative studies on modality for English words are relatively common . however, they are limited to a relatively small number of languages and require costly ratings. |
| Approach: | They aim to learn a mapping between word embeddings and modality norms by training on a high-resource language and testing on . monolingual and crosslingual word embeds are used to predict modality association scores . |
| Outcome: | The proposed model predicts modality associations even when trained on an English resource and tested on a completely unseen language. |
Let’s Think Frame by Frame with VIP: A Video Infilling and Prediction Dataset for Evaluating Video Chain-of-Thought (2023.emnlp-main)
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Vaishnavi Himakunthala, Andy Ouyang, Daniel Rose, Ryan He, Alex Mei, Yujie Lu, Chinmay Sonar, Michael Saxon, William Wang
| Challenge: | Existing studies show vision-language systems can reason about images using natural language, but their capacity for video reasoning remains underexplored. |
| Approach: | They propose to frame video reasoning as the sequential understanding of a small number of keyframes, thereby leveraging the power and robustness of vision-language systems' capacity to reason about images using natural language. |
| Outcome: | The proposed models can generate multiple intermediate keyframes and predict future keyframe, and they perform poorly on GPT-4, GPT-3, and VICUNA. |
Revisiting DocRED - Addressing the False Negative Problem in Relation Extraction (2022.emnlp-main)
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| Challenge: | Using incomplete annotations, we find that false negative samples are prevalent in the DocRED dataset . we reannotate 4,053 documents in the dataset by adding the missed relation triples back to the original DocRED. |
| Approach: | They propose to re-annotate 4,053 documents in the document-level relation extraction dataset by adding missing relation triples back to the original DocRED. |
| Outcome: | The proposed dataset improves on the existing DocRED dataset by 13 F1 points. |
HELENE: Hessian Layer-wise Clipping and Gradient Annealing for Accelerating Fine-tuning LLM with Zeroth-order Optimization (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) face memory challenges due to the high cost of backpropagation. |
| Approach: | They propose a zeroth-order (ZO) optimization that matches memory usage to inference . they propose scalable and memory-efficient zeroth order (ZE) optimizer that integrates annealed A-GNB gradients with diagonal Hessian estimation and layer-wise clipping as a second-order pre-conditioner. |
| Outcome: | The proposed algorithm outperforms state-of-the-art methods with an average speedup of 20 over MeZO on RoBERTa-large and OPT-1.3B. |
MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing (2026.acl-industry)
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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
| 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. |
Self-Para-Consistency: Improving Reasoning Tasks at Low Cost for Large Language Models (2024.findings-acl)
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| Challenge: | Recent studies have shown that self-consistency decoding can improve performance for complex reasoning tasks with large language models. |
| Approach: | They propose a self-consistency decoding strategy that generates multiple paraphrases for each test question and then generates reasoning paths for the original and all the paraphrased questions based on greedy decoding. |
| Outcome: | The proposed strategy reduces the sampling number and improves performance on complex reasoning tasks. |
Representation Potentials of Foundation Models for Multimodal Alignment: A Survey (2025.emnlp-main)
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| Challenge: | foundation models learn highly transferable representations through large-scale pretraining on diverse data. |
| Approach: | They examine the representation potentials of foundation models by examining their latent capacity to capture task-specific information within a single modality while providing a transferable basis for alignment and unification across modalities. |
| Outcome: | The foundation models exhibit remarkable similarities across architectures and modalities, the authors show . the models can capture task-specific information within a single modality while providing a transferable basis for alignment and unification across modality. |
Explorer: Scaling Exploration-driven Web Trajectory Synthesis for Multimodal Web Agents (2025.findings-acl)
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Vardaan Pahuja, Yadong Lu, Corby Rosset, Boyu Gou, Arindam Mitra, Spencer Whitehead, Yu Su, Ahmed Hassan Awadallah
| Challenge: | Recent success in large multimodal models (LMMs) has sparked promising applications of agents capable of autonomously completing complex web tasks. |
| Approach: | They propose a scalable recipe to synthesize the largest and most diverse trajectory-level dataset to date. |
| Outcome: | The proposed model synthesizes the largest and most diverse trajectory-level dataset to date, with 94K successful multimodal web trajectories, 720K screenshots, and 33M web elements. |
Few-shot Named Entity Recognition via Superposition Concept Discrimination (2024.lrec-main)
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| 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. |
Quantifying and Mitigating Unimodal Biases in Multimodal Large Language Models: A Causal Perspective (2024.findings-emnlp)
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| Challenge: | Recent advances in Large Language Models have facilitated the development of Multimodal LLMs. |
| Approach: | They propose a causal framework to interpret unimodal biases in visual question answering problems and a framework to integrate information from different modalities and mitigate biase. |
| Outcome: | The proposed framework analyzes visual question answering (VQA) problems to assess their impact on predictions. |
TransBench: Breaking Barriers for Transferable Graphical User Interface Agents in Dynamic Digital Environments (2025.findings-acl)
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Yuheng Lu, Qian Yu, Hongru Wang, Zeming Liu, Wei Su, Yanping Liu, Yuhang Guo, Maocheng Liang, Yunhong Wang, Haifeng Wang
| Challenge: | Existing GUI agents struggle to adapt to dynamic and interconnected nature of real-world digital environments, authors show . |
| Approach: | They propose a benchmark to evaluate the transferability of GUI agents across three key dimensions . transBench includes 15 app categories with diverse functionalities . |
| Outcome: | The proposed benchmark shows that existing GUI agents struggle to adapt to dynamic, interconnected environments. |
Event Extraction as Question Generation and Answering (2023.acl-short)
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| Challenge: | Recent work on Event Extraction addresses the error propagation issue found in token-based classification approaches. |
| Approach: | They propose a Question Generation (QG) model that generates questions that leverage contextual information instead of fixed templates. |
| Outcome: | The proposed model outperforms all previous single-task-based models on the ACE05 English dataset. |
Socratic-MCTS: Test-Time Visual Reasoning by Asking the Right Questions (2025.emnlp-main)
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| Challenge: | Recent research in vision-language models has centered around the possibility of equipping them with implicit long-form chain-of-thought reasoning via distillation and reinforcement learning. |
| Approach: | They propose a Monte Carlo Tree Search-inspired algorithm that injects subquestion–subanswer pairs into the model’s output stream to elicit hidden knowledge and induce long reasoning traces. |
| Outcome: | The proposed method yields a 2% improvement on MMMU-PRO, including a significant 9% gain in Liberal Arts. |
LeakDojo: Decoding the Leakage Threats of RAG Systems (2026.findings-acl)
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| Challenge: | Existing studies have failed to assess RAG leakage risks for large language models . constructing and maintaining highquality RAG knowledge databases has become increasingly costly . |
| Approach: | They propose a framework for controlled evaluation of RAG leakage using query generation and adversarial instructions. |
| Outcome: | The proposed framework compares six existing attacks across fourteen LLMs, four datasets, and diverse RAG systems. |
FLRC: Fine-grained Low-Rank Compressor for Efficient LLM Inference (2025.emnlp-main)
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| Challenge: | Low-rank compression can reduce memory usage and computational demand, but results are poor during decoding. |
| Approach: | They propose a fine-grained low-rank compression algorithm that determines optimal rank allocation for each layer and incorporates progressive low-ranked decoding to maintain text generation quality. |
| Outcome: | The proposed approach outperforms state-of-the-art methods on summarization tasks and on understanding tasks. |
Pause or Fabricate? Training Language Models for Grounded Reasoning (2026.findings-acl)
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Yiwen Qiu, Linjuan Wu, Yizhou Liu, Yuchen Yan, Jin Ma, Xu Tan, Yao Hu, Daoxin Zhang, Wenqi Zhang, Weiming Lu, Jun Xiao, Yongliang Shen
| Challenge: | Large language models implicitly fabricate information when inputs are incomplete, causing confidence but unreliable conclusions. |
| Approach: | They propose a framework for grounded reasoning under incomplete information that decomposes reasoning into two stages . they propose stage-specific rewards to penalize hallucinations, enabling models to detect gaps, stop proactively, and resume reasoning after clarification. |
| Outcome: | The proposed framework improves premise detection and task success by 30% . it also reduces average response length by over 20% . |
Automatic Construction of a Chinese Review Dataset for Aspect Sentiment Triplet Extraction via Iterative Weak Supervision (2024.lrec-main)
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| Challenge: | Aspect Sentiment Triplet Extraction (ASTE) is a task that involves the extraction of three key elements: target aspects, descriptive opinion spans, and their corresponding sentiment polarity. |
| Approach: | They propose a framework that facilitates automatic construction of Aspect Sentiment Triplet Extraction (ASTE) by iterative weak supervision and a discriminator to weed out subpar samples. |
| Outcome: | The proposed framework automates the construction of Aspect Sentiment Triplet Extraction tasks in Chinese by using iterative weak supervision. |
CogToM: A Comprehensive Theory of Mind Benchmark inspired by Human Cognition for Large Language Models (2026.acl-long)
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Haibo Tong, Zeyang Yue, Feifei Zhao, Erliang Lin, Lu Jia, Ruolin Chen, Yinqian Sun, Qian Zhang, Yi Zeng
| 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. |
Enhancing Speech Large Language Models with Prompt-Aware Mixture of Audio Encoders (2025.emnlp-main)
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Weiqiao Shan, Yuang Li, Yuhao Zhang, Yingfeng Luo, Chen Xu, Xiaofeng Zhao, Long Meng, Yunfei Lu, Min Zhang, Hao Yang, Tong Xiao, JingBo Zhu
| Challenge: | Existing work on integrating audio encoders with large language models (LLMs) has focused on semantic understanding tasks, but different tasks may require distinct features that emphasize either semantic or acoustic aspects. |
| Approach: | They propose to use a prompt-aware mixture to enhance the Speech LLM that uses multiple audio encoders to extract different features based on the prompt. |
| Outcome: | The proposed approach outperforms all single-encoder Speech LLMs on ASR, speaker number verification, and AC tasks. |
Iterative Dual Domain Adaptation for Neural Machine Translation (D19-1)
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| Challenge: | Empirical results on Chinese-English and English-German translation tasks demonstrate the effectiveness of our proposed framework. |
| Approach: | They propose an iterative dual domain adaptation framework for neural machine translation that uses multiple corpora to perform bidirectional translation knowledge transfer. |
| Outcome: | Empirical results on Chinese-English and English-German translation tasks demonstrate the effectiveness of the proposed framework. |
Visual Evidence Prompting Mitigates Hallucinations in Large Vision-Language Models (2025.acl-long)
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| Challenge: | LVLMs have shown impressive progress by integrating visual perception with linguistic understanding to produce contextually grounded outputs. |
| Approach: | They propose a visual evidence prompting method to mitigate hallucinations in large vision-language models by using small visual models to complement them. |
| Outcome: | The proposed method reduces hallucinations by reducing false activation and enhancing correct ones. |
Clear Up Confusion: Advancing Cross-Domain Few-Shot Relation Extraction through Relation-Aware Prompt Learning (2024.naacl-short)
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Ge Bai, Chenji Lu, Daichi Guo, Shilong Li, Ying Liu, Zhang Zhang, Guanting Dong, Ruifang Liu, Sun Yong
| Challenge: | Existing approaches to few-shot Relation Extraction (RE) are prone to confusion when applying knowledge to a target domain with entirely new types of relations. |
| Approach: | They propose a relation-aware prompt learning method with pre-training to clear confusion by decomposing relation types through an innovative label prompt. |
| Outcome: | The proposed method outperforms previous sota methods and yields better results on cross-domain few-shot RE tasks. |
From Toxicity in Online Comments to Incivility in American News: Proceed with Caution (2021.eacl-main)
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| Challenge: | Existing tools for quantifying incivility online, in news and in congressional debates are inadequate for the analysis of incivility in news. |
| Approach: | They develop a Jigsaw Perspective API to quantify incivility in news . they show that toxicity models are inadequate for the analysis of incivility in news. |
| Outcome: | The Jigsaw Perspective API detects incivility on a corpus of American news articles. |
Leveraging Outline-Optimized Generative Interactions and Critique for Self-Refining Outlines with Reinforcement Learning (2026.acl-long)
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| Challenge: | Logic-RL is a framework that transforms critique-guided outline refinement into a learnable policy through reinforcement learning. |
| Approach: | They propose a framework that transforms critique-guided outline refinement into a learnable policy through reinforcement learning. |
| Outcome: | The proposed framework improves on FreshWiki and WikiOutline . it can be iteratively applied, with improved quality continuing through three refinement rounds before diminishing returns. |
Learning Multi-Step Reasoning by Solving Arithmetic Tasks (2023.acl-short)
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| Challenge: | Recent studies have demonstrated large LMs’ impressive performance in solving math problems, but such ability seems only to emerge from models with abundant parameters. |
| Approach: | They propose to continuously pre-train LMs with the capabilities of multi-step reasoning by continuously pretraining them on a synthetic dataset MsAT. |
| Outcome: | The proposed method improves LMs' multi-step reasoning abilities on four math word problem datasets. |
A Systematic Survey of Automatic Prompt Optimization Techniques (2025.emnlp-main)
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Kiran Ramnath, Kang Zhou, Sheng Guan, Soumya Smruti Mishra, Xuan Qi, Zhengyuan Shen, Shuai Wang, Sangmin Woo, Sullam Jeoung, Yawei Wang, Haozhu Wang, Han Ding, Yuzhe Lu, Zhichao Xu, Yun Zhou, Balasubramaniam Srinivasan, Qiaojing Yan, Yueyan Chen, Haibo Ding, Panpan Xu, Lin Lee Cheong
| Challenge: | Recent advances in prompt engineering have created impediments for end users to adopt . however, prompt engineering remains an impedance due to rapid advances in models, tasks, and associated best practices. |
| Approach: | They propose to define APO as a 5-part unifying framework and categorize all relevant works based on their salient features. |
| Outcome: | The proposed framework aims to improve the performance of large language models on various tasks. |
CELLO: Causal Evaluation of Large Vision-Language Models (2024.emnlp-main)
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| Challenge: | Recent advances in large vision-language models have improved causal reasoning abilities . however, current models struggle with tasks like causal reasoning . |
| Approach: | They propose a fine-grained and unified definition of causality involving interactions between humans and objects. |
| Outcome: | The proposed model surpasses traditional commonsense causality by including explicit causal graphs . it also shows that current LVLMs can benefit from a causally inspired prompting strategy . |
Structured Pruning for Efficient Generative Pre-trained Language Models (2023.findings-acl)
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| Challenge: | Large-scale generative Pre-trained Language Models (PLMs) are limited in their deployment in real-world applications. |
| Approach: | They propose to prune the feed-forward networks of generative pre-trained language models to smaller widths without designing extra operators. |
| Outcome: | The proposed method achieves 1.51x/6.96x inference speedup on GPU/CPU with 67% size reduction. |
Multilingual Knowledge Graph Completion with Self-Supervised Adaptive Graph Alignment (2022.acl-long)
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Zijie Huang, Zheng Li, Haoming Jiang, Tianyu Cao, Hanqing Lu, Bing Yin, Karthik Subbian, Yizhou Sun, Wei Wang
| Challenge: | Existing methods to predict missing facts in knowledge graphs are limited in language alignment . SS-AGA uses seed alignment as an edge type to fuses all KGs as a whole graph . |
| Approach: | They propose a self-supervised adaptive graph alignment method that fuses all KGs as a whole graph by regarding alignment as 'a new edge type' they propose SS-AGA method that uses relation-aware attention weights to capture potential alignment pairs in a new paradigm. |
| Outcome: | The proposed method can predict missing facts in a knowledge graph (KG) but language alignment is scarce and new alignment identification is noisy. |
Revealing the Seen, Imagining the Beyond: A Survey of Image-Grounded Chain-of-Thought Reasoning in Multimodal LLMs (2026.acl-long)
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| Challenge: | Recent advances in Multimodal Large Language Models (MLLMs) have shifted visual reasoning from tool-calling to end-to-end perceptionreasoning. |
| Approach: | They synthesize the emerging paradigm of Image-Grounded Chain-of-Thought (IG-CoT) they propose a method-centric taxonomy covering prompting, supervised fine-tuning, and reinforcement learning . |
| Outcome: | The proposed model is based on a method-centric taxonomy and benchmarks. |
Alleviating Exposure Bias in Abstractive Summarization via Sequentially Generating and Revising (2024.lrec-main)
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| Challenge: | Existing approaches to abstractive summarization suffer from exposure bias . Existing solutions bridge this gap through un- or semi-supervised holistic learning . |
| Approach: | They propose to reformat abstractive summarization to sequential generation and revision (SeGRe) this allows the model to assess the flawed summary from a global perspective and modify inappropriate expressions. |
| Outcome: | The proposed model can assess the flawed summary from a global view and modify inappropriate expressions. |
PaperRegister: Boosting Flexible-grained Paper Search via Hierarchical Register Indexing (2026.acl-long)
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| 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. |
HyperCL: A Contrastive Learning Framework for Hyper-Relational Knowledge Graph Embedding with Hierarchical Ontology (2024.findings-acl)
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| Challenge: | Existing studies neglect the ontology of knowledge Graph (KG) embeddings and suffer from the dominance issue of facts over ontologies. |
| Approach: | They propose a framework for hyper-relational KG embeddings that captures the hierarchical ontology and a concept-aware contrastive loss to alleviate the dominance issue. |
| Outcome: | The proposed framework improves on three real-world datasets and shows that it can integrate with other embedding methods and improve link prediction performance. |
Breaking the Noise Barrier: LLM-Guided Semantic Filtering and Enhancement for Multi-Modal Entity Alignment (2025.emnlp-main)
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| Challenge: | Multi-modal entity alignment (MMEA) aims to identify equivalent entities between two multimodal knowledge graphs. |
| Approach: | They propose a novel LLMguided MMEA framework that prioritizes noise reduction before fusion. |
| Outcome: | The proposed framework prioritizes noise reduction before fusion and improves semantics on the noisy FB YG dataset. |
Virtual Context Enhancing Jailbreak Attacks with Special Token Injection (2024.findings-emnlp)
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| 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. |
From Model-centered to Human-Centered: Revision Distance as a Metric for Text Evaluation in LLMs-based Applications (2024.findings-acl)
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| Challenge: | Existing evaluation metrics for large language models yield numerical scores that ignore user experience. |
| Approach: | They propose a metric that suggests revision edits that mimic the human writing process . their results show that the metric offers more insightful feedback and distinguishes between texts . |
| Outcome: | The proposed metric can provide a self-explained text evaluation result in a human-understandable manner beyond the context-independent score. |
ECHA: Jailbreaking LVLMs via the Mismatch between Implicit Semantic Reconstruction and Explicit Safety Alignment (2026.findings-acl)
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| Challenge: | Existing safety guardrails fail to intercept latent intent, whereas LVLMs can implicitly synthesize holistic malicious semantics from fragmented visual cues. |
| Approach: | They propose an Emoji Chain Hinting Attack (ECHA) framework that decouples sensitive concepts into semantically related emoji chains and structural text masks. |
| Outcome: | The proposed framework outperforms existing baselines and bypasses safety guardrails in over 81% of instances with a single attempt. |
Prototype-based Prompt-Instance Interaction with Causal Intervention for Few-shot Event Detection (2024.lrec-main)
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| Challenge: | Few-shot Event Detection (FSED) requires limited labeled data and expensive manual labeling. |
| Approach: | They propose a prototype-based prompt-instance Interaction with causal Intervention model to utilize both prompts and verbalizers and effectively eliminate all biases. |
| Outcome: | The proposed model utilizes both prompts and verbalizers and eliminates all biases on RAMS and ACE datasets. |
Medical Entity Disambiguation with Medical Mention Relation and Fine-grained Entity Knowledge (2024.lrec-main)
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| Challenge: | Existing methods for medical entity disambiguation (MED) fail to fully utilize the knowledge within medical knowledge bases (KBs) Existing models overlook essential interactions between medical mentions and candidate entities, resulting in knowledge- and interaction-inefficient modeling and suboptimal disambiguations performance. |
| Approach: | They propose to combine a mention relation fusion module and an entity knowledge fusion modules to map medical mentions to corresponding entities in a knowledge base (KB) |
| Outcome: | The proposed method outperforms state-of-the-art MED models on two publicly available real-world datasets. |
i-Code V2: An Autoregressive Generation Framework over Vision, Language, and Speech Data (2024.findings-naacl)
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Ziyi Yang, Mahmoud Khademi, Yichong Xu, Reid Pryzant, Yuwei Fang, Chenguang Zhu, Dongdong Chen, Yao Qian, Xuemei Gao, Yi-Ling Chen, Robert Gmyr, Naoyuki Kanda, Noel Codella, Bin Xiao, Yu Shi, Lu Yuan, Takuya Yoshioka, Michael Zeng, Xuedong Huang
| Challenge: | i-Code V2 is one of the first models capable of generating natural language from any combination of Vision, Language, and Speech data. |
| Approach: | They propose to create a model that can generate natural language from any combination of Vision, Language, and Speech data. |
| Outcome: | i-Code V2 matches or outperforms state-of-the-art single- and dual-modality baselines on 7 multimodal tasks. |
Narrative-of-Thought: Improving Temporal Reasoning of Large Language Models via Recounted Narratives (2024.findings-emnlp)
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| Challenge: | Large language models (LLMs) have demonstrated impressive performance in many reasoning tasks, but temporal reasoning remains challenging due to its intrinsic complexity. |
| Approach: | They propose a new prompting technique tailored for temporal reasoning, Narrative-of-Thought (NoT), that first converts the events set to a Python class, then prompts a small model to generate a temporal narrative. |
| Outcome: | The proposed technique achieves the highest F1 on Schema-11 evaluation set, while securing an overall F1 of par with GPT-3.5/4. |
Adaptive Scaling for Sparse Detection in Information Extraction (P18-1)
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| 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. |
Putting Captions to the Test: Evaluating Video Caption Quality through Multiple-Choice Question Answering (2026.acl-long)
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| Challenge: | Existing metrics for video captioning are based on text-based comparisons with ground-truth references. |
| Approach: | They propose a reference-free benchmark that assesses video captions based on their utility . they will release the benchmark to facilitate reproducible research . |
| Outcome: | The proposed benchmark improves on human-verified, fine-grained questions . it correlates significantly better with human judgments than existing metrics . |
Learned Incremental Representations for Parsing (2022.acl-long)
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| Challenge: | a new syntactic representation that commits to syntakic choices is proposed for humans . we use a system that uses only incremental processing of a prefix to predict the word in a sentence . |
| Approach: | They propose a syntactic representation that commits to syntakic choices incrementally . they say the system can achieve 93.72 F1 on the Penn Treebank with as few as 5 bits per word . |
| Outcome: | The proposed representation achieves 93.72 F1 on the Penn Treebank with as few as 5 bits per word . the analysis of the representations shows they have interpretable features and deferred resolution of syntactic ambiguities. |
Robust Preference Optimization via Dynamic Target Margins (2025.findings-acl)
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| Challenge: | Direct Preference Optimization (DPO) is an efficient method for ensuring safety and reliability in practical applications. |
| Approach: | They propose a dynamic target margin preference optimization algorithm that adjusts reward margins at the pairwise level. |
| Outcome: | The proposed method achieves an average 4.4% improvement over baselines, setting new benchmarks for state-of-the-art performance. |
Text2Math: End-to-end Parsing Text into Math Expressions (D19-1)
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| Challenge: | Empirical results on benchmark datasets demonstrate the efficacy of our approach. |
| Approach: | They propose a model for semantically parsing text into math expressions and propose 'text2math' which aims to predict the complete math expression as a tree structure, with minimal manual efforts. |
| Outcome: | Empirical results on benchmark datasets demonstrate the efficacy of the proposed model. |
Enhancing Emotion Recognition in Conversation via Multi-view Feature Alignment and Memorization (2023.findings-emnlp)
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| Challenge: | Emotion recognition in conversation (ERC) is an advanced capability of conversational AI systems. |
| Approach: | They propose a semi-parametric paradigm for Emotion Recognition in conversation that uses supervised contrastive learning to align semantic-view and context-view features. |
| Outcome: | The proposed model achieves state-of-the-art on four widely used benchmarks. |
TheoremQA: A Theorem-driven Question Answering Dataset (2023.emnlp-main)
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| Challenge: | Recent LLMs like GPT-4 and PaLM-2 have made tremendous progress in solving fundamental math problems like GSM8K by achieving over 90% accuracy. |
| Approach: | They propose to use theorem-driven question-answering dataset to evaluate AI models' ability to apply theoretic concepts to solving challenging science problems. |
| Outcome: | TheoremQA is curated by domain experts and contains 800 high-quality questions covering 350 theoremics from Math, Physics, EE&CS, and Finance. |
VDebugger: Harnessing Execution Feedback for Debugging Visual Programs (2024.findings-emnlp)
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| 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. |
AnaloBench: Benchmarking the Identification of Abstract and Long-context Analogies (2024.emnlp-main)
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Xiao Ye, Andrew Wang, Jacob Choi, Yining Lu, Shreya Sharma, Lingfeng Shen, Vijay Murari Tiyyala, Nicholas Andrews, Daniel Khashabi
| Challenge: | Analogical reasoning is an important part of human communication, says a new study . a benchmark to determine analogical reasoning ability in language models is needed . |
| Approach: | They propose to benchmark analogical reasoning ability in language models by collecting 340 analogies from human writings. |
| Outcome: | The proposed benchmark aims to determine analogical reasoning ability in language models. |
Understanding Attention for Text Classification (2020.acl-main)
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| Challenge: | Existing studies have focused on whether local attention weights reflect the importance of input representations. |
| Approach: | They propose to analyze for each word token the following two quantities: its polarity score and its attention score, where the latter is a global assessment on the token’s significance. |
| Outcome: | The proposed model can be improved under conditions where the interplay between the two quantities can contribute towards model performance. |
JointCoder: Exploring Automated ICD Coding on Real-World Chinese EHRs with a Multi-Agent Framework (2026.acl-demo)
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| Challenge: | Existing automated ICD coding systems face several fundamental challenges due to the limited availability of publicly available Chinese ICD datasets. |
| Approach: | They propose to use a Chinese ICD coding dataset and a multi-agent framework to reformulate ICD as a joint disease-procedure coding task. |
| Outcome: | The proposed system outperforms state-of-the-art methods on real-world Chinese ICD coding datasets and 1.7B-parameter models. |
Similar but not the Same: Word Sense Disambiguation Improves Event Detection via Neural Representation Matching (D18-1)
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| Challenge: | Event detection (ED) and word sense disambiguation (WSD) are similar tasks, but they require different neural representations. |
| Approach: | They propose a method to transfer the knowledge learned on WSD to ED by matching neural representations learned for the two tasks. |
| Outcome: | The proposed method can be applied to event detection and word sense disambiguation datasets. |
Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network (P18-1)
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| Challenge: | Existing models for matching dialogue responses rely on semantic and functional dependencies . a recent study only uses the last utterance in context for matching a reply . |
| Approach: | They propose a model that matches a response with its multi-turn context using attention. |
| Outcome: | The proposed model outperforms the state-of-the-art models on two large-scale multi-turn response selection tasks. |
Neural Keyphrase Generation via Reinforcement Learning with Adaptive Rewards (P19-1)
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| Challenge: | Existing generative models generate too few keyphrases, but they often generate too many . et al. (2017) propose a reinforcement learning approach for keyphrase generation . |
| Approach: | They propose a reinforcement learning approach that encourages a model to generate sufficient keyphrases with an adaptive reward function. |
| Outcome: | The proposed method improves state-of-the-art generative models with conventional and new evaluation methods on real-world datasets. |
Beyond Fully Random Masking: Attention-Guided Denoising and Optimization for Diffusion Language Models (2026.acl-long)
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| Challenge: | Existing methods for full-attention dLLMs rely on random masking strategies that overlook intrinsic token dependencies. |
| Approach: | They propose an attention-guided denoising and optimization framework that aligns training and optimization with attention-derived dependencies. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on mathematical and coding benchmarks. |
Learning Syntactic Dense Embedding with Correlation Graph for Automatic Readability Assessment (2021.acl-long)
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| Challenge: | Existing deep learning models for automatic readability assessment discard linguistic features traditionally used for the task. |
| Approach: | They propose to incorporate linguistic features into machine learning models by learning syntactic dense embeddings based on linguistic feature extraction. |
| Outcome: | Experiments with six data sets of two proficiency levels show that the proposed model can perform better than existing models. |
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
| 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. |
All Things Considered: Detecting Partisan Events from News Media with Cross-Article Comparison (2023.emnlp-main)
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| Challenge: | a recent study shows that media influence opinion via the inclusion or omission of partisan events. |
| Approach: | They develop a latent variable-based framework to predict the ideology of news articles by comparing multiple articles on the same story and identifying partisan events whose inclusion or omission reveals ideology. |
| Outcome: | The proposed framework validates the existence of partisan event selection and detects partisan events and article ideology better than baselines. |
An Iterative Emotion Interaction Network for Emotion Recognition in Conversations (2020.coling-main)
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| Challenge: | Emotion recognition in conversations (ERC) is a task that aims to recognize the emotion of each utterance in conversations. |
| Approach: | They propose an iterative emotion interaction network which uses iterativly predicted emotion labels instead of gold emotion labels to explicitly model the emotion interaction. |
| Outcome: | The proposed method retains state-of-the-art performance on two datasets and achieves high accuracy. |
MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering (2021.tacl-1)
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| Challenge: | Existing multilingual QA datasets lack linguistic diversity and comparable evaluation between languages. |
| Approach: | They propose a multilingual question-answer evaluation set with 10k English queries and human translations of them into 25 additional languages and dialects. |
| Outcome: | The proposed model is based on a multilingual knowledge questions and answers evaluation set with 26 languages. |
Adaptive Prompt Optimization for Open-Ended Tasks: Uncertainty Preference as a Secondary Signal (2026.findings-acl)
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| Challenge: | Recent training-free prompt optimizers treat performance as maximizing a single scalar score and ignore a second signal that the desired style is task dependent. |
| Approach: | They propose a semantic-entropy-based method that uses task uncertainty to guide prompt optimization by selecting high-entropicy candidates for creative tasks and low-energetic candidates for conservative ones. |
| Outcome: | The proposed method outperforms baselines on MT-Bench subsets and integrates easily into existing prompt optimizers. |
Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles (2020.emnlp-main)
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| Challenge: | Multi-XScience is a dataset construction protocol that favours abstractive modeling approaches. |
| Approach: | They propose a large-scale multi-document summarization dataset that is based on articles and lexical databases and WordNet synonymy information to generate related-work sections of a paper. |
| Outcome: | The proposed method is based on lexical databases and WordNet synonymy information to write related work sections of a paper based upon their abstract and the articles they reference. |
Reflective Decoding: Beyond Unidirectional Generation with Off-the-Shelf Language Models (2021.acl-long)
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| Challenge: | Existing methods for generating text are unsupervised and require supervision. |
| Approach: | They propose an unsupervised method that uses two off-the-shelf pretrained LMs in opposite directions to apply them to non-sequential tasks. |
| Outcome: | The proposed method outperforms strong unsupervised baselines on paraphrasing and abductive text infilling. |
AutoDetect: Towards a Unified Framework for Automated Weakness Detection in Large Language Models (2024.findings-emnlp)
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Jiale Cheng, Yida Lu, Xiaotao Gu, Pei Ke, Xiao Liu, Yuxiao Dong, Hongning Wang, Jie Tang, Minlie Huang
| Challenge: | Large Language Models (LLMs) exhibit significant but subtle weaknesses, such as mistakes in instruction-following or coding tasks. |
| Approach: | They propose a framework to automatically expose weaknesses in Large Language Models (LLMs) they use three LLM-powered agents to perform comprehensive weakness identification . |
| Outcome: | The proposed framework shows that it is more effective than untargeted data augmentation methods like Self-Instruct to identify weaknesses in LLMs. |
A Table-to-Text Framework with Heterogeneous Multidominance Attention and Self-Evaluated Multi-Pass Deliberation (2023.findings-emnlp)
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| Challenge: | Table-to-text works have been widely applied in different domains, such as weather forecast and financial report generation. |
| Approach: | They propose a table-to-text approach on top of Self-evaluated multi-pass Generation and Heterogenous Multidominance Attention to explore the hierarchical structure. |
| Outcome: | The proposed method outperforms several SOTA methods quantitatively and qualitatively on three public datasets. |
Navigating the Infinite Dynamic Web Space: Effective In-Context Exploration via Cognitive Multi-Agent Collaboration (2026.eacl-long)
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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
| 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. |
WN-Salience: A Corpus of News Articles with Entity Salience Annotations (2020.lrec-1)
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| Challenge: | Existing work on entity salience does not distinguish between salient and non-salient entities. |
| Approach: | They propose a dataset to measure entity salience using WikiNews dataset . WN-Salience is built on top of Wikinews, a Wikimedia project . |
| Outcome: | The proposed dataset can be used to benchmark tasks such as entity salience detection and salient entity linking. |
HomoGraphAdapter: A Homogeneous Graph Neural Network as an Effective Adapter for Vision-Language Models (2025.findings-emnlp)
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| Challenge: | Existing adaptation methods overlook structural knowledge between text and image modalities or create overly complex graphs containing redundant information for alignment. |
| Approach: | They propose a method to adapt visual models to downstream tasks using text and image modalities. |
| Outcome: | The proposed method improves classification accuracy by 1.51% for 1-shot and 0.74% for 16-shot on 11 datasets. |
Compression of Generative Pre-trained Language Models via Quantization (2022.acl-long)
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| Challenge: | Existing methods to compress generative pre-trained language models fail on generative tasks due to homogeneous word embeddings and limited memory. |
| Approach: | They propose a token-level contrastive distillation method to learn distinguishable word embeddings and a module-wise dynamic scaling method to make quantizers adaptive to different modules. |
| Outcome: | The proposed method outperforms the state-of-the-art compression methods on generative PLMs by a clear margin. |
LLM Critics Help Catch Bugs in Mathematics: Towards a Better Mathematical Verifier with Natural Language Feedback (2025.findings-acl)
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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
| 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. |
StorySparkQA: Expert-Annotated QA Pairs with Real-World Knowledge for Children’s Story-Based Learning (2024.emnlp-main)
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Jiaju Chen, Yuxuan Lu, Shao Zhang, Bingsheng Yao, Yuanzhe Dong, Ying Xu, Yunyao Li, Qianwen Wang, Dakuo Wang, Yuling Sun
| Challenge: | Existing story reading systems fail to capture the nuances of how education experts think when conducting interactive story reading activities. |
| Approach: | They propose to use existing question-answering (QA) datasets to capture experts' annotations and thinking process to construct a story-based annotation framework. |
| Outcome: | The proposed framework captures experts’ annotations and thinking process and can be used to generate 5, 868 expert-annotated QA pairs with real-world knowledge. |
Advancing Process Verification for Large Language Models via Tree-Based Preference Learning (2024.emnlp-main)
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| Challenge: | Existing methods for generating step-by-step rationales fail to fully utilize the relative merits of intermediate steps, limiting the effectiveness of feedback provided. |
| Approach: | They propose a tree-based preference learning verifier that constructs reasoning trees via a best-first search algorithm and collects step-level paired data for preference training. |
| Outcome: | The proposed approach outperforms existing benchmarks on arithmetic and commonsense reasoning tasks. |
TIE: Topological Information Enhanced Structural Reading Comprehension on Web Pages (2022.naacl-main)
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| Challenge: | Existing models for structural reading comprehension (SRC) only focus on comprehension of plain text, tables, tables or knowledge bases. |
| Approach: | They propose a topological information enhanced model which transforms a token-level task into a tag-level one by introducing a two-stage process. |
| Outcome: | The proposed model outperforms baselines and achieves state-of-the-art performance on the web-based SRC benchmark WebSRC at the time of writing. |
Deeply Coupled Cross-Modal Prompt Learning (2023.findings-acl)
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| Challenge: | Existing prompt-tuning methods focus on language branch or learn vision-language interaction in a shallow mechanism. |
| Approach: | They propose a Deeply coupled Cross-modal Prompt learning method based on CLIP to facilitate the interplay between vision and language with a Cross-Modal Prompting Attention mechanism. |
| Outcome: | The proposed method enables the interplay between vision and language with a Cross-Modal Prompt Attention mechanism. |
Self-Training with Direct Preference Optimization Improves Chain-of-Thought Reasoning (2024.acl-long)
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| Challenge: | Recent studies focus on enhancing large-scale language models' reasoning abilities, but the research question of how to GSM8K Performance vs. computational cost remains. |
| Approach: | They propose to train small-scale language models with their own outputs to avoid relying on large models' outputs. |
| Outcome: | The proposed approach outperforms baseline models with comparable sizes while minimizing the required compute. |
Break Through the Compression Bottleneck: From Theory to Practice (2026.findings-acl)
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| Challenge: | Existing compression methods suffer from bottleneck issues when compression ratio is increased. |
| Approach: | They propose a novel approach to combine low-rank decomposition and quantization methods to reduce the compression bottleneck. |
| Outcome: | The proposed method reduces the computational and memory overhead of existing methods while maintaining model accuracy. |
Parameter Importance is Not Static: Evolving Parameter Isolation for Supervised Fine-Tuning (2026.acl-long)
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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
| 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. |
Memorizing is Not Enough: Deep Knowledge Injection Through Reasoning (2025.acl-long)
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Ruoxi Xu, Yunjie Ji, Boxi Cao, Yaojie Lu, Hongyu Lin, Xianpei Han, Ben He, Yingfei Sun, Xiangang Li, Le Sun
| 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. |
How to Train Your Fact Verifier: Knowledge Transfer with Multimodal Open Models (2024.findings-emnlp)
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Jaeyoung Lee, Ximing Lu, Jack Hessel, Faeze Brahman, Youngjae Yu, Yonatan Bisk, Yejin Choi, Saadia Gabriel
| Challenge: | a growing influx of misinformation across news and social media is hampered by outdated foundation model training data. |
| Approach: | They propose to use large language models to scale up online policing mechanisms . they evaluate foundation model performance without continual updating . |
| Outcome: | The proposed model can improve performance without continual updating . the proposed model improves on two widely used benchmarks . |
DialogGen: Multi-modal Interactive Dialogue System with Multi-turn Text-Image Generation (2025.findings-naacl)
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Minbin Huang, Yanxin Long, Xinchi Deng, Ruihang Chu, Jiangfeng Xiong, Xiaodan Liang, Hong Cheng, Qinglin Lu, Wei Liu
| Challenge: | Text-to-image (T2I) generation models have advanced in recent years, but effective interaction with these models is challenging for average users due to the need for specialized prompt engineering knowledge and the inability to perform multi-turn image generation. |
| Approach: | They propose to use off-the-shelf MLLMs and T2I models to build a multi-modal interactive dialogue system (MIDS) that can generate correct output modalities and coherence of output images. |
| Outcome: | The proposed pipeline can generate correct output modalities and coherent multi-modal outputs compared with other state-of-the-art models. |
A Rigorous Study on Named Entity Recognition: Can Fine-tuning Pretrained Model Lead to the Promised Land? (2020.emnlp-main)
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| 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. |
Through the Valley: Path to Effective Long CoT Training for Small Language Models (2025.emnlp-main)
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| Challenge: | Long chain-of-thought (CoT) supervision is effective for large language models . but small models trained on limited long CoT data experience performance degradation . |
| Approach: | They identify a phenomenon called Long CoT Degradation in small language models . long CoT data can be used to generate long chain-of-thought (CoT) responses . |
| Outcome: | The results show that models trained on 8k long CoT examples lose up to 75% of their original performance before fine-tuning. |
Empowering Large Language Model for Continual Video Question Answering with Collaborative Prompting (2024.emnlp-main)
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| Challenge: | Existing VideoQA models struggle to adapt to new questions or tasks posed by newly available content. |
| Approach: | They propose a continual learning framework that fine-tunes a large language model for a sequence of tasks and integrates specific question constraint prompting, knowledge acquisition prompting and visual temporal awareness prompting. |
| Outcome: | The proposed model achieves 55.14% accuracy on both NExT-QA and DramaQA datasets and 71.24% accuracy for DramaQA. |
Sparsity-Accelerated Training for Large Language Models (2024.findings-acl)
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| Challenge: | Large language models (LLMs) have demonstrated proficiency across various NLP tasks but often require additional training, such as continual pre-training and supervised fine-tuning. |
| Approach: | They propose to leverage sparsity in pre-trained LLMs to accelerate training by disregarding computations for unimportant neurons. |
| Outcome: | The proposed framework achieves comparable or superior performance to standard training while significantly accelerating the process. |
ShadowGNN: Graph Projection Neural Network for Text-to-SQL Parser (2021.naacl-main)
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| Challenge: | Existing semantic parsing models struggle to adapt to unseen database schemas . a new architecture, ShadowGNN, processes schemas at abstract and semantic levels . |
| Approach: | They propose a new architecture which processes schemas at abstract and semantic levels. |
| Outcome: | The proposed architecture outperforms state-of-the-art models on a text-to-sql benchmark . it uses domain-independent representations to extract logical linking between question and schema . |
LLM-Driven Completeness and Consistency Evaluation for Cultural Heritage Data Augmentation in Cross-Modal Retrieval (2025.emnlp-main)
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| Challenge: | Cross-modal retrieval is essential for interpreting cultural heritage data, but its effectiveness is limited by incomplete or inconsistent textual descriptions. |
| Approach: | They propose a data augmentation framework that enhances cross-modal retrieval performance by improving the completeness and consistency of LLM-generated descriptions. |
| Outcome: | The proposed framework improves cross-modal retrieval performance by improving completeness and consistency of LLM-generated descriptions. |
AgentCPM-GUI: Building Mobile-Use Agents with Reinforcement Fine-Tuning (2025.emnlp-demos)
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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
| 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. |
D-Artemis: A Deliberative Cognitive Framework for Mobile GUI Multi-Agents (2026.findings-acl)
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Hongze Mi, Yibo Feng, WenJie Lu, Yuqi Wang, Jinyuan Li, Song Cao, He Cui, Tengfei Tian, Xuelin Zhang, Haotian Luo, Di Sun, Jun Fang, Hua Chai, Naiqiang Tan, Gang Pan
| Challenge: | Graphical User Interface (GUI) agents aim to automate a wide spectrum of human tasks by emulating user interaction. |
| Approach: | They propose a deliberative framework that leverages a fine-grained tip retrieval mechanism to inform its decision-making process. |
| Outcome: | The proposed framework achieves SOTA among open-source general models on AndroidWorld and ScreenSpot-V2 . it leverages a fine-grained, app-specific tip retrieval mechanism to inform its decision-making process . |
Measuring Pointwise 𝒱-Usable Information In-Context-ly (2023.findings-emnlp)
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| Challenge: | In-context learning (ICL) is a new learning paradigm that has gained popularity along with the development of large language models. |
| Approach: | They propose to adapt a recently proposed hardness metric, pointwise V-usable information (PVI), to an in-context version. |
| Outcome: | The proposed hardness metric is compared with the original model and is more efficient because it requires only a few exemplars and does not require fine-tuning. |
Twitter Homophily: Network Based Prediction of User’s Occupation (P19-1)
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| Challenge: | Existing approaches to predicting Twitter users' demographic attributes exploit, select, and combine various features generated from text and network to achieve the best performance. |
| Approach: | They extend existing Twitter occupational class prediction data set and exploit social network homophily to achieve competitive performance. |
| Outcome: | The proposed method achieves better performance on a dataset with a small fraction of the training data. |
Document Segmentation Matters for Retrieval-Augmented Generation (2025.findings-acl)
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Zhitong Wang, Cheng Gao, Chaojun Xiao, Yufei Huang, Shuzheng Si, Kangyang Luo, Yuzhuo Bai, Wenhao Li, Tangjian Duan, Chuancheng Lv, Guoshan Lu, Gang Chen, Fanchao Qi, Maosong Sun
| Challenge: | Existing rule-based chunking methods lead to suboptimal splits, where overly large chunks introduce irrelevant information and small chunks lack semantic coherence. |
| Approach: | They propose a method that leverages document summaries as pseudo-instructions to guide chunking by computing semantic similarity between sentences and the summary. |
| Outcome: | Experiments on multiple open-domain question-answering benchmarks show that PIC significantly improves retrieval accuracy (Hits@k) and end-to-end QA performance (Exact Match) without any additional training. |
CHENGYU-BENCH: Benchmarking Large Language Models for Chinese Idiom Understanding and Use (2025.emnlp-main)
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| Challenge: | Existing benchmarks focus on narrow tasks such as multiple-choice cloze tests, isolated translation, or simple paraphrasing. |
| Approach: | They propose a benchmark to measure Chinese idioms' cultural and contextual nuances . they evaluate 2,937 human-verified examples covering 1,765 common idiomes . |
| Outcome: | The proposed benchmarks achieve 95% accuracy on Evaluative Connotation, but only 85% on Appropriateness and 40% top-1 accuracy in Open Cloze. |
The Role of Mixed-Language Documents for Multilingual Large Language Model Pretraining (2026.acl-long)
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Jiandong Shao, Raphael Tang, Crystina Zhang, Karin Sevegnani, Pontus Stenetorp, Jianfei Yang, Yao Lu
| Challenge: | Existing research suggests that multilingual large language models can achieve impressive cross-lingual understanding despite largely monolingual pretraining. |
| Approach: | They compare a monolingual-only corpus with a standard web corpus that removes all multilingual documents and then retrain the models from scratch under controlled conditions. |
| Outcome: | The results show that removing bilingual data causes translation performance to drop 56% in BLEU, whereas code-switching contributes minimally. |
Jointly Learning Semantic Parser and Natural Language Generator via Dual Information Maximization (P19-1)
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| Challenge: | Semantic parsing aims to transform natural language utterances into formal meaning representations (MRs) whereas an NL generator achieves the reverse, the two tasks are often studied separately. |
| Approach: | They propose a method of dual information maximization to regularize the learning process by matching the joint distributions of p and q of NLs. |
| Outcome: | The proposed method empirically maximizes the variational lower bounds of expected joint distributions of NL and MRs. |
ATGL: An Adaptive-Threshold Global Loss for Document-level Relation Extraction (2026.acl-long)
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| Challenge: | Document-level relation extraction (DocRE) aims to determine which relations hold between a given entity pair in a document. |
| Approach: | They propose a document-level relation extraction paradigm that decouples existing losses into independent positive and negative losses, which interact solely with a shared threshold. |
| Outcome: | The proposed model outperforms existing models on four datasets and achieves state-of-the-art results. |
ToM-Synth: Scaling Robust Theory of Mind in LLMs via 6,912 Structured Social Units (2026.findings-acl)
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Guiyang Hou, Xiang Huang, Shangke Lyu, Yuchuan Wu, Weiyao Luo, Xinyu Mei, Yongliang Shen, Weiming Lu, Yongbin Li
| Challenge: | Existing methods endowing LLMs with Theory of Mind fail to internalize the augmented ToM into the LLM. |
| Approach: | They propose a factorial combinatorial synthesis framework that enables systematic synthesis of ToM data and uses it for RL fine-tuning. |
| Outcome: | The proposed framework yields a training dataset of 27,648 instances. |
MDBench: A Synthetic Multi-Document Reasoning Benchmark Generated with Knowledge Guidance (2025.findings-acl)
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| Challenge: | Multi-document reasoning is an area of increasing relevance given LLM capabilities in handling longer-context inputs, but few benchmarks exist to rigorously examine model behavior in this setting. |
| Approach: | They propose a new dataset for evaluating LLMs on the task of multi-document reasoning that uses condensed structured seed knowledge to modify it through LLM-assisted edits. |
| Outcome: | The proposed method generates document sets and QA examples on a multi-document reasoning task using a synthetic generation process. |
Learning to Refine: Self-Refinement of Parallel Reasoning in LLMs (2026.findings-acl)
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Qibin Wang, Pu Zhao, Shaohan Huang, Fangkai Yang, Lu Wang, Furu Wei, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang
| 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. |
AgentGym: Evaluating and Training Large Language Model-based Agents across Diverse Environments (2025.acl-long)
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Zhiheng Xi, Yiwen Ding, Wenxiang Chen, Boyang Hong, Honglin Guo, Junzhe Wang, Xin Guo, Dingwen Yang, Chenyang Liao, Wei He, Songyang Gao, Lu Chen, Rui Zheng, Yicheng Zou, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang, Zuxuan Wu, Yu-Gang Jiang
| Challenge: | Large language models (LLMs) are promising foundations to build generally-capable agents . however, the community lacks a unified interactive framework that covers diverse environments for comprehensive evaluation of agents. |
| Approach: | They propose a framework that features 7 real-world scenarios, 14 environments, and 89 tasks for unified, real-time, and concurrent agent interaction. |
| Outcome: | The proposed framework features 7 real-world scenarios, 14 environments, and 89 tasks for unified, real-time, and concurrent agent interaction. |
Few-shot Reranking for Multi-hop QA via Language Model Prompting (2023.acl-long)
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| Challenge: | Existing methods for multi-hop QA with open-domain questions require a large number of labeled question-document pairs for retrieval. |
| Approach: | They propose a language-based prompt for multi-hop path reranking that relies on language model prompting to generate a relevance score between a question and the path. |
| Outcome: | The proposed method yields strong retrieval performance on HotpotQA with only 128 training examples compared to state-of-the-art methods trained on thousands of examples. |
Why Supervised Fine-Tuning Fails to Learn: A Systematic Study of Incomplete Learning in Large Language Models (2026.acl-long)
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Chao Xue, Yao Wang, Mengqiao Liu, Di Liang, Xingsheng Han, Peiyang Liu, Xianjie Wu, Chenyao Lu, Lei Jiang, Yu Lu, Haibo Shi, Shuang Liang, Minlong Peng, Flora D. Salim
| Challenge: | Incomplete learning is widespread and heterogeneous in large language models . authors identify five recurrent sources of incomplete learning: missing prerequisite knowledge, conflicts between SFT supervision and pre-training knowledge, internal inconsistencies within SFT data, left-side forgetting during sequential fine-tuning, and insufficient optimization for rare or complex patterns. |
| Approach: | They propose a diagnostic-first framework that maps incomplete learning to causes . they identify five recurrent sources of incomplete learning: missing prerequisite knowledge, conflicts between supervision and pre-training knowledge, internal inconsistencies, left-side forgetting during sequential fine-tuning, and insufficient optimization for rare or complex patterns. |
| Outcome: | The proposed framework maps incomplete learning to causes using observable training and inference signals. |
ConsistentChat: Building Skeleton-Guided Consistent Multi-Turn Dialogues for Large Language Models from Scratch (2025.emnlp-main)
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Jiawei Chen, Xinyan Guan, Qianhao Yuan, Mo Guozhao, Weixiang Zhou, Yaojie Lu, Hongyu Lin, Ben He, Le Sun, Xianpei Han
| 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. |
Does BERT Rediscover a Classical NLP Pipeline? (2022.coling-1)
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| Challenge: | Existing theories of BERT's structure lack conclusive empirical support . however, there is scepticism about the premises of probing itself . |
| Approach: | They propose a new probe called GridLoc that can take into account token positions, training rounds, and random seeds. |
| Outcome: | The proposed probe detects other, stronger regularities suggesting appeals to layer depth may not be the preferable mode of explanation for BERT’s inner workings. |
AUGUST: an Automatic Generation Understudy for Synthesizing Conversational Recommendation Datasets (2023.findings-acl)
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| Challenge: | Existing work on conversational recommendation systems lacks high-quality data . existing datasets lack large-scale and high-level data based on human annotators . |
| Approach: | They propose an automatic dataset synthesis approach that generates large-scale recommendation dialogues using structured graphs based on user-item information from the real world. |
| Outcome: | The proposed approach can generate large-scale and high-quality recommendation dialogues . it exploits user preferences, knowledge graphs, and conversation ability from existing datasets based on real-world data . |
ParaCook: On Time-Efficient Planning for Multi-Agent Systems (2026.findings-acl)
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Shiqi Zhang, Xinbei Ma, Yunqing Xu, Zouying Cao, Pengrui Lu, Haobo Yuan, Tiancheng Shen, Zhuosheng Zhang, Hai Zhao, Ming-Hsuan Yang
| Challenge: | Existing agent benchmarks focus on task completion while neglecting time efficiency in parallel and asynchronous operations. |
| Approach: | They propose a framework for large language models that allows agents to plan long-horizon tasks in a scalable way. |
| Outcome: | The proposed framework is based on the Overcooked game and can be used to evaluate time efficiency-aware multi-agent planning. |
Dynamic Online Conversation Recommendation (2020.acl-main)
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| Challenge: | Existing models that assume static user interests are unable to capture the temporal aspects of user interactions and interest changes over time. |
| Approach: | They propose a neural architecture to exploit changes of user interactions and interests over time to predict which discussions they are likely to enter. |
| Outcome: | The proposed model outperforms state-of-the-art models that assume static user interests and handle future conversations that are unseen during training time. |
Tuna: Instruction Tuning using Feedback from Large Language Models (2023.findings-emnlp)
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| Challenge: | LLms like LLaMA have shown to be cost-effective for generating better responses . however, the instruction-tuned model has only seen one response per instruction . |
| Approach: | They propose to fine tune an instruction-tuned LLM using probabilistic ranking and contextual ranking approaches to increase the likelihood of generating better responses. |
| Outcome: | The proposed model improves on Super Natural Instructions, LMentry and Vicuna QA. |
CLEAN–EVAL: Clean Evaluation on Contaminated Large Language Models (2024.findings-naacl)
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Wenhong Zhu, Hongkun Hao, Zhiwei He, Yun-Ze Song, Jiao Yueyang, Yumeng Zhang, Hanxu Hu, Yiran Wei, Rui Wang, Hongyuan Lu
| Challenge: | Existing methods to evaluate large language models are prone to data contamination. |
| Approach: | They propose a method which parses contaminated data and back-translates it into a candidate set. |
| Outcome: | The proposed method reduces data contamination and evaluates the LLMs more cleanly. |
Connecting the Dots between Audio and Text without Parallel Data through Visual Knowledge Transfer (2022.naacl-main)
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| Challenge: | Existing methods for learning audio-text connections rely on parallel audio- text data . a new approach allows for the representation of environmental soundscapes without using parallel data - a challenge for many applications . |
| Approach: | They propose a model that induces Audio-Text alignment without using parallel audio-text data. |
| Outcome: | The proposed model outperforms the current state-of-the-art for audio classification tasks with no audio-text data by 2.2% on the ESC50 and US8K tasks. |
SAGE : A Top-Down Bottom-Up Knowledge-Grounded User Simulator for Multi-turn Agent Evaluation (2026.findings-eacl)
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| Challenge: | Existing evaluation methods rely on static benchmarks or narrow task-specific datasets that fail to capture the open-ended nature of real-world interactions. |
| Approach: | They propose a user Simulation framework for multi-turn AGent Evaluation that integrates top-down knowledge from business contexts and bottom-up knowledge from agent infrastructure. |
| Outcome: | The proposed framework produces interactions that are more realistic and diverse while identifying up to 33% more agent errors. |
Out-of-Scope Intent Detection with Self-Supervision and Discriminative Training (2021.acl-long)
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| Challenge: | Existing methods for out-of-scope intent detection rely on strong assumptions on data distribution and confidence threshold selection. |
| Approach: | They propose a method to train an out-of-scope intent classifier in a fully end-to-end manner by simulating the test scenario in training. |
| Outcome: | The proposed method improves on four benchmark dialogue datasets and improves over state-of-the-art methods. |
SC-LSTM: Learning Task-Specific Representations in Multi-Task Learning for Sequence Labeling (N19-1)
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| Challenge: | Multi-task learning (MTL) has been studied for sequence labeling tasks . auxiliary tasks are selected specifically to improve performance of a target task . |
| Approach: | They propose a shared-cell long-short-term memory cell which contains shared parameters that can learn from all tasks and task-specific parameters that could learn task-related information. |
| Outcome: | The proposed model can learn from all tasks and task-specific parameters. |
Self-Harmonized Chain of Thought (2025.naacl-long)
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| Challenge: | Existing methods for chain-of-thought prompting have limitations . arithmetic, commonsense, and symbolic reasoning tasks are challenging . |
| Approach: | They propose a method that unifies diverse solution paths into a consistent reasoning pattern. |
| Outcome: | The proposed method outperforms existing methods by 2.8% on reasoning tasks. |
NewsInterview: a Dataset and a Playground to Evaluate LLMs’ Grounding Gap via Informational Interviews (2025.acl-long)
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Alexander Spangher, Michael Lu, Sriya Kalyan, Hyundong Justin Cho, Tenghao Huang, Weiyan Shi, Jonathan May
| Challenge: | Existing large datasets (1k-10k transcripts) are generated via crowdsourcing and are inherently unnatural. |
| Approach: | They curate a dataset of 40,000 two-person informational interviews from NPR and CNN . they find that LLMs are significantly less likely than human interviewers to use acknowledgements and pivot to higher-level questions. |
| Outcome: | The proposed model is based on 40,000 interviews with journalists and CNN . |
LayoutDIT: Layout-Aware End-to-End Document Image Translation with Multi-Step Conductive Decoder (2023.findings-emnlp)
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| Challenge: | Existing methods struggle to capture the visual layout in complex document images. |
| Approach: | They propose to integrate layout knowledge into document image translation by using a layout-aware encoder and a multi-step conductive decoder to achieve the translation step by step. |
| Outcome: | The proposed model outperforms state-of-the-art methods with better parameter efficiency. |
ELISA-EDL: A Cross-lingual Entity Extraction, Linking and Localization System (N18-5)
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| 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. |
MIME: MIMicking Emotions for Empathetic Response Generation (2020.emnlp-main)
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Navonil Majumder, Pengfei Hong, Shanshan Peng, Jiankun Lu, Deepanway Ghosal, Alexander Gelbukh, Rada Mihalcea, Soujanya Poria
| Challenge: | Empathy is a fundamental human trait that reflects our ability to understand and reflect the thoughts and feelings of the people we interact with. |
| Approach: | They propose to use polarity-based emotion clusters to generate empathetic responses . they also introduce stochasticity into the emotion mixture that yields emotionally more varied responses compared to the previous work . |
| Outcome: | The proposed methods improve empathy and contextual relevance of the response, and introduce stochasticity into the emotion mixture that yields emotionally more varied responses than the previous work. |
BotEval: Facilitating Interactive Human Evaluation (2024.acl-demos)
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| Challenge: | Using language models to perform complex interactive tasks is becoming more common with the rapid progress in natural language processing (NLP) models. |
| Approach: | They develop an evaluation toolkit that enables human-bot interactions as part of the evaluation process. |
| Outcome: | The evaluation toolkit enables human-bot interactions as part of the evaluation process, rather than making judgements for a static input. |
Insert or Attach: Taxonomy Completion via Box Embedding (2024.acl-long)
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| Challenge: | Existing taxonomy expansion methods embed concepts as vectors in Euclidean space, causing incorrectly model asymmetric relations. |
| Approach: | They propose to use box containment and center closeness to create geometric scorers that capture intrinsic relationships between concepts. |
| Outcome: | The proposed framework outperforms existing methods on four real-world datasets. |
Critic-CoT: Boosting the Reasoning Abilities of Large Language Model via Chain-of-Thought Critic (2025.findings-acl)
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Xin Zheng, Jie Lou, Boxi Cao, Xueru Wen, Yuqiu Ji, Hongyu Lin, Yaojie Lu, Xianpei Han, Debing Zhang, Le Sun
| 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. |
Enabling Lightweight Fine-tuning for Pre-trained Language Model Compression based on Matrix Product Operators (2021.acl-long)
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| Challenge: | Existing methods for pre-trained language models (PLMs) use parameter reduction techniques. |
| Approach: | They propose a pre-trained language model compression approach based on the matrix product operator from quantum many-body physics. |
| Outcome: | The proposed approach can decompose an original matrix into central tensors and auxiliary tenses . it can be applied to the original or compressed PLMs in a general way, with a lighter network . |
JoPA: Explaining Large Language Model’s Generation via Joint Prompt Attribution (2025.acl-long)
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| 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. |
From Conversation to Automation: Leveraging LLMs for Problem-Solving Therapy Analysis (2025.findings-acl)
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| Challenge: | Problem-Solving Therapy (PST) is a structured psychological approach that helps individuals manage stress and resolve personal issues. |
| Approach: | They developed a framework for PST annotation using established PST Core Strategies and a set of novel Facilitative Strategies to analyze a corpus of real-world therapy transcripts to determine which strategies are most prevalent. |
| Outcome: | The proposed framework outperforms existing models and LLMs to identify the most prevalent strategies in a corpus of real-world therapy transcripts. |
NovaCOMET: Open Commonsense Foundation Models with Symbolic Knowledge Distillation (2023.findings-emnlp)
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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
| 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. |
Amphista: Bi-directional Multi-head Decoding for Accelerating LLM Inference (2025.naacl-long)
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Zeping Li, Xinlong Yang, Ziheng Gao, Ji Liu, Guanchen Li, Zhuang Liu, Dong Li, Jinzhang Peng, Lu Tian, Emad Barsoum
| Challenge: | Existing methods such as Medusa lack adequate information interaction between different drafting heads. |
| Approach: | They propose an enhanced speculative decoding framework that builds upon Medusa and integrates a drafting block capable of parallel inference. |
| Outcome: | The proposed framework outperforms Medusa in terms of head accuracy and latency. |
PCC: Paraphrasing with Bottom-k Sampling and Cyclic Learning for Curriculum Data Augmentation (2023.eacl-main)
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| Challenge: | Curriculum Data Augmentation (CDA) presents synthetic data with increasing difficulties to neural models. |
| Approach: | They propose a curriculum-aware paraphrase generation module with bottom-k sampling and cyclic learning strategy that passes through the curriculums multiple times. |
| Outcome: | The proposed framework surpasses competitive baselines on few-shot text classification and dialogue generation. |
FLAT-LLM: Fine-grained Low-rank Activation Space Transformation for Large Language Model Compression (2026.findings-eacl)
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| Challenge: | Low-rank decomposition methods suffer from accuracy degradation and expensive calibration procedures. |
| Approach: | They propose a fast and accurate, training-free structural compression method based on fine-grained low-rank transformations in the activation space. |
| Outcome: | The proposed method outperforms pruning baselines in generalization and downstream performance while delivering inference speedups. |
BadWindtunnel: Defending Backdoor in High-noise Simulated Training with Confidence Variance (2025.findings-acl)
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| Challenge: | Current backdoor attack defenders in NLP typically involve data reduction or model pruning, risking losing crucial information. |
| Approach: | They propose a backdoor defender that allows precise control over training conditions to model backdoor learning behavior without affecting the final model. |
| Outcome: | The proposed model reduces the backdoor learning behavior without affecting the final model. |
MIND: From Passive Mimicry to Active Reasoning through Capability-Aware Multi-Perspective CoT Distillation (2026.acl-long)
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Jin Cui, Jiaqi Guo, Jiepeng Zhou, Ruixuan Yang, Jiayi Lu, Jiajun Xu, Jiangcheng Song, Boran Zhao, Pengju Ren
| Challenge: | Existing approaches restrict students to following a single golden rationale and treat different reasoning paths independently, causing suboptimal performance. |
| Approach: | They propose a capability-adaptive framework that transitions distillation from passive mimicry to active cognitive construction and employ a feedback-driven inertia calibration mechanism to align supervision with the student’s current adaptability. |
| Outcome: | Experiments show that the proposed framework achieves state-of-the-art performance on both in-distribution and out-of distribution benchmarks. |
Enhance Robustness of Sequence Labelling with Masked Adversarial Training (2020.findings-emnlp)
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| Challenge: | Adversarial training (AT) has shown strong regularization effects on deep learning algorithms by introducing small input perturbations to improve model robustness. |
| Approach: | They propose to use adversarial training to improve robustness from contextual information in sequence labelling tasks by masking or replacing some words in the sentence. |
| Outcome: | The proposed method shows significant improvements on accuracy and robustness of sequence labelling on CoNLL 2000 and 2003 benchmarks. |
Late Fusion with Triplet Margin Objective for Multimodal Ideology Prediction and Analysis (2022.emnlp-main)
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| Challenge: | Prior work on ideology prediction has focused on single modalities, i.e., text or images. |
| Approach: | They propose a task where a model predicts binary or five-point scale ideological leanings given a text-image pair with political content. |
| Outcome: | The proposed model outperforms the state-of-the-art model by almost 4% and a strong multimodal baseline with no pretraining by over 3%. |
Is LLM an Overconfident Judge? Unveiling the Capabilities of LLMs in Detecting Offensive Language with Annotation Disagreement (2025.findings-acl)
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| 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. |
SSR: Utilizing Simplified Stance Reasoning Process for Robust Stance Detection (2022.coling-1)
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| Challenge: | Existing methods for stance detection are task-agnostic, which fail to utilize task knowledge to better discriminate between genuine and bias features. |
| Approach: | They propose to incorporate stance reasoning process as task knowledge to aid in learning genuine features without using targets. |
| Outcome: | The proposed model achieves better performance than previous task-agnostic debiasing methods on new test sets. |
RepoGenesis: Benchmarking End-to-End Microservice Generation from Readme to Repository (2026.acl-long)
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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. |
Mitigating Boundary Ambiguity and Inherent Bias for Text Classification in the Era of Large Language Models (2024.findings-acl)
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| Challenge: | a new text classification framework for large language models addresses the problem of boundary ambiguity and inherent biases in LLMs. |
| Approach: | They propose a two-stage classification framework for large language models to mitigate bottlenecks . their approach uses pairwise comparisons to efficiently narrow down options . |
| Outcome: | The proposed framework reduces the number of options and improves on four datasets. |
SERE: Structural Example Retrieval for Enhancing LLMs in Event Causality Identification (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) have demonstrated strong performance across various NLP tasks, but their effectiveness in ECI remains limited due to biases in causal reasoning. |
| Approach: | They propose a structural example retrieval framework that leverages LLMs’ few-shot learning capabilities to help LLM models in ECI. |
| Outcome: | The proposed framework leverages LLMs’ few-shot learning capabilities to guide LLM models in causal reasoning, mitigating bias and improving accuracy. |
Social Bot-Aware Graph Neural Network for Early Rumor Detection (2022.coling-1)
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| Challenge: | Existing models do not distinguish genuine users from social bots, and their failure in identifying rumors timely. |
| Approach: | They propose to account for social bots’ behavior and construct a Social Bot-Aware Graph Neural Network to model early propagation of posts and then use it to detect rumors. |
| Outcome: | The proposed method achieves significant improvements over baselines and identifies rumors within 3 hours while maintaining more than 90% accuracy. |
Asymmetric feature interaction for interpreting model predictions (2023.findings-acl)
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| Challenge: | Prior work on feature interaction attribution studies focus on asymmetric interaction that only explains the additional influence of a set of words in combination, which fails to capture asymmetry influence that contributes to model prediction. |
| Approach: | They propose an asymmetric feature interaction attribution explanation model that explores asymmetry higher-order feature interactions in the inference of deep neural NLP models. |
| Outcome: | The proposed model outperforms state-of-the-art models on two sentiment classification datasets. |
ChatGPT Is a Knowledgeable but Inexperienced Solver: An Investigation of Commonsense Problem in Large Language Models (2024.lrec-main)
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| 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. |
ToolCPT: Improving Tool Utilization in LLM Agents via Continuous Pre-training (2026.findings-acl)
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| Challenge: | Current approaches to enhancing tool use for LLM-based agents focus on post-training fine-tuning or test-time context extension. |
| Approach: | They propose to enhance tool knowledge for LLM-based agents during continuous pre-training . they curate 5.1 million code artifacts from large-scale, high-quality code repositories . |
| Outcome: | The proposed model outperforms existing methods on out-of-distribution tools on multiple benchmarks. |
Reasoning Makes Good Annotators : An Automatic Task-specific Rules Distilling Framework for Low-resource Relation Extraction (2023.findings-emnlp)
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| Challenge: | Existing methods to extract knowledge from unlabeled data generate noise labels. |
| Approach: | They propose an automatic task-specific rules distilling framework to generate a logic rule from unlabeled data. |
| Outcome: | The proposed framework could power the labeling ability by discovering reliable model-labeled data. |
ProcessBench: Identifying Process Errors in Mathematical Reasoning (2025.acl-long)
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Chujie Zheng, Zhenru Zhang, Beichen Zhang, Runji Lin, Keming Lu, Bowen Yu, Dayiheng Liu, Jingren Zhou, Junyang Lin
| 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 . |
Re3Syn: A Dependency-Based Data Synthesis Framework for Long-Context Post-training (2025.acl-long)
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| Challenge: | Existing methods for constructing long-context data by concatenating short documents have overlooked a crucial characteristic of long-constituency data quality, semantic dependency. |
| Approach: | They propose a framework called Retrieval, Dependency Recognition, and Reorder for data synthesis which leverages semantic similarity to retrieve relevant documents and form several batches. |
| Outcome: | The proposed framework leverages semantic similarity to retrieve relevant documents and form several batches. |
Unlocking Multilingual Reasoning Capability of LLMs and LVLMs through Representation Engineering (2026.acl-long)
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Qiming Li, Xiaocheng Feng, Yixuan Ma, Ruihan Chen, Zihe Tong, Zekai Ye, Xiachong Feng, Libo Qin, Haoyu Ren, Kun Chen, Yunfei Lu, Dandan Tu, Bing Qin
| Challenge: | Existing approaches to enhance multilingual reasoning capabilities rely on costly multilingual training or employ prompting with external translation tools. |
| Approach: | They propose a training-free inference-time method to enhance multilingual reasoning capabilities via Representation Engineering without additional training data or tools. |
| Outcome: | The proposed method outperforms existing methods on four reasoning benchmarks in English and Thai and Swahili. |
Recipes for Sequential Pre-training of Multilingual Encoder and Seq2Seq Models (2023.findings-acl)
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| Challenge: | Pre-trained encoder-only and sequence-to-sequence models are computationally expensive. |
| Approach: | They propose a recipe to initialize one model from the other to improve pre-training efficiency. |
| Outcome: | The proposed method matches the performance of a from-scratch model with a multilingual encoder while reducing the total compute cost by 27%. |
Retaining Key Information under High Compression Ratios: Query-Guided Compressor for LLMs (2024.acl-long)
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| Challenge: | Existing methods to compress long contexts have degraded dramatically as compression ratios increase, sometimes even falling to the closed-book level. |
| Approach: | They propose a query-guided compression method that preserves key information within the compressed context. |
| Outcome: | The proposed method can consistently perform well even at high compression ratios, and offers significant benefits in terms of inference cost and throughput. |
RMTBench: Benchmarking LLMs Through Multi-Turn User-Centric Role-Playing (2025.findings-emnlp)
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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
| 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. |
Natural Language Generation for Effective Knowledge Distillation (D19-61)
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| 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. |
QACheck: A Demonstration System for Question-Guided Multi-Hop Fact-Checking (2023.emnlp-demo)
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| Challenge: | Existing fact-checking systems lack transparency in their decision-making process, making it difficult for users to comprehend their reasoning process. |
| Approach: | They propose a Question-guided Multi-hop Fact-Checking system which asks a series of questions critical for verifying a claim. |
| Outcome: | The proposed model provides a comprehensive report detailing its reasoning process, guided by a sequence of questions, answer pairs, and the source of evidence supporting each question. |
Merger-as-a-Stealer: Stealing Targeted PII from Aligned LLMs with Model Merging (2025.emnlp-main)
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| 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. |
A Large Scale Speech Sentiment Corpus (2020.lrec-1)
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| Challenge: | Existing corpus for sentiment analysis uses text inputs, but voice inputs are becoming more important as smart assistants and mobile voice control become more prevalent. |
| Approach: | They propose to extend the Switchboard-1 Telephone Speech Corpus by adding sentiment labels from 3 different human annotators for every transcript segment. |
| Outcome: | The proposed corpus contains 49500 labeled speech segments covering 140 hours of audio. |
OctoTools: A Multi-Agent Framework with Extensible Tools for Complex Reasoning (2026.acl-long)
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| Challenge: | Existing prompting methods for large language models (LLMs) are restricted to specialized domains, limited tool types, or require additional training data. |
| Approach: | They propose a training-free, user-friendly, and easily extensible multi-agent framework designed to tackle complex reasoning across diverse domains. |
| Outcome: | The proposed framework outperforms AutoGen, GPT-Functions, and LangChain by up to 10.6% when given the same set of tools. |
Efficient Classification of Long Documents via State-Space Models (2023.emnlp-main)
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| Challenge: | Existing transformer-based models can only process long documents with limited computational resources due to their quadratic computation time and space. |
| Approach: | They propose to use state-space models for long document classification tasks instead of using sparse or hierarchical structures to solve this problem. |
| Outcome: | The proposed model performs comparable to self-attention models while being 36% more efficient. |
BUMP: A Benchmark of Unfaithful Minimal Pairs for Meta-Evaluation of Faithfulness Metrics (2023.acl-long)
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Liang Ma, Shuyang Cao, Robert L Logan IV, Di Lu, Shihao Ran, Ke Zhang, Joel Tetreault, Alejandro Jaimes
| Challenge: | Existing benchmarks measure the correlation with human judgements of faithfulness on model-generated summaries, but they are insufficient for diagnosing whether metrics are consistent, effective on human-written texts, and sensitive to different error types. |
| Approach: | They propose to use unfaithful minimal pairs to measure the consistency of automatic faithfulness metrics by comparing human-written summary pairs with a dataset of 889 human-writing, minimally different summary pairs. |
| Outcome: | The proposed benchmarks show that the most discriminative metrics tend not to be the most consistent, and that the best performing metrics are sensitive to errors. |
RoleMRC: A Fine-Grained Composite Benchmark for Role-Playing and Instruction-Following (2025.findings-acl)
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| 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. |
Cross-Cultural Analysis of Human Values, Morals, and Biases in Folk Tales (2023.emnlp-main)
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| Challenge: | Existing studies on folk tales focus on European tales, ignoring large swaths of the world's diverse cultures. |
| Approach: | They compile a corpus of over 1,900 folk tales originating from 27 diverse cultures across six continents and employ lexicon-based correlation analyses to examine human values, morals, and gender biases. |
| Outcome: | The results show that folk tales are influenced by cultural norms and cultural values and are well-known for their morals and values. |
Zero-shot Event Detection Using a Textual Entailment Model as an Enhanced Annotator (2024.lrec-main)
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| Challenge: | Recent work proposed to use a pre-trained textual entailment model for event detection . but, those methods treated the TE model as a frozen annotator . |
| Approach: | They propose to use TE models to annotate large-scale unlabeled text and annotated data to fine-tune the TE model. |
| Outcome: | The proposed method outperforms baseline methods by 15% on the ACE05 dataset. |
Are Emergent Abilities in Large Language Models just In-Context Learning? (2024.acl-long)
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| Challenge: | Large language models have been claimed to acquire certain capabilities without having been specifically trained on them. |
| Approach: | They propose a theory that explains emergent abilities by taking into account their potential confounding factors and rigorously substantiate this theory through over 1000 experiments. |
| Outcome: | The proposed theory proves that emergent abilities are not truly emergental, but result from a combination of in-context learning, model memory, and linguistic knowledge. |
Logic: Long-form Outline Generation via Imitative and Critical Self-refinement (2025.findings-emnlp)
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| Challenge: | Existing methods for long-form outline generation have low knowledge density and lack detail . retrieval-augmented approaches struggle to maintain logical coherence across retrieved information . |
| Approach: | They propose a system that mimics human writers' refinement process by mimicking outlines through imitation and critical self-refinement. |
| Outcome: | The proposed system improves on the FreshWiki and WikiOutline datasets and establishes a coherent planning framework and structured knowledge base. |
GhostBERT: Generate More Features with Cheap Operations for BERT (2021.acl-long)
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| Challenge: | Existing studies show that some parameters in pre-trained language models can be pruned away without severe accuracy degradation. |
| Approach: | They propose a method which generates more features with very cheap operations from the remaining features and can be applied to unpruned BERT models to enhance their performance. |
| Outcome: | Empirical results on the GLUE benchmark on three backbone models (i.e., BERT, RoBERTa and ELECTRA) verify the efficacy of the proposed method. |
Which Sense Dominates Multisensory Semantic Understanding? A Brain Decoding Study (2024.lrec-main)
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| Challenge: | Decoding semantic meanings from brain activity is open to multisensory stimulation, as word meanings can be delivered by both auditory and visual inputs. |
| Approach: | They aim to develop a computational model to probing what information from the act of language understanding is represented in human brain. |
| Outcome: | The proposed model dissociates multisensory integration of word understanding into written text, spoken text and image perception respectively, exploring the decoding efficiency and reliability of unisensory information in the brain representation. |
TriageAgent: Towards Better Multi-Agents Collaborations for Large Language Model-Based Clinical Triage (2024.findings-emnlp)
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| Challenge: | escalation in emergency department patient visits poses challenges to efficient clinical management . Currently, hospitals rely on human experts to review clinical notes and determine case urgency . |
| Approach: | a team of researchers develop a multi-agent framework to enhance collaborative decision-making in clinical triage. |
| Outcome: | The proposed framework outperforms state-of-the-art LLM-based methods on three clinical triage test sets. |
NOVA: An Iterative Planning Framework for Enhancing Scientific Innovation with Large Language Models (2025.findings-acl)
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Xiang Hu, Hongyu Fu, Jinge Wang, Yifeng Wang, Zhikun Li, Renjun Xu, Yu Lu, Yaochu Jin, Lili Pan, Zhenzhong Lan
| Challenge: | Existing approaches to generate research ideas rely on retrieval or prompt engineering to generate ideas. |
| Approach: | They propose a method that uses iterative planning and search to boost creative potential of LLMs by integrating external knowledge with broader and deeper insights. |
| Outcome: | The proposed method outperforms the current state-of-the-art in generating 2.5 times more top-rated ideas based on 170 seed papers in a Swiss Tournament evaluation. |
AgentTuning: Enabling Generalized Agent Abilities for LLMs (2024.findings-acl)
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| Challenge: | Open large language models (LLMs) with great performance in various tasks are far inferior to commercial models such as ChatGPT and GPT-4 when acting as agents to tackle complex tasks in the real world. |
| Approach: | They propose a method to enhance the agent capabilities of LLMs while maintaining their general abilities. |
| Outcome: | The AgentLM-70B is comparable to GPT-3.5-turbo on unseen agent tasks, demonstrating generalized agent capabilities. |
GenPT: Beyond Self-Report for Reliable LLM Psychometrics via Generative Projective Testing (2026.acl-long)
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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. |
Benchmarking Vision-Language Models on Chinese Ancient Documents: From OCR to Knowledge Reasoning (2026.findings-acl)
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Haiyang Yu, Yuchuan Wu, Fan Shi, Jinghui Lu, Ke Niu, Xiaodong Ge, Minghan Zhuo, Jingqun Tang, Bin Li
| Challenge: | Existing document benchmarks focus on English printed texts or simplified Chinese . current vision-language models struggle with visual complexity and poor adaptability . |
| Approach: | They propose a benchmark to evaluate Chinese ancient documents' visual/linguistic complexity . ancient documents are valuable cultural heritage, but they face challenges in digitization and understanding . |
| Outcome: | the first benchmark for Chinese ancient documents evaluates VLMs from OCR to knowledge reasoning . ancient documents carry thousands of years of Chinese history and culture . traditional methods only scan images, while current models struggle with visual complexity . |
Alignment for Efficient Tool Calling of Large Language Models (2025.emnlp-main)
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| Challenge: | Recent advances in tool learning have enabled large language models to integrate external tools, enhancing their task performance by expanding their knowledge boundaries. |
| Approach: | They propose a framework that combines probabilistic knowledge boundary estimation with dynamic decision-making to allow LLMs to better assess when to invoke tools based on their confidence. |
| Outcome: | The proposed framework shows significant improvements in tool efficiency by reducing unnecessary tool usage. |
Always the Best Fit: Adaptive Domain Gap Filling from Causal Perspective for Few-Shot Relation Extraction (2023.findings-emnlp)
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Ge Bai, Chenji Lu, Jiaxiang Geng, Shilong Li, Yidong Shi, Xiyan Liu, Ying Liu, Zhang Zhang, Ruifang Liu
| Challenge: | Existing approaches to cross-domain relation extraction have been limited by domains . data bias between domains can be difficult to fill, especially in few-shot scenarios . |
| Approach: | They propose a framework to bridge the semantic gap caused by data bias between domains . they use syntactic structure, label distribution, and entities to calculate causal effects . |
| Outcome: | The proposed framework fills the domain gap and yields better results on the few-shot task. |
SARA: Salience-Aware Reinforced Adaptive Decoding for Large Language Models in Abstractive Summarization (2025.acl-long)
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| Challenge: | Existing decoding strategies neglect the explicit use of salient contextual information and rely on static hyperparameters to fix the balance between contextual and prior knowledge. |
| Approach: | They propose a salience-aware reinforced adaptive decoding (SARA) which incorporates salient contextual information and allows the model to determine reliance on source document's context, salient context, and model's prior knowledge based on pointwise mutual information. |
| Outcome: | The proposed model improves the quality and faithfulness of summaries across LLMs without modifying their weights. |
Rethinking Why Intermediate-Task Fine-Tuning Works (2021.findings-emnlp)
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| Challenge: | Pretrained language models can be fine-tuned on intermediate labeled-data tasks before fine- tuning the models on the target task of interest. |
| Approach: | They conduct extensive experiments to study the impact of different factors on STILT . they find that the improvement from an intermediate task could be orthogonal to it containing reasoning or other complex skills. |
| Outcome: | The proposed method improves the performance of pretrained language models on various target tasks. |
Continual Dialogue State Tracking via Reason-of-Select Distillation (2024.findings-acl)
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| Challenge: | Existing research on dialogue systems has focused on domain-specific offline systems lacking adaptation abilities. |
| Approach: | They propose a Reason-of-Select distillation method that enhances smaller models with a novel "meta-reasoning" capability. |
| Outcome: | Experiments show that the proposed method significantly improves the performance and generalization capabilities of existing models. |
WaterSeeker: Pioneering Efficient Detection of Watermarked Segments in Large Documents (2025.findings-naacl)
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| Challenge: | Existing methods focus on distinguishing fully watermarked text from non-watermarked text, overlooking real-world scenarios where LLMs generate only brief segments within longer documents. |
| Approach: | They propose a method to detect watermarked segments in large documents using an anomaly extraction method and a local traversal. |
| Outcome: | The proposed method achieves a superior balance between detection accuracy and computational efficiency. |
EXCEEDS: Extracting Complex Events via Nugget-based Grid Modeling in Scientific Domain (2026.acl-long)
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| Challenge: | Extensive event extraction research has been conducted in many domains, including news, finance, and biology. |
| Approach: | They propose an end-to-end scientific event extraction framework for encoding nuggets into a grid matrix and simplifying complex event extraction as a nuggot-based grid modeling task. |
| Outcome: | The proposed framework performs well in scientific domain, demonstrating state-of-the-art performance. |
XFUND: A Benchmark Dataset for Multilingual Visually Rich Form Understanding (2022.findings-acl)
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| Challenge: | Existing research on multimodal pre-training for visually rich document understanding tasks has focused on the English domain while neglecting the importance of multilingual generalization. |
| Approach: | They propose a multimodal pre-trained model for multilingual document understanding which bridges the language barriers for visually rich document understanding. |
| Outcome: | The proposed model outperforms existing cross-lingual pre-trained models on the XFUND dataset on visual document understanding tasks. |
ProgressLM: Towards Progress Reasoning in Vision-Language Models (2026.acl-long)
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| Challenge: | Existing models for task progress estimation lack long-horizon and dynamic reasoning . estimating how much of a task has been completed requires long-term reasoning based on partial information. |
| Approach: | They propose a benchmark for evaluating progress reasoning from a single observation . they instantiate a two-stage paradigm that combines episodic retrieval with mental simulation . |
| Outcome: | The proposed benchmark improves on 14 VLMs on a small scale and shows common failure patterns. |
Improve Dense Passage Retrieval with Entailment Tuning (2024.emnlp-main)
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| Challenge: | Existing methods for dense retrieval have demonstrated remarkable performance in IR tasks. |
| Approach: | They propose a method to improve the embedding of dense retrievers by using existence claim as a bridge. |
| Outcome: | The proposed method can be plugged into current dense retrieval methods and the results are published in the journal Nature. |
EmoHarbor: Evaluating Personalized Emotional Support by Simulating the User’s Internal World (2026.acl-long)
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| Challenge: | EmoHarbor is an evaluation framework that rewards generic empathetic responses but fails to assess whether the support is genuinely personalized to users’ unique psychological profiles and contextual needs. |
| Approach: | They propose an automated evaluation framework that adopts a User-as-a-Judge paradigm by simulating the user's inner world. |
| Outcome: | The proposed framework decomposes users' internal processes into three specialized roles and defines 10 evaluation dimensions of personalized support quality. |
Gradient-Guided Multi-Judge Prompt Optimization (2026.acl-long)
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ChenZhuo Zhao, Xinda Wang, Pu Zhao, Yue Huang, Junting Lu, Ziqian Liu, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang
| 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. |
Cross-lingual Structure Transfer for Zero-resource Event Extraction (2020.lrec-1)
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| Challenge: | Existing approaches for information extraction only use name tagging . Currently, most successful cross-lingual transfer learning methods are limited to sequence labeling . |
| Approach: | They propose a share-and-transfer framework to transfer graph structures across languages . they propose to convert sentences in any language to language-universal graph structures . |
| Outcome: | The proposed framework performs comparable to state-of-the-art models on three languages without annotations. |
LoRA-drop: Efficient LoRA Parameter Pruning based on Output Evaluation (2025.coling-main)
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| Challenge: | Low-Rank Adaptation (LoRA) is currently the most commonly used PEFT method for fine-tuning models with billions of parameters. |
| Approach: | They propose to use low-rank Adaptation to evaluate LoRA parameter features and then retain LoRA for important layers and the other layers share the same LoRA. |
| Outcome: | The proposed method achieves comparable performance to full fine-tuning and LoRA while retaining 50% of the LoRA parameters on average. |
Multi-Level Cross-Modal Alignment for Speech Relation Extraction (2024.emnlp-main)
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Liang Zhang, Zhen Yang, Biao Fu, Ziyao Lu, Liangying Shao, Shiyu Liu, Fandong Meng, Jie Zhou, Xiaoli Wang, Jinsong Su
| Challenge: | Existing studies use synthetic speech to train and evaluate SpeechRE models, hindering their development . modality gap issue limits performance of existing models, limiting future researches . |
| Approach: | They propose to use speech data to train and evaluate SpeechRE models by using real speech . they propose to train a cross-modal alignment model to bridge the modality gap . |
| Outcome: | The proposed model can train to bridge the modality gap between speech encoder and text decoder . the proposed model is based on two real SpeechRE datasets . |
Parameter-Efficient Mixture-of-Experts Architecture for Pre-trained Language Models (2022.coling-1)
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| Challenge: | Recent results show that the mix-of-experts architecture is parameter inefficient . large-scale pre-trained language models can achieve excellent performance in many NLP tasks. |
| Approach: | They propose to build a parameter-efficient mix-of-experts architecture by sharing information across experts. |
| Outcome: | The proposed architecture increases model capacity without increasing computation costs. |
RealBench: A Chinese Multi-image Understanding Benchmark Close to Real-world Scenarios (2025.findings-emnlp)
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Fei Zhao, Chengqiang Lu, Yufan Shen, Qimeng Wang, Yicheng Qian, Haoxin Zhang, Yan Gao, null Yiwu, Yao Hu, Zhen Wu, Shangyu Xing, Xinyu Dai
| Challenge: | RealBench is the first Chinese multimodal multi-image dataset . the dataset contains 9393 samples and 69910 images . |
| Approach: | They propose to create a Chinese multimodal multi-image dataset using 21 models . they use closed-source models that support multi-inputs as well as open-source visual and video models a . |
| Outcome: | The first Chinese multimodal multi-image dataset contains 9393 samples and 69910 images. |
Constructing Multilingual Code Search Dataset Using Neural Machine Translation (2023.acl-srw)
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| Challenge: | Existing datasets for code search are monolingual, but their query data are only in English. |
| Approach: | They construct a multilingual code search dataset in four natural and four programming languages using a neural machine translation model and apply back-translation data filtering to it. |
| Outcome: | The proposed model pre-trained with all natural and programming language data performs best under almost all settings. |
HetGCoT: Heterogeneous Graph-Enhanced Chain-of-Thought LLM Reasoning for Academic Question Answering (2025.findings-emnlp)
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| Challenge: | graph neural networks capture structured graph information, but lack integration at the reasoning level. |
| Approach: | They propose a framework that leverages graph structural information to reason interpretable academic QA results. |
| Outcome: | The proposed framework outperforms sota baselines on OpenAlex and DBLP datasets. |
Towards Interactivity and Interpretability: A Rationale-based Legal Judgment Prediction Framework (2022.emnlp-main)
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| Challenge: | Existing models lack interpretability due to the neglect of rationale in the prediction process. |
| Approach: | They propose a rationale-based legal judgment prediction framework that follows the judge's real trial logic and provides good interactivity and interpretability. |
| Outcome: | The proposed framework provides good interactivity and interpretability which enables practical use. |
STORM-BORN: A Challenging Mathematical Derivations Dataset Curated via a Human-in-the-Loop Multi-Agent Framework (2025.findings-acl)
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Wenhao Liu, Zhenyi Lu, Xinyu Hu, Jerry Zhang, Dailin Li, Jiacheng Cen, Huilin Cao, Haiteng Wang, Yuhan Li, Xie Kun, Dandan Li, Pei Zhang, Chengbo Zhang, Yuxiang Ren, Xiaohong Huang, Yan Ma
| Challenge: | Existing datasets suffer from outdated and insufficient challenging content, neglecting human-like reasoning, and limited reliability due to single-LLM generation. |
| Approach: | They propose a human-in-the-loop, multi-agent data generation framework that integrates reasoning-dense filters, multiagent collaboration, and human mathematicians’ evaluations to ensure the reliability and quality of the dataset. |
| Outcome: | The proposed framework improves accuracy and quality of the 2,000-synthesized datasets by integrating reasoning-dense filters, multi-agent collaboration, and human mathematicians’ evaluations. |
Compound AI Systems Optimization: A Survey of Methods, Challenges, and Future Directions (2025.emnlp-main)
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| Challenge: | Recent advances in large language models (LLMs) and AI systems have led to a paradigm shift in the design and optimization of complex workflows. |
| Approach: | They propose a systematic review of recent progress in optimizing compound AI systems . they formalize the notion of compound AI system optimization and classify existing methods along several key dimensions . |
| Outcome: | The proposed methods outperform existing methods in the field of compound AI and highlight open research challenges and future directions. |
GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media (2020.acl-main)
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| Challenge: | Existing methods to detect fake news on social media are based on textual features and advanced linguistic features. |
| Approach: | They propose a neural network-based model to detect fake news on social media . they use a short-text tweet and a sequence of retweets without text comments to predict whether the source tweet is fake or not. |
| Outcome: | The proposed model outperforms state-of-the-art methods by 16% on real tweet datasets and produces reasonable explanations. |
Contextual Distortion Reveals Constituency: Masked Language Models are Implicit Parsers (2023.acl-long)
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| Challenge: | a novel chart-based method for extracting parse trees from masked language models is proposed . a graph-based approach can be used to extract parser trees without training separate parsers . |
| Approach: | They propose a chart-based method for extracting parse trees from masked language models . they use a set of perturbations motivated by the linguistic concept of constituency tests to score each span . |
| Outcome: | The proposed method outperforms state-of-the-art methods on english with masked LMs and in multilingual settings. |
QaDialMoE: Question-answering Dialogue based Fact Verification with Mixture of Experts (2022.findings-emnlp)
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| Challenge: | Existing research on fact verification focuses on news, tables and Wikipedia passages. |
| Approach: | They propose a question-answering dialogue based fact verification with mixture of experts that exploits questions and evidence effectively in the verification process. |
| Outcome: | The proposed approach outperforms previous approaches on three benchmark datasets and achieves state-of-the-art results. |
Tell Me What You Don’t Know: Enhancing Refusal Capabilities of Role-Playing Agents via Representation Space Analysis and Editing (2025.findings-acl)
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Wenhao Liu, Siyu An, Junru Lu, Muling Wu, Tianlong Li, Xiaohua Wang, Changze Lv, Xiaoqing Zheng, Di Yin, Xing Sun, Xuanjing Huang
| Challenge: | Role-playing Agents (RPAs) struggle to recognize and respond to hard queries that conflict with their role-play knowledge. |
| Approach: | They propose a lightweight representation editing approach that conveniently shifts conflicting requests to the rejection region, thereby enhancing the model’s refusal accuracy. |
| Outcome: | The proposed model improves RPAs’ refusal ability of conflicting requests while maintaining their general role-playing capabilities. |
SLAM-Omni: Timbre-Controllable Voice Interaction System with Single-Stage Training (2025.findings-acl)
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Wenxi Chen, Ziyang Ma, Ruiqi Yan, Yuzhe Liang, Xiquan Li, Ruiyang Xu, Zhikang Niu, Yanqiao Zhu, Yifan Yang, Zhanxun Liu, Kai Yu, Yuxuan Hu, Jinyu Li, Yan Lu, Shujie Liu, Xie Chen
| Challenge: | a new spoken dialogue system with single-stage training is demonstrating its low latency and high quality . SLAM-Omni achieves zero-shot timbre control by modeling spoken language with semantic tokens . |
| Approach: | They propose a timbre-controllable, end-to-end voice interaction system with single-stage training. |
| Outcome: | The proposed system outperforms previous models on 4 GPUs with limited data. |
SynC-LLM: Generation of Large-Scale Synthetic Circuit Code with Hierarchical Language Models (2025.emnlp-main)
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| Challenge: | Recent years, AI-assisted integrated circuit design methods have shown great potential in boosting IC design efficiency. however, this emerging technique is limited by the serious scarcity of publicly accessible large-scale circuit design data, which are mostly private IPs owned by semiconductor companies. |
| Approach: | They propose a hierarchical framework that exploits LLM's ability to generate new large-scale synthetic digital circuits by learning sequential logic skeletons and annotating function descriptions. |
| Outcome: | The proposed framework generates large-scale synthetic circuits that are valid and fully functional, and can significantly improve AI models’ performance in multiple IC design tasks. |
Knowledge Graph Enhanced Neural Machine Translation via Multi-task Learning on Sub-entity Granularity (2020.coling-main)
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| Challenge: | Existing methods to integrate knowledge graph (KG) with neural machine translation (NMT) have two problems: knowledge under-utilization and granularity mismatch. |
| Approach: | They propose a multi-task learning method on sub-entity granularity to combine machine translation and knowledge reasoning tasks. |
| Outcome: | The proposed method significantly outperforms baseline models on translation tasks and handling the entities. |
LongLeader: A Comprehensive Leaderboard for Large Language Models in Long-context Scenarios (2025.naacl-long)
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Pei Chen, Hongye Jin, Cheng-Che Lee, Rulin Shao, Jingfeng Yang, Mingyu Zhao, Zhaoyu Zhang, Qin Lu, Kaiwen Men, Ning Xie, Huasheng Li, Bing Yin, Han Li, Lingyun Wang
| Challenge: | LongLeader aims to assess different LLMs' long-context comprehension abilities . long-constext comprehension is a key bottleneck for many use cases . |
| Approach: | They propose a leaderboard to assess different LLMs' long-context comprehension abilities . they offer open-source access to the benchmarks and maintain a dedicated website . |
| Outcome: | The proposed model assesses different LLMs on selected benchmarks and provides open-source access to the benchmarks. |
Unveiling the Generalization Power of Fine-Tuned Large Language Models (2024.naacl-long)
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| Challenge: | Large Language Models (LLMs) have demonstrated exceptional multitasking abilities, but the comprehensive effects of fine-tuning on the LLMs’ generalization ability are not fully understood. |
| Approach: | They conduct extensive experiments across five distinct language tasks on different datasets to investigate whether fine-tuning affects the generalization ability intrinsic to LLMs. |
| Outcome: | The proposed model can generalize to different domains and tasks by integrating the in-context learning strategy during fine-tuning on generation tasks. |
Parameter-Efficient Routed Fine-Tuning: Mixture-of-Experts Demands Mixture of Adaptation Modules (2026.findings-eacl)
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| Challenge: | Existing Parameter-Efficient Fine-Tuning (PEFT) strategies that focus on specialized experts are not effective for Mixture-of-Experts (MoE). |
| Approach: | They propose to integrate a dynamic routing mechanism among specialized experts in Mixture-of-Experts (MoE) . |
| Outcome: | Extensive experiments on commonsense and math reasoning tasks validate the performance and efficiency of the proposed routed approach. |
Where to Attack: A Dynamic Locator Model for Backdoor Attack in Text Classifications (2022.coling-1)
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| Challenge: | BackDoor Attack (BDA) study aims to train a poisoned model with clean data and some trigger-embedded instances to perform normally on normal inputs. |
| Approach: | They propose to train a poisoned model with clean and poisonest inputs . they propose to use triggers to predict those poisonets as target labels . |
| Outcome: | The proposed model can predict P2P dynamically without human intervention. |
Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimization (2024.acl-long)
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Wenqi Zhang, Ke Tang, Hai Wu, Mengna Wang, Yongliang Shen, Guiyang Hou, Zeqi Tan, Peng Li, Yueting Zhuang, Weiming Lu
| Challenge: | Large Language Models (LLMs) are designed as specific task solvers with sophisticated prompt engineering, but are inherently incapacitating to address complex dynamic scenarios. |
| Approach: | They propose an LLM-based agent with policy-level reflection and optimization that can learn from interactive experiences and progressively elevate its behavioral policy. |
| Outcome: | The proposed agent outperforms vanilla LLM and specialized models in blackjack and Texas hold’em. |
Event Extraction as Multi-turn Question Answering (2020.findings-emnlp)
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| Challenge: | Current approaches to event extraction fail to model rich interactions among event types and arguments of different roles. |
| Approach: | They propose a new paradigm that formulates event extraction as multi-turn question answering . they propose to use reading comprehension problems to extract triggers and arguments . |
| Outcome: | The proposed approach outperforms current state-of-the-art on argument extraction tasks . it makes full use of dependency among arguments and event types, and generalizes well . |
WordGame: Efficient & Effective LLM Jailbreak via Simultaneous Obfuscation in Query and Response (2025.findings-naacl)
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| 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. |
AskToAct: Enhancing LLMs Tool Use via Self-Correcting Clarification (2025.emnlp-main)
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Xuan Zhang, Yongliang Shen, Zhe Zheng, Linjuan Wu, Wenqi Zhang, Yuchen Yan, Qiuying Peng, Jun Wang, Weiming Lu
| Challenge: | Existing tools for ambiguous and incomplete queries are limited by manual construction and lack of error correction mechanisms during multi-turn clarification. |
| Approach: | They propose a framework that exploits the mapping between queries and their tool invocation solutions by removing key parameters from queries while retaining them as ground truth. |
| Outcome: | The proposed framework outperforms existing methods while maintaining high accuracy in tool invocation. |
Model Surgery: Modulating LLM’s Behavior Via Simple Parameter Editing (2025.naacl-long)
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| Challenge: | Current approaches for detoxification or preventing jailbreaking involve fine-tuning billions of parameters through gradient descent with substantial computational cost. |
| Approach: | They propose to use supervised fine-tuning and Reinforcement Learning from human feedback to modify LLMs' behavior by directly editing a small subset of parameters. |
| Outcome: | Experiments show that editing a small subset of parameters can modulate specific behaviors of LLMs, such as detoxification and resistance to jailbreak, with only inference-level computational resources. |
Multi-Agent Comedy Club: Investigating Community Discussion Effects on LLM Humor Generation (2026.findings-acl)
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| Challenge: | Existing studies on the use of multi-turn interaction and feedback for LLM writing focus on prompts and localized feedback. |
| Approach: | They build a controlled multi-agent sandbox that instantiates a small standup comedy community and allows it to manipu-late whether public reception is generated, logged, and fed back into later rounds. |
| Outcome: | The proposed model improves craft/clarity and social response with occasional increases in aggressive humor. |
A Set Prediction Network For Extractive Summarization (2023.findings-acl)
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| Challenge: | Recent approaches to extracting salient sentences from source document are naive and lack dependencies between sentences. |
| Approach: | They propose a set prediction network to detect redundancy relationship between sentences . they use a non-autoregressive decoder to predict sentences in parallel . |
| Outcome: | The proposed method outperforms previous state-of-the-art models on extracted summary datasets. |
Global Eye: Breaking the “Fixed Thinking Pattern” during the Instruction Expansion Process (2025.acl-long)
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| Challenge: | Existing methods focus on constructing multi-perspective prompts to expand instructions, overlooking the “Fixed Thinking Pattern” issue of Large Language Models. |
| Approach: | They propose a method that analyzes the statistical characteristics of newly generated instructions and updates the prompts after a fixed number of instruction expansions. |
| Outcome: | The proposed method surpasses open-source LLMs and GPT3.5 in several metrics. |
Two are Better than One: Joint Entity and Relation Extraction with Table-Sequence Encoders (2020.emnlp-main)
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| Challenge: | Named entity recognition and relation extraction are two important fundamental problems. |
| Approach: | They propose to design two separate encoders to capture two different types of information in the representation learning process. |
| Outcome: | The proposed methods show significant improvements on standard datasets. |
A Fast and High-quality Text-to-Speech Method with Compressed Auxiliary Corpus and Limited Target Speaker Corpus (2024.lrec-main)
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| Challenge: | Existing methods to generate high-quality speech with limited target speaker corpus require extensive training data. |
| Approach: | They propose an auxiliary corpus compression algorithm that reduces the training cost while the naturalness of synthesized speech is not significantly degraded. |
| Outcome: | The proposed method significantly reduces training costs while maintaining the naturalness of synthesized speech. |
Improving Generalization of Pre-trained Language Models via Stochastic Weight Averaging (2022.findings-emnlp)
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| Challenge: | Recent studies show that the flatness of the local minimum correlates well with better generalization. |
| Approach: | They propose to use a method encouraging convergence to a flatter minimum to fine-tune PLMs. |
| Outcome: | The proposed method outperforms state-of-the-art methods on NLP tasks without extra computation cost. |
Read As Human: Compressing Context via Parallelizable Close Reading and Skimming (2026.acl-long)
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Jiwei Tang, Shilei Liu, Zhicheng Zhang, Qingsong Lv, Runsong Zhao, Tingwei Lu, Langming Liu, Haibin Chen, Yujin Yuan, Hai-Tao Zheng, Wenbo Su, Bo Zheng
| Challenge: | Existing task-aware methods require loading the entire input sequence at once for compression, which suffer from computational inefficiency. |
| Approach: | They propose a framework that adopts an adaptive hybrid reading strategy to reduce computational inefficiency and redundant information in long-context scenarios. |
| Outcome: | Experiments show that RAM outperforms baselines on multiple question answering and summarization benchmarks while delivering up to a 12x speedup on long inputs. |
Constructing Interpretive Spatio-Temporal Features for Multi-Turn Responses Selection (P19-1)
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| Challenge: | Existing models for response selection do not perform well when there are many candidate responses. |
| Approach: | They propose a Spatio-Temporal Matching network (STM) for response selection . they use soft alignment to obtain local relevance between context and response . |
| Outcome: | The proposed model significantly outperforms the state-of-the-art model on two large-scale multi-turn response selection tasks. |
Investigating Dictionary Expansion for Video-based Sign Language Dictionaries (2025.findings-emnlp)
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| Challenge: | Currently, most dictionary retrieval methods only work with fixed vocabularies, and it is unclear how they might support dictionary expansion without retraining. |
| Approach: | They propose to use a representation-based method to explore the feasibility of dictionary expansion for sign language dictionaries. |
| Outcome: | The proposed method improves sign language dictionaries by varying number of signs added and amount of data for newly added signs. |
Mulan: A Multi-Level Alignment Model for Video Question Answering (2023.findings-emnlp)
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| Challenge: | Existing methods focus on visual-language alignment at the video level, but they do not account for fine-grained semantic interaction between video and text. |
| Approach: | They propose a multi-level Alignment Model for Video Question Answering that establishes alignment between visual and textual modalities at the object-level, frame-level and video-level. |
| Outcome: | The proposed model outperforms state-of-the-art methods even with a small amount of extra visual-language pre-training data and a reduced number of trainable parameters. |
Unifying Language Agent Algorithms with Graph-based Orchestration Engine for Reproducible Agent Research (2025.acl-demo)
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Qianqian Zhang, Jiajia Liao, Heting Ying, Yibo Ma, Haozhan Shen, Jingcheng Li, Peng Liu, Lu Zhang, Chunxin Fang, Kyusong Lee, Ruochen Xu, Tiancheng Zhao
| Challenge: | Language agents powered by large language models (LLMs) have demonstrated remarkable capabilities in understanding, reasoning, and executing complex tasks. |
| Approach: | They propose a flexible framework that addresses engineering overhead and insufficient evaluation frameworks for fair comparison. |
| Outcome: | The proposed framework simplifies language agent development and establishes a foundation for reproducible agent research. |
Evaluating Saliency Explanations in NLP by Crowdsourcing (2024.lrec-main)
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| 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 . |
CoVerRL: Breaking the Consensus Trap in Label-Free Reasoning via Generator-Verifier Co-Evolution (2026.acl-long)
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Teng Pan, Yuchen Yan, Zixuan Wang, Ruiqing Zhang, Guiyang Hou, Wenqi Zhang, Weiming Lu, Jun Xiao, Yongliang Shen
| Challenge: | Label-free reinforcement learning enables large language models to improve reasoning capabilities . but as training maximizes self-consistency, output diversity collapses, authors say . authors propose a framework where a single model alternates between generator and verifier roles . |
| Approach: | They propose a framework where a model alternates between generator and verifier roles, bootstrapping each other. |
| Outcome: | Experiments show that CoVerRL outperforms label-free baselines on reasoning benchmarks . the framework can be used to improve reasoning abilities without ground-truth supervision . |
IntactKV: Improving Large Language Model Quantization by Keeping Pivot Tokens Intact (2024.findings-acl)
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| 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. |
Multi-Agent Language Learning: Symbolic Mapping (2023.findings-acl)
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| Challenge: | Recent work has focused on the emergence of language in cooperative tasks where neural network agents learn a communication protocol from scratch to solve problems together. |
| Approach: | They propose a task transfer method and symbolic mapping architecture to help agents learn a compositional and symmetric language in dialog games. |
| Outcome: | The proposed method can help agents learn a compositional and symmetric language in complex settings like dialog games and the proposed architecture promotes vocabulary expansion. |
Structured Episodic Event Memory (2026.acl-long)
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| Challenge: | Current approaches to memory in Large Language Models (LLMs) rely on static Retrieval-Augmented Generation (RAG) this lacks the cognitive organization necessary to model the dynamic and associative nature of long-term interaction. |
| Approach: | They propose a hierarchical framework that transforms interaction streams into structured Episodic Event Frames (EEFs) anchored by precise provenance pointers. |
| Outcome: | The proposed framework outperforms baseline approaches on LoCoMo and LongMemEval benchmarks. |
FNSCC: Fuzzy Neighborhood-Aware Self-Supervised Contrastive Clustering for Short Text (2025.findings-emnlp)
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| Challenge: | Short texts pose significant challenges for clustering due to semantic sparsity, limited context and fuzzy category boundaries. |
| Approach: | proposed framework incorporates neighborhood information at instance and cluster levels . a cluster-level framework introduces fuzzy neighborhood-aware weighting . |
| Outcome: | The proposed framework outperforms state-of-the-art models on short texts . it excludes neighbors from negative sample set to enhance inter-cluster separability . |
Teaching According to Talents! Instruction Tuning LLMs with Competence-Aware Curriculum Learning (2025.findings-emnlp)
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Yangning Li, Tingwei Lu, Yinghui Li, Yankai Chen, Wei-Chieh Huang, Wenhao Jiang, Hui Wang, Hai-Tao Zheng, Philip S. Yu
| Challenge: | Efficient instruction tuning aims to enhance the ultimate performance of large language models (LLMs) current methods suffer from the curriculum rigidity, resulting in a fixed and potentially sub-optimal learning trajectory. |
| Approach: | a framework for efficient instruction tuning is proposed to address the issue of curriculum rigidity . current methods rely on static heuristic difficulty metrics and fail to adapt to evolving capabilities . |
| Outcome: | Efficient instruction tuning aims to enhance the ultimate performance of large language models . current methods suffer from the curriculum rigidity, resulting in a fixed learning trajectory . |
Probability-Consistent Preference Optimization for Enhanced LLM Reasoning (2025.findings-acl)
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Yunqiao Yang, Houxing Ren, Zimu Lu, Ke Wang, Weikang Shi, Aojun Zhou, Junting Pan, Mingjie Zhan, Hongsheng Li
| Challenge: | Recent advances in preference optimization have demonstrated significant potential for improving mathematical reasoning capabilities in large language models. |
| Approach: | They propose a framework that establishes two quantitative metrics for preference selection: surface-level answer correctness and intrinsic token-level probability consistency. |
| Outcome: | The proposed framework outperforms existing outcome-only criterion approaches across a diverse range of LLMs and benchmarks. |
Theory-optimal Quantization Based on Flatness (2026.acl-long)
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| Challenge: | Recent approaches to quantization of Large Language Models (LLMs) have been widely adopted due to activation outliers, which degrade model performance especially at lower bit precision. |
| Approach: | They propose a new metric for quantization that strategically distributes outlier magnitudes across matrix dimensions via optimized diagonal operations. |
| Outcome: | The proposed framework achieves less than 1% accuracy drop in W4A4 quantization on the LLaMA-3-8B model and reduces the performance gap by 39.1% on the more challenging W2A4KV16 model. |
LLM-based Rumor Detection via Influence Guided Sample Selection and Game-based Perspective Analysis (2025.acl-long)
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Zhiliang Tian, Jingyuan Huang, Zejiang He, Zhen Huang, Menglong Lu, Linbo Qiao, Songzhu Mei, Yijie Wang, Dongsheng Li
| Challenge: | Existing methods for rumor detection on social media are limited by limited modeling capacity and insufficient training corpora. |
| Approach: | They propose an SFT-based rumor detection model with Influence guided Sample selection and Game-based multi-perspective analysis to address these issues. |
| Outcome: | The proposed model outperforms existing SOTA on three datasets. |
Anti-Length Shift: Dynamic Outlier Truncation for Training Efficient Reasoning Models (2026.acl-long)
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Wei Wu, Liyi Chen, Congxi Xiao, Tianfu Wang, Qimeng Wang, Chengqiang Lu, Yan Gao, null Yiwu, Yao Hu, Hui Xiong
| Challenge: | Existing efficient reasoning methods rely on explicit length penalties for excessive verbosity on simple queries. |
| Approach: | They propose a training-time intervention that selectively suppresses redundant tokens . they find length shift occurs when models generate unnecessary reasoning on trivial inputs - a phenomenon that is often unexplored . |
| Outcome: | The proposed method reduces inference token usage by 78% while increasing accuracy compared to the initial policy and surpasses state-of-the-art efficient reasoning methods. |
Towards Table-to-Text Generation with Pretrained Language Model: A Table Structure Understanding and Text Deliberating Approach (2022.emnlp-main)
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| Challenge: | Currently, the generalization issues hinder the applicability of neural table-to-text models due to the limited source tables. |
| Approach: | They propose a table-structureaware text generation model with pretrained language model and propose TASD to bridge the gap between the structured table and text input. |
| Outcome: | The proposed model bridges the gap between the structured table and text input and generates accurate and fluent descriptive texts on two public datasets. |
FFN-SkipLLM: A Hidden Gem for Autoregressive Decoding with Adaptive Feed Forward Skipping (2024.emnlp-main)
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| Challenge: | Autoregressive Large Language Models (LLMs) are omnipresent but typically come with a substantial model size. |
| Approach: | They propose a novel fine-grained skip strategy for autoregressive large language models . they observe the saturation of computationally expensive feed-forward blocks of LLMs . |
| Outcome: | The proposed method can skip 25-30% of FFN blocks with marginal change in performance on knowledge-intensive generation tasks. |
OmniCode: A Benchmark for Evaluating Software Development Agents (2026.findings-acl)
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Atharv Sonwane, Eng-Shen Tu, Wei-Chung Lu, Claas Beger, Carter Larsen, Debjit Dhar, Simon Alford, Rachel Chen, Ronit Pattanayak, Tuan Anh Dang, Guohao Chen, Gloria Geng, Kevin Ellis, Saikat Dutta
| Challenge: | popular coding benchmarks focus on narrowly scoped tasks such as competition programming and patch generation. |
| Approach: | They propose a software engineering benchmark that aims to provide a broader set of tasks beyond code or patch generation. |
| Outcome: | The proposed framework performs well on bug fixing for Python, test generation, code review fixing, and style fixing with popular agent frameworks such as SWE-Agent. |
Line Graph Enhanced AMR-to-Text Generation with Mix-Order Graph Attention Networks (2020.acl-main)
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| Challenge: | Existing graph-to-sequence approaches use graph neural networks as encoders, but they lack the structure information needed to translate AMR into the graph-based data. |
| Approach: | They propose a graph-to-sequence task which aims to recover natural language from Abstract Meaning Representations (AMR) they adopt graph attention networks with higher-order neighborhood information to explore the edge relations in AMR graphs. |
| Outcome: | The proposed framework achieves state-of-the-art performance on English AMR benchmark datasets and is able to translate the AMR semantics into the natural language. |
System Prompt Hijacking via Permutation Triggers in LLM Supply Chains (2025.findings-acl)
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| Challenge: | Large language models (LLMs) are rapidly transforming the landscape of artificial intelligence due to the substantial resources required for training. |
| Approach: | They propose a post-deployment attack that bypasses system prompts to compromise models . they introduce Precise Activation Guarding and Unit Deviation Sampling to protect against attack . |
| Outcome: | The proposed attack bypasses system prompts, enabling unrestricted model outputs and safety violations. |
Across Programming Language Silos: A Study on Cross-Lingual Retrieval-Augmented Code Generation (2026.findings-acl)
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Qiming Zhu, Jialun Cao, Xuanang Chen, Weili Zhang, Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun, Shing-Chi Cheung
| 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. |
AdapterShare: Task Correlation Modeling with Adapter Differentiation (2022.emnlp-main)
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| Challenge: | AdapterShare is an adapter differentiation method to explicitly model the task correlation among multiple tasks. |
| Approach: | They propose an adapter differentiation method to explicitly model the task correlation among multiple tasks. |
| Outcome: | The proposed method achieves 1.90 points improvement on five dialogue understanding tasks and 2.33 points gain on NLU tasks. |
An Unsupervised Method for Building Sentence Simplification Corpora in Multiple Languages (2021.findings-emnlp)
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| Challenge: | Existing methods to build parallel sentence simplification corpora are limited . SS is used to rephrase sentences into simpler forms for those with cognitive disabilities . |
| Approach: | They propose to build SS corpora from large-scale bilingual translation corpors using a parallel approach. |
| Outcome: | The proposed method outperforms the existing methods on WikiLarge and achieves state-of-the-art results. |
CLAIM: Mitigating Multilingual Object Hallucination in Large Vision-Language Models with Cross-Lingual Attention Intervention (2025.acl-long)
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Zekai Ye, Qiming Li, Xiaocheng Feng, Libo Qin, Yichong Huang, Baohang Li, Kui Jiang, Yang Xiang, Zhirui Zhang, Yunfei Lu, Duyu Tang, Dandan Tu, Bing Qin
| Challenge: | Large Vision-Language Models (LVLMs) have impressive multimodal abilities but remain prone to multilingual object hallucination. |
| Approach: | They propose a cross-lingual attention intervention method to mitigate multilingual object hallucination in LVLMs by aligning attention patterns. |
| Outcome: | The proposed method improves 13.56% (up to 30%) on the POPE and 21.75% on the hallucination subsets across languages. |
SpanMlt: A Span-based Multi-Task Learning Framework for Pair-wise Aspect and Opinion Terms Extraction (2020.acl-main)
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| Challenge: | Aspect terms and opinion terms are key problems of fine-grained aspect-based sentiment analysis. |
| Approach: | They propose a method to extract aspect and opinion terms as pairs from a sentence . they use shared spans to extract the terms under supervision of span boundaries . |
| Outcome: | The proposed method outperforms state-of-the-art methods on both aspects and opinion terms extraction tasks. |
Modeling Content Importance for Summarization with Pre-trained Language Models (2020.emnlp-main)
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| Challenge: | Existing studies on content importance do not consider semantics and context when evaluating importance. |
| Approach: | They apply information theory to pre-trained language models to define the concept of importance from the perspective of information amount. |
| Outcome: | Experiments on CNN/Daily Mail and New York Times show that the proposed model can model the importance of content better than previous methods based on F1 and ROUGE scores. |
Multi-hop Evidence Retrieval for Cross-document Relation Extraction (2023.findings-acl)
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| Challenge: | Relation Extraction (RE) is a task that seeks to identify the relation of entities described according to some context. |
| Approach: | They propose a multi-hop evidence retrieval method based on evidence path mining and ranking to support cross-document relation extraction. |
| Outcome: | The proposed method acquires cross-document evidence and boosts performance in both closed and open environments. |
LipoAgent: Coordinating Fine-Tuned LLM Agents for Safer Lipid Design (2026.findings-acl)
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| Challenge: | Lipid nanoparticles (LNPs) are among the most clinically mature platforms for nucleic acid delivery, yet designing lipids that are effective and biologically safe remains a major bottleneck. |
| Approach: | They propose a safety-aware multi-agent LLM framework for lipid discovery that enforces toxicity as a prerequisite for efficiency prediction. |
| Outcome: | The proposed framework achieves an average improvement in mRNA transfection efficiency prediction across multiple foundation models. |
FlashAudio: Rectified Flow for Fast and High-Fidelity Text-to-Audio Generation (2025.acl-long)
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| Challenge: | Recent advances in latent diffusion models (LDMs) have markedly enhanced text-to-audio generation, yet their iterative sampling processes impose substantial computational demands, limiting practical deployment. |
| Approach: | They propose to learn straight flow for fast simulation by using flashAudio with rectified flows and immiscible flow to minimize the total distance of data-noise pairs in a batch vias assignment. |
| Outcome: | The proposed method can learn straight flow for fast simulations and reduce noise distribution. |
TRIP: Accelerating Document-level Multilingual Pre-training via Triangular Document-level Pre-training on Parallel Data Triplets (2023.findings-emnlp)
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Hongyuan Lu, Haoyang Huang, Shuming Ma, Dongdong Zhang, Wai Lam, Zhaochuan Gao, Anthony Aue, Arul Menezes, Furu Wei
| Challenge: | Existing approaches to multilingual sequence-to-sequence pre-training rely on monolingual corpora and sometimes synthetic document-level bilingual corporata. |
| Approach: | They propose to leverage document-level trilingual parallel corpora to improve sequence-to-sequence multilingual pre-training by using a novel method called Grafting. |
| Outcome: | The proposed method achieves strong state-of-the-art (SOTA) scores on three multilingual document-level machine translation benchmarks and one cross-lingual abstractive summarization benchmark. |
Document-level Biomedical Relation Extraction Based on Multi-Dimensional Fusion Information and Multi-Granularity Logical Reasoning (2022.coling-1)
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| Challenge: | Existing models with reasoning are single-granularity based on one element information, ignoring complementary fact of different granularities. |
| Approach: | They propose a document-level biomedical relation extraction model called FILR . it uses multi-dimensional information fusion and multi-granularity logic to obtain rich inferences . |
| Outcome: | The proposed model extracts all relation facts from biomedical documents . it is based on multi-dimensional information fusion and multi-granularity logic reasoning . the proposed model achieves state-of-the-art performance on two widely used biomedically corpora . |
CSPO: Alleviating Reward Ambiguity for Structured Table-to-LaTeX Generation (2026.acl-long)
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| Challenge: | Tables contain rich structured information, but when stored as images their contents remain "locked" within pixels. |
| Approach: | They propose a framework that disentangles optimization across LaTeX tables components . CSPO assigns component-specific rewards and backpropagates each signal through tokens . |
| Outcome: | The proposed framework disentangles optimization across LaTeX tables components—structure, style, and content. |
VQAGuider: Guiding Multimodal Large Language Models to Answer Complex Video Questions (2025.acl-long)
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| Challenge: | Multimodal large language models (MLLMs) can grasp the intention of a question and decomposing it to a series of visual recognition sub-tasks to find out the answer with the help of an agent. |
| Approach: | They propose a framework for multimodal large language models to grasp the intention of a question and decompose it into a series of visual recognition sub-tasks to find out the answer. |
| Outcome: | The proposed framework improves the accuracy of complex video-related questions by 29.6% and 17.2% on CVQA and the existing VQA datasets. |
Sample Efficient Alignment Learning With Episodic Control (2025.findings-emnlp)
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| Challenge: | Existing parametric methods for aligning large language models with task objectives are limited. |
| Approach: | They propose a non-parametric framework that aligns large language models with task objectives . they use a key-value memory to store associations between generated text and its corresponding values . |
| Outcome: | The proposed framework outperforms state-of-the-art baselines on harmless, helpful, and summarization tasks. |
CLUE: A Chinese Language Understanding Evaluation Benchmark (2020.coling-main)
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Liang Xu, Hai Hu, Xuanwei Zhang, Lu Li, Chenjie Cao, Yudong Li, Yechen Xu, Kai Sun, Dian Yu, Cong Yu, Yin Tian, Qianqian Dong, Weitang Liu, Bo Shi, Yiming Cui, Junyi Li, Jun Zeng, Rongzhao Wang, Weijian Xie, Yanting Li, Yina Patterson, Zuoyu Tian, Yiwen Zhang, He Zhou, Shaoweihua Liu, Zhe Zhao, Qipeng Zhao, Cong Yue, Xinrui Zhang, Zhengliang Yang, Kyle Richardson, Zhenzhong Lan
| Challenge: | Existing language evaluation benchmarks for English are limited to English . lack of such benchmarks makes it difficult to replicate success in other languages . |
| Approach: | They introduce a large-scale Chinese language understanding evaluation benchmark . the benchmark uses a set of current state-of-the-art pre-trained Chinese models . |
| Outcome: | The first large-scale Chinese Language Understanding Evaluation (CLUE) benchmark is released . the benchmark evaluates models across a wide range of tasks on original Chinese text . existing language evaluation benchmarks are mostly limited to English . |
On-Policy Self-Alignment with Fine-grained Knowledge Feedback for Hallucination Mitigation (2025.findings-acl)
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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
| 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. |
RORA: Robust Free-Text Rationale Evaluation (2024.acl-long)
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| Challenge: | Existing metrics rely on degree to which rationale supports a label, but they fail to evaluate rationales that inadvertently leak the label. |
| Approach: | They propose a RObust free-text RAtionale evaluation against label leakage that quantifies the new information supplied by a rationale to justify the label. |
| Outcome: | The proposed evaluation outperforms existing methods in evaluating human-written, synthetic, or model-generated rationales, particularly demonstrating robustness against label leakage. |
Unknown Intent Detection Using Gaussian Mixture Model with an Application to Zero-shot Intent Classification (2020.acl-main)
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| Challenge: | Existing methods for unknown intent detection are limited by prior knowledge of class labels. |
| Approach: | They propose to use a Gaussian mixture model to model utterance embeddings with a distribution and inject dynamic class semantic information into Gausssian means. |
| Outcome: | The proposed model performs well on three real task-oriented dialogue datasets in two languages. |
Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning Architecture (2023.findings-emnlp)
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Bingsheng Yao, Ishan Jindal, Lucian Popa, Yannis Katsis, Sayan Ghosh, Lihong He, Yuxuan Lu, Shashank Srivastava, Yunyao Li, James Hendler, Dakuo Wang
| Challenge: | Existing low-resource learning techniques focus on label annotation while neglecting the natural language explanation of a data point. |
| Approach: | They propose a novel architecture that leverages an explanation-generation model to produce explanations guided by human explanations and a prediction model that utilizes generated explanations toward prediction faithfully. |
| Outcome: | The proposed architecture produces explanations guided by human explanations, a prediction model that utilizes generated explanations toward prediction faithfully, and a data diversity-based AL sampling strategy that benefits from the explanation annotations. |
DIALKI: Knowledge Identification in Conversational Systems through Dialogue-Document Contextualization (2021.emnlp-main)
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| Challenge: | Existing knowledge grounding models focus on locating knowledge in document contexts that are relevant to the conversation. |
| Approach: | They propose a knowledge identification model that leverages document structure to provide dialogue-contextualized passage encodings and better locate knowledge relevant to the conversation. |
| Outcome: | The proposed model can be applied to document-grounded conversational datasets and shows generalization to unseen documents and long dialogue contexts. |
Code Execution with Pre-trained Language Models (2023.findings-acl)
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Chenxiao Liu, Shuai Lu, Weizhu Chen, Daxin Jiang, Alexey Svyatkovskiy, Shengyu Fu, Neel Sundaresan, Nan Duan
| Challenge: | Pre-trained code intelligence models ignore the execution trace and only rely on source code and syntactic structures to understand code execution. |
| Approach: | They develop a mutation-based data augmentation technique to create a Python dataset and task for code execution that challenges existing models. |
| Outcome: | The proposed model outperforms existing models on code execution and shows its potential for zero-shot code-to-code search and text-to code generation. |
UNComp: Can Matrix Entropy Uncover Sparsity? — A Compressor Design from an Uncertainty-Aware Perspective (2025.emnlp-main)
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Jing Xiong, Jianghan Shen, Fanghua Ye, Chaofan Tao, Zhongwei Wan, Jianqiao Lu, Xun Wu, Chuanyang Zheng, Zhijiang Guo, Min Yang, Lingpeng Kong, Ngai Wong
| Challenge: | Deploying large language models (LLMs) for long-context inference remains challenging due to their substantial memory and computational demands. |
| Approach: | They propose an uncertainty-aware framework that leverages truncated matrix entropy to identify areas of low information content. |
| Outcome: | The proposed framework reduces the KV cache size to 4.74% of the original and achieves a 6% speedup. |
LLaMAX: Scaling Linguistic Horizons of LLM by Enhancing Translation Capabilities Beyond 100 Languages (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) exhibit exceptional translation capabilities in high-resource language tasks, yet their effectiveness in low-resourced languages is suboptimal. |
| Approach: | They conduct extensive multilingual continual pre-training on the LLaMA series models and develop LLiMAX for translation support across more than 100 languages. |
| Outcome: | The proposed model achieves higher translation performance than existing open-source models and performs on-par with specialized translation model on the Flores-101 benchmark. |
Trajectory2Task: Training Robust Tool-Calling Agents with Synthesized Yet Verifiable Data for Complex User Intents (2026.acl-long)
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Ziyi Wang, Yuxuan Lu, Yimeng Zhang, Pei Chen, Ziwei Dong, Jing Huang, Jiri Gesi, Xianfeng Tang, Chen Luo, Qun Liu, Yisi Sang, Hanqing Lu, Manling Li, Jin Lai, Dakuo Wang
| Challenge: | Tool-calling agents are increasingly deployed in real-world customer-facing workflows . but most studies on tool-callers focus on idealized settings with general, fixed, and well-specified tasks. |
| Approach: | They propose a tool-calling agent-based data pipeline that converts trajectories into user-facing tasks with controlled intent adaptations. |
| Outcome: | The proposed pipeline can be used to study tool use under three scenarios. |
STATE ToxiCN: A Benchmark for Span-level Target-Aware Toxicity Extraction in Chinese Hate Speech Detection (2025.findings-acl)
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| 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. |
Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity (2022.acl-long)
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| Challenge: | Large pretrained language models can generate text classification results that match fully supervised models. |
| Approach: | They propose to use a few sample training to determine which permutations are performant . they use generative language models to construct an artificial development set . |
| Outcome: | The proposed model outperforms fully-supervised models in eleven text classification tasks. |
A Bounding Box is Worth One Token - Interleaving Layout and Text in a Large Language Model for Document Understanding (2025.findings-acl)
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Jinghui Lu, Haiyang Yu, Yanjie Wang, Yongjie Ye, Jingqun Tang, Ziwei Yang, Binghong Wu, Qi Liu, Hao Feng, Han Wang, Hao Liu, Can Huang
| Challenge: | Existing methods for integrating spatial layouts with text have limitations . existing methods produce overly long text sequences or lack autoregressive traits of LLMs . |
| Approach: | They introduce Interleaving Layout and Text in a Large Language Model (LayTextLLM) they use OCR-derived text and spatial layouts to integrate with LLMs for document understanding . |
| Outcome: | The proposed model shows an increase in performance in KIE and VQA tasks. |
Word Sense Disambiguation with Knowledge-Enhanced and Local Self-Attention-based Extractive Sense Comprehension (2022.coling-1)
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| Challenge: | Word sense disambiguation (WSD) is one of the most challenging tasks in natural language processing. |
| Approach: | They propose a method to extract the right sense from a sentence context . they propose to incorporate additional examples and definitions of related senses in WordNet . |
| Outcome: | The proposed method achieves better performance than baseline models on public benchmark datasets. |
WarriorCoder: Learning from Expert Battles to Augment Code Large Language Models (2025.acl-long)
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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
| 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 . |
LLMs Learn Task Heuristics from Demonstrations: A Heuristic-Driven Prompting Strategy for Document-Level Event Argument Extraction (2024.acl-long)
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| Challenge: | In-context learning (ICL) is an emerging ability of large-scale labeled data for document-level event argument extraction (EAE). |
| Approach: | They propose an explicit heuristic-driven demonstration construction approach that emphasizes task heurs in document-level event argument extraction tasks. |
| Outcome: | The proposed method outperforms existing prompting methods and few-shot supervised learning methods on document-level EAE datasets. |
Recurrent Knowledge Identification and Fusion for Language Model Continual Learning (2025.acl-long)
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Yujie Feng, Xujia Wang, Zexin Lu, Shenghong Fu, Guangyuan Shi, Yongxin Xu, Yasha Wang, Philip S. Yu, Xu Chu, Xiao-Ming Wu
| Challenge: | Continual learning (CL) is crucial for large language models without costly retraining. |
| Approach: | They propose a framework for recurrent knowledge identification and fusion that enables dynamic estimation of parameter importance distributions to enhance knowledge transfer. |
| Outcome: | The proposed framework mitigates catastrophic forgetting and enhances knowledge transfer. |
Threshold Filtering Packing for Supervised Fine-Tuning: Training Related Samples within Packs (2025.naacl-long)
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| Challenge: | Randomly concatenating data points can lead to cross-contamination due to the significant difference in their subject matter. |
| Approach: | They propose a method that randomly concatenates data of varying lengths until reaching the designed maximum length to optimize context length and reduce padding. |
| Outcome: | The proposed method significantly improves performance on GSM8K and HumanEval, and also improves fairness and accuracy by 15%. |
Dependency-based Hybrid Trees for Semantic Parsing (D18-1)
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| Challenge: | Existing models for semantic parsing focus on structure-based models, but none deal with dependency information. |
| Approach: | They propose a dependency-based hybrid tree model which converts natural language utterances into machine interpretable meaning representations. |
| Outcome: | The proposed model achieves state-of-the-art performance across eight languages and is highly tractable inferenceable. |
LatticeGen: Hiding Generated Text in a Lattice for Privacy-Aware Large Language Model Generation on Cloud (2024.findings-naacl)
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Mengke Zhang, Tianxing He, Tianle Wang, Lu Mi, Niloofar Mireshghallah, Binyi Chen, Hao Wang, Yulia Tsvetkov
| Challenge: | Currently, the server controls the generated text, but users can't keep it private . prompted generation is a common interaction paradigm for large language models on cloud . |
| Approach: | They propose a protocol where the server handles most of the computation while the client controls the sampling operation. |
| Outcome: | The proposed protocol protects both prompt and generation under strong attacks. |
Too Correct to Learn: Reinforcement Learning on Saturated Reasoning Data (2026.acl-short)
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| Challenge: | Strong base models saturate benchmarks, resulting in weaker performance, a paradox . a new approach to Reinforcement Learning (RL) is needed to improve performance . |
| Approach: | They propose a method that uses constrained uniform top-k sampling to flatten the local optimization landscape by sampling uniformly from constrained high-confidence candidates. |
| Outcome: | Experiments show that the proposed approach prevents policy degeneration and boosts out-of-domain generalization. |
FactBench: A Dynamic Benchmark for In-the-Wild Language Model Factuality Evaluation (2025.acl-long)
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| Challenge: | Language models (LMs) generate false or unverifiable content, often known as hallucination, despite ongoing efforts to enhance their factuality. |
| Approach: | They propose a tool that measures LMs’ factuality in real-world user interactions by evaluating their factual accuracy and categorizing content units as Supported, Unsupported, or Undecidable based on Web-retrieved evidence. |
| Outcome: | The proposed evaluation pipeline measures language models’ factuality in real-world user interactions. |
Cross-Lingual Dependency Parsing by POS-Guided Word Reordering (2020.findings-emnlp)
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| Challenge: | Existing approaches to cross-lingual dependency parsing rely on large corpus size and cost. |
| Approach: | They propose a cross-lingual dependency parsing approach based on word reordering . they propose to train a model that transfers knowledge learned in one or multiple languages to target languages . |
| Outcome: | The proposed approach outperforms the baseline approach in Hindi and Latin by 15.3% and 6.7%. |
LongHeads: Multi-Head Attention is Secretly a Long Context Processor (2024.findings-emnlp)
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| Challenge: | Large language models struggle to process lengthy inputs due to limited length generalization and attention’s quadratic computational demands. |
| Approach: | They propose a training-free framework that allows each head to attend to important context chunks instead of allowing each head a full sentence . |
| Outcome: | The proposed framework unlocks multi-head attention's untapped potential by allowing each head to attend to important context chunks instead of the full sentence. |
From Generic Empathy to Personalized Emotional Support: A Self-Evolution Framework for User Preference Alignment (2025.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) show great potential for expressing empathy, but often deliver generic responses that fail to address users’ specific needs. |
| Approach: | They propose a self-evolution framework to help LLMs improve their responses to better align with users’ implicit preferences concerning personality, emotional state, and specific context. |
| Outcome: | The proposed model significantly improves the model's performance in emotional support, reducing unhelpful responses and minimizing discrepancies between user preferences and model outputs. |
STEER: Semantic Turn Extension-Expansion Recognition for Voice Assistants (2023.emnlp-industry)
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Leon Zhang, Jiarui Lu, Joel Ruben Antony Moniz, Aditya Kulkarni, Dhivya Piraviperumal, Tien Dung Tran, Nick Tzou, Hong Yu
| Challenge: | Existing training datasets for steering use cases are limited due to the cold-start problem. |
| Approach: | They propose a steering detection model that predicts whether a follow-up turn is a user’s attempt to steer the previous command. |
| Outcome: | The proposed model outperforms existing models on human-graded evaluation sets and shows that it can identify steering intent with over 95% accuracy. |
A Knowledge-driven Generative Model for Multi-implication Chinese Medical Procedure Entity Normalization (2020.emnlp-main)
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| Challenge: | Medical entity normalization (NEN) is a task that links medical mentions to entities in knowledge bases. |
| Approach: | They propose a sequence generative framework to generate Chinese medical procedure entity normalization by constraint decoding and category-based model refining. |
| Outcome: | The proposed model improves on baselines especially in the case of multi-implication Chinese medical procedures. |
CLaSp: In-Context Layer Skip for Self-Speculative Decoding (2025.acl-long)
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Longze Chen, Renke Shan, Huiming Wang, Lu Wang, Ziqiang Liu, Run Luo, Jiawei Wang, Hamid Alinejad-Rokny, Min Yang
| Challenge: | Existing methods for drafting Large Language Models require additional modules to be trained, which can be challenging to implement and ensure compatibility across various LLMs. |
| Approach: | They propose an in-context layer-skipping strategy for self-speculative decoding that uses a plug-and-play mechanism to skip intermediate layers of the verify model to construct a compressed draft model. |
| Outcome: | The proposed method achieves a speedup of 1.3 1.7 on LLaMA3 series models without altering the original distribution of the generated text. |
LONGAGENT: Achieving Question Answering for 128k-Token-Long Documents through Multi-Agent Collaboration (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) have been successful in understanding language and processing text, but their cost prohibits their practical applications. |
| Approach: | They propose a multi-agent collaboration method that breaks down lengthy documents into smaller, more manageable chunks and organizes the member agents to read their assigned chunks. |
| Outcome: | The proposed method achieves 16.42% and 1.63% accuracy gains over existing models on single-hop and multi-hop QA settings. |
WildDoc: How Far Are We from Achieving Comprehensive and Robust Document Understanding in the Wild? (2025.emnlp-main)
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An-Lan Wang, Jingqun Tang, Lei Liao, Hao Feng, Qi Liu, Xiang Fei, Jinghui Lu, Han Wang, Hao Liu, Yuliang Liu, Xiang Bai, Can Huang
| Challenge: | Existing benchmarks for document understanding in the wild are based on scanned or digital documents . however, these benchmarks fail to capture the challenges posed by documents in the real world . |
| Approach: | They propose a new benchmark that incorporates a diverse set of manually captured document images reflecting real-world conditions. |
| Outcome: | The proposed model is based on a set of manually captured document images reflecting real-world conditions and is compared with digital or scanned documents. |
ACT-SQL: In-Context Learning for Text-to-SQL with Automatically-Generated Chain-of-Thought (2023.findings-emnlp)
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| Challenge: | Recent studies have focused on the development of semantic parsers within the framework of cross-domain analysis. |
| Approach: | They propose a method to generate auto-CoT exemplars using ACT-SQL and extend it to multi-turn text-to-Sql tasks. |
| Outcome: | The proposed method achieves SOTA performance on the Spider dev set among existing in-context learning approaches. |
Wukong-Reader: Multi-modal Pre-training for Fine-grained Visual Document Understanding (2023.acl-long)
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Haoli Bai, Zhiguang Liu, Xiaojun Meng, Li Wentao, Shuang Liu, Yifeng Luo, Nian Xie, Rongfu Zheng, Liangwei Wang, Lu Hou, Jiansheng Wei, Xin Jiang, Qun Liu
| Challenge: | Existing solutions for visual document understanding lack granularity of document textlines. |
| Approach: | They propose a supervised pre-training program to leverage structural knowledge nested in document textlines to achieve fine-grained alignment between visual regions and texts. |
| Outcome: | The proposed system performs better on various VDU tasks in English and Chinese. |
ModRWKV: Transformer Multimodality in Linear Time (2025.emnlp-main)
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| Challenge: | Currently, multimodal studies are based on large language models with quadratic-complexity Transformer architectures. |
| Approach: | They propose a decoupled multimodal framework built upon the RWKV7 architecture as its LLM backbone and a lightweight architecture to achieve multi-source information fusion. |
| Outcome: | The proposed framework achieves multi-source information fusion through dynamically adaptable heterogeneous modality encoders. |
CogBench: Benchmarking Cognitive Alignment of Large Language Models in Educational Question Answering (2026.findings-acl)
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| Challenge: | Large language models (LLMs) possess strong capabilities in language understanding and generation, as well as remarkable problem-solving abilities. |
| Approach: | They propose a benchmark to assess the cognitive alignment capabilities of large language models in educational QA. |
| Outcome: | The proposed evaluation benchmark assesses the cognitive alignment capabilities of large language models in educational QA. |
BinaryBERT: Pushing the Limit of BERT Quantization (2021.acl-long)
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| Challenge: | Recent pre-trained language models have achieved remarkable performance improvement in various tasks, but the improvement generally comes at the cost of increasing model size and computation. |
| Approach: | They propose a binary quantization technique which initializes binaryBERT by splitting from a ternary network. |
| Outcome: | The proposed model achieves state-of-the-art performance on the GLUE and SQUAD benchmarks while being 24x smaller. |
PLATO-XL: Exploring the Large-scale Pre-training of Dialogue Generation (2022.findings-aacl)
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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
| 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. |
VersaTune: An Efficient Data Composition Framework for Training Multi-Capability LLMs (2025.emnlp-main)
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| Challenge: | Existing work focuses on domain-specific enhancements during fine-tuning, the challenge of which lies in catastrophic forgetting of knowledge across other domains. |
| Approach: | They propose a data composition framework that allows LLMs to enhance their multi-domain capabilities during supervised fine-tuning. |
| Outcome: | The proposed framework improves multi-domain fostering performance by 29.77% compared to uniform weights. |
Specialization without Sparsity: Efficient and Expressive Split-Path Experts for LLM Fine-Tuning (2026.findings-acl)
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| Challenge: | Parameter-efficient fine-tuning (PEFT) is a low-cost alternative to full fine-timing due to the massive overhead. |
| Approach: | They propose a Mixture-of-Experts approach that enhances specialization while maintaining low resource overhead. |
| Outcome: | The proposed approach outperforms or matches state-of-the-art methods on GLUE, GSM8K, MBPP, and a text rewriting task from SmolTalk. |
Insights into LLM Long-Context Failures: When Transformers Know but Don’t Tell (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) exhibit positional bias, struggling to utilize information from the middle or end of long contexts. |
| Approach: | They propose to examine LLMs' long-context generalizations by probing their hidden representations. |
| Outcome: | The proposed models excel at processing extended contexts while preserving their positional bias. |
An Entropy-based Text Watermarking Detection Method (2024.acl-long)
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| Challenge: | Existing text watermarking algorithms for large language models (LLMs) are effective in identifying machine-generated texts, but they are not effective in low-entropy scenarios. |
| Approach: | They propose an Entropy-based text watermarking detection method that takes into account the influence of token entropy to better reflect the degree of watermark detection. |
| Outcome: | The proposed method is training-free and fully automated. |
Mitigating Hallucinations in Large Vision-Language Models by Self-Injecting Hallucinations (2025.findings-emnlp)
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| Challenge: | Existing methods for hallucination mitigation are based on external dependency and require external annotations or auxiliary models for preference data collection. |
| Approach: | a new method is proposed to help model-generated hallucinations without external dependencies. |
| Outcome: | a new method that self-injects hallucinations into a generated response improves halluuutations mitigation. |
CogNet-KG: Empowering Tutoring Dialogues with a Cognitively-Structured Knowledge Graph for STEM Learning (2026.findings-acl)
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| Challenge: | Educational knowledge graphs are a critical component of intelligent tutoring systems that are structured around cognitive principles and provide support for interactive teaching. |
| Approach: | They propose a cognitively-structured large-scale knowledge graph for STEM learning that models nearly 500 core concepts across five subjects with various cognitively grounded relations corresponding to specific learning objectives. |
| Outcome: | The proposed model generates a high-quality tutoring dialogue dataset CogDialogue-QA and a specialized tutorial LLM that internalizes this structured pedagogical reasoning. |
Beyond Full Fine-tuning: Harnessing the Power of LoRA for Multi-Task Instruction Tuning (2024.lrec-main)
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Chunlei Xin, Yaojie Lu, Hongyu Lin, Shuheng Zhou, Huijia Zhu, Weiqiang Wang, Zhongyi Liu, Xianpei Han, Le Sun
| 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. |
Logical Consistency is Vital: Neural-Symbolic Information Retrieval for Negative-Constraint Queries (2025.findings-acl)
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Ganlin Xu, Zhoujia Zhang, Wangyi Mei, Jiaqing Liang, Weijia Lu, Xiaodong Zhang, Zhifei Yang, Xiaofeng Ma, Yanghua Xiao, Deqing Yang
| Challenge: | Current dense retrieval methods compute similarities between dense vectors but overlook the real query intents. |
| Approach: | They propose a neuro-symbolic information retrieval method that leverages first-order logic to optimize the embeddings of naive natural language by considering the logical consistency between queries and documents. |
| Outcome: | The proposed method outperforms existing methods on negative-constraint queries under zero-shot and low-resource retrieval tasks. |
Industry Scale Semi-Supervised Learning for Natural Language Understanding (2021.naacl-industry)
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| Challenge: | Obtaining human annotation is expensive and time-consuming process. |
| Approach: | They propose a semi-supervised learning pipeline which leverages millions of unlabeled examples to improve natural language understanding tasks. |
| Outcome: | The proposed pipeline can be used to improve natural language understanding tasks. |
RealHiTBench: A Comprehensive Realistic Hierarchical Table Benchmark for Evaluating LLM-Based Table Analysis (2025.findings-acl)
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Pengzuo Wu, Yuhang Yang, Guangcheng Zhu, Chao Ye, Hong Gu, Xu Lu, Ruixuan Xiao, Bowen Bao, Yijing He, Liangyu Zha, Wentao Ye, Junbo Zhao, Haobo Wang
| Challenge: | Existing benchmarks for large language models focus on simple, flat table structures. |
| Approach: | They propose a benchmark to evaluate the performance of both Large Language Models and Multimodal LLMs across a variety of input formats for complex tabular data, including LaTeX, HTML, and PNG. |
| Outcome: | The proposed benchmark evaluates the performance of LLMs and Multimodal LLM models across a variety of input formats for complex tabular data, including LaTeX, HTML, and PNG. |
Unleashing the Power of LLMs in Court View Generation by Stimulating Internal Knowledge and Incorporating External Knowledge (2024.findings-naacl)
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| Challenge: | Large Language Models (LLMs) have made remarkable strides in language generation, but they encounter difficulties in the knowledge-intensive legal domain. |
| Approach: | They propose to decompose court views into different parts, stimulate internal knowledge, and incorporate external information to unleash the power of LLMs in the task. |
| Outcome: | The proposed method generates more accurate and reliable court views on two real-world datasets LAIC2021 and CJO2022. |
SelfAug: Mitigating Catastrophic Forgetting in Retrieval-Augmented Generation via Distribution Self-Alignment (2025.findings-emnlp)
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Yuqing Huang, Rongyang Zhang, Qimeng Wang, Chengqiang Lu, Yan Gao, null Yiwu, Yao Hu, Xuyang Zhi, Guiquan Liu, Xin Li, Hao Wang, Enhong Chen
| Challenge: | Existing solutions for supervised fine-tuning often lead to catastrophic forgetting, where models lose their previously acquired knowledge and general capabilities. |
| Approach: | They propose a self-distribution alignment method that aligns input sequence logits to preserve the model’s semantic distribution, thereby mitigating catastrophic forgetting and improving downstream performance. |
| Outcome: | The proposed method achieves a superior balance between downstream learning and general capability retention. |
MolErr2Fix: Benchmarking LLM Trustworthiness in Chemistry via Modular Error Detection, Localization, Explanation, and Correction (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have shown growing potential in molecular sciences, but they often produce chemically inaccurate descriptions and struggle to recognize or justify potential errors. |
| Approach: | They propose a benchmark to assess LLMs on error detection and correction in molecular descriptions. |
| Outcome: | The proposed benchmark targets LLMs on error detection and correction in molecular descriptions. |
Eliminating Out-of-Domain Recommendations in LLM-based Recommender Systems: A Unified View (2026.findings-acl)
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Hao Liao, Jiwei Zhang, Jianxun Lian, Wensheng Lu, Mingqi Wu, null Shuowangg, Yong Zhang, Yitian Huang, Mingyang Zhou, Rui Mao
| Challenge: | Existing approaches to reduce OOD recommendations fall into three grounding paradigms: retrieval, constrained generation and discrete item tokenizer generation. |
| Approach: | They propose a framework that instantiates three grounding paradigms under a single architecture . embedding-based retrieval, constrained generation and discrete item-tokenizer methods are implemented . |
| Outcome: | The proposed framework eradicates OOD recommendations across all variants and achieves state-of-the-art accuracy compared to strong ID-based and LLM-based baselines. |
SiNER: A Large Dataset for Sindhi Named Entity Recognition (2020.lrec-1)
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| Challenge: | Named entity recognition is an essential lower-level task in natural language processing (NLP). |
| Approach: | They propose to develop a named entity recognition dataset for low-resourced Sindhi language with quality baselines. |
| Outcome: | The proposed dataset is likely to be a significant resource for statistical Sindhi language processing. |
Exploring Multimodal Challenges in Toxic Chinese Detection: Taxonomy, Benchmark, and Findings (2025.findings-acl)
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Shujian Yang, Shiyao Cui, Chuanrui Hu, Haicheng Wang, Tianwei Zhang, Minlie Huang, Jialiang Lu, Han Qiu
| Challenge: | Recent studies show that character substitutions in toxic Chinese text can confuse state-of-the-art LLMs. |
| Approach: | They propose a taxonomy of 3 perturbation strategies and 8 specific approaches in Chinese text to assess if they can detect perturbed Chinese toxic contents. |
| Outcome: | The proposed model can detect perturbed Chinese text with 8 different approaches . the proposed model is compared with 9 other LLMs from the US and China . |
PELMS: Pre-training for Effective Low-Shot Multi-Document Summarization (2024.naacl-long)
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| Challenge: | Existing methods for abstractive multi-document summarization fail to generate concise, reflective summaries. |
| Approach: | They propose a pre-trained abstractive multi-document summarization model that uses unlabeled multi-doctoral inputs to generate concise, reflective summaries. |
| Outcome: | The proposed model outperforms competing models on a wide range of MDS datasets. |
CARD: Cross-modal Agent Framework for Generative and Editable Residential Design (2025.emnlp-main)
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| Challenge: | Architectural design automation has made significant progress, but the complexity of open-world environments makes residential design a challenging task. |
| Approach: | They propose a framework that leverages a system of specialized cross-modal agents to adapt to open-world residential design. |
| Outcome: | The proposed framework enables users to generate and edit residential design without requiring specialized expertise. |
DeepSolution: Boosting Complex Engineering Solution Design via Tree-based Exploration and Bi-point Thinking (2025.acl-long)
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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. |
Reason Only When Needed: Efficient Generative Reward Modeling via Model-Internal Uncertainty (2026.findings-acl)
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Chao Xue, Yao Wang, Mengqiao Liu, Di Liang, Xingsheng Han, Peiyang Liu, Xianjie Wu, Chenyao Lu, Lei Jiang, Yu Lu, Haibo Shi, Shuang Liang, Minlong Peng, Flora D. Salim
| Challenge: | Existing approaches to generating reward models rely on voting-based mechanisms to evaluate CoT outputs. |
| Approach: | They propose an efficient generative reward modeling framework grounded in model-internal uncertainty. |
| Outcome: | The proposed framework reduces inference cost while improving answer accuracy. |
Visual Prompt Tuning for Few-Shot Text Classification (2022.coling-1)
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| Challenge: | Existing work on pretraining models for text classification uses image encoders instead of visual prompts. |
| Approach: | They propose a method to deploy large-scale pre-trained models in the prompt-tuning paradigm in few-shot learning. |
| Outcome: | The proposed method outperforms the most recent prompt-tuning methods on five public text classification datasets. |
MProto: Multi-Prototype Network with Denoised Optimal Transport for Distantly Supervised Named Entity Recognition (2023.emnlp-main)
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| Challenge: | Distantly supervised named entity recognition (DS-NER) aims to locate entity mentions and classify their types with knowledge bases or gazetteers and unlabeled corpus. |
| Approach: | They propose a noise-robust prototype network named MProto for a DS-NER task . they propose an optimal transport algorithm to mitigate the noise from incomplete labeling . |
| Outcome: | The proposed network achieves state-of-the-art on several DS-NER benchmarks. |
Class-Adaptive Self-Training for Relation Extraction with Incompletely Annotated Training Data (2023.findings-acl)
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| Challenge: | Existing relation extraction models rely on supervised machine learning, but many datasets are incompletely annotated, causing false negatives and errors during inference stage. |
| Approach: | They propose a class-adaptive re-sampling self-training framework that favored the pseudo-labels of classes with high precision and low recall scores. |
| Outcome: | The proposed framework outperforms existing methods on the Re-DocRED and ChemDisgene datasets when the training data are incompletely annotated. |
The Multilingual Amazon Reviews Corpus (2020.emnlp-main)
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| Challenge: | The corpus contains reviews in English, Japanese, German, French, Spanish, and Chinese, which were collected between 2015 and 2019 . |
| Approach: | They propose to use mean absolute error (MAE) instead of classification accuracy for this task since MAE accounts for ordinal nature of the ratings. |
| Outcome: | The proposed model uses mean absolute error (MAE) instead of classification accuracy since MAE accounts for ordinal nature of the ratings. |
Position-Aware Tagging for Aspect Sentiment Triplet Extraction (2020.emnlp-main)
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| Challenge: | Existing research efforts focus on extracting the triplets of target entities, their associated sentiment, and opinion spans explaining the reason for the sentiment. |
| Approach: | They propose a position-aware tagging scheme that can extract triplets using a sequence tapping approach. |
| Outcome: | The proposed model improves performance on multiple datasets and compares with existing models. |
Interactive Training: Feedback-Driven Neural Network Optimization (2025.emnlp-demos)
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| Challenge: | In traditional neural network training, static optimization methods lack flexibility and responsiveness . authors demonstrate that Interactive Training provides superior training stability and reduced sensitivity to initial hyperparameters . |
| Approach: | They propose an open-source framework that enables real-time feedback-driven optimization of neural networks by human experts or automated AI agents. |
| Outcome: | The proposed framework achieves superior training stability, reduced sensitivity to initial hyperparameters, and improved adaptability to evolving user needs. |
Intent Contrastive Learning Based on Multi-view Augmentation for Sequential Recommendation (2025.coling-main)
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Bo Pei, Yingzheng Zhu, Guangjin Wang, Huajuan Duan, Wenya Wu, Fuyong Xu, Yizhao Zhu, Peiyu Liu, Ran Lu
| Challenge: | Existing work on intent-related models fails to capture long-term dependencies in user behavior and fails to effectively utilize item relevance. |
| Approach: | They propose a sequential recommendation framework that combine temporal variability with position encoding that has extrapolation properties to encode sequences, thereby expanding the model’s view of user behavior. |
| Outcome: | The proposed model improves on three real datasets by 0.8% to 14.7% compared to baselines. |
CL-QR: Cross-Lingual Enhanced Query Reformulation for Multi-lingual Conversational AI Agents (2023.emnlp-industry)
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| Challenge: | Existing QR systems that reformulate defective user queries are limited in English due to the scarcity of non-English QR labels. |
| Approach: | They propose a query reformulation method which reformulates defective user queries to improve non-English QR performance. |
| Outcome: | The proposed framework improves non-English QR performance by leveraging abundant reformulation resources in English. |
Just Adjust One Prompt: Enhancing In-Context Dialogue Scoring via Constructing the Optimal Subgraph of Demonstrations and Prompts (2023.emnlp-main)
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| Challenge: | Using large language models as chatbots can cause hallucinations and lack of empathy, authors report . a dimension-agnostic scoring method is proposed to improve the performance of chatbot performance . |
| Approach: | They propose a dimension-agnostic scoring method that leverages in-context learning . they propose to automatically generate prompts and then request the LLM multiple times . |
| Outcome: | The proposed method outperforms baselines on five datasets. |
When Efficiency Meets Safety: A Benchmark Security Analysis of KV Cache Compression in Large Language Models (2026.acl-long)
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| Challenge: | Key-Value (KV) caching is widely used in large language models to enable long-context inference efficiently, yet its security implications remain underexplored. |
| Approach: | They propose a history-aware, per-head feedback merging strategy that prevents safety degradation while maintaining efficiency. |
| Outcome: | The proposed strategy prevents safety degradation while maintaining efficiency. |
LitVISTA: A Benchmark for Narrative Orchestration in Literary Text (2026.acl-long)
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Mingzhe Lu, Yiwen Wang, Yanbing Liu, Qi You, Chong Liu, Ruize Qin, Haoyu Dong, Wenyu Zhang, JiaRui Zhang, Yue Hu, Yunpeng Li
| Challenge: | Existing large language models focus on causal coherence, neglecting the complex story arcs and orchestration inherent in human narratives. |
| Approach: | They propose a high-dimensional framework for narrative orchestration that unifies human and model perspectives while jointly characterizing narrative function and structure in a common space. |
| Outcome: | The proposed framework unifies human and model perspectives while jointly characterizing narrative function and structure in a common space. |
Slot Transferability for Cross-domain Slot Filling (2021.findings-acl)
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| Challenge: | Existing work on slot filling uses labeled data from source domains to train a model for target domains. |
| Approach: | They propose a model-agnostic Slot Transferability Measure (STM) to evaluate the transferability from a source slot to a target slot. |
| Outcome: | The proposed method outperforms state-of-the-art models on multiple datasets and models. |
Towards Quantifiable Dialogue Coherence Evaluation (2021.acl-long)
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| 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. |
“Where Does This Strange Smell Come from?”: Enabling Conversational Interfaces for Artificial Olfaction (2025.findings-emnlp)
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| Challenge: | Existing Artificial Olfaction (AO) systems are not compatible with smart home scenarios due to diverse obstacles and the need for natural interaction. |
| Approach: | They propose to use large language models to train a CIAO system for Odor Classification and Odor Source Localization in smart home scenarios. |
| Outcome: | The proposed system outperforms existing systems in indoor event detection scenarios. |
UniXcoder: Unified Cross-Modal Pre-training for Code Representation (2022.acl-long)
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| Challenge: | Pre-trained models for programming languages have demonstrated great success on code intelligence . however, such pre-tried models are sub-optimal for auto-regressive tasks . |
| Approach: | They propose a unified cross-modal pre-trained model for programming language that leverages cross-module contents like AST and code comment to enhance code representation. |
| Outcome: | The proposed model achieves state-of-the-art on most code-related tasks and compares with existing models on zero-shot code-to-code search. |
RSVP: Reasoning Segmentation via Visual Prompting and Multi-modal Chain-of-Thought (2025.acl-long)
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Yi Lu, Jiawang Cao, Yongliang Wu, Bozheng Li, Licheng Tang, Yangguang Ji, Chong Wu, Jay Wu, Wenbo Zhu
| Challenge: | Recent advances in multi-modal learning have enhanced MLLMs' ability to reason about visual content. |
| Approach: | They propose a framework that unifies multi-step multimodal reasoning with grounded visual understanding. |
| Outcome: | The proposed framework surpasses state-of-the-art methods by +6.5 gIoU and +9.2 cIou on ReasonSeg and achieves 49.7 mAP on SegInW under zero-shot settings. |
Controllable Open-ended Question Generation with A New Question Type Ontology (2021.acl-long)
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| Challenge: | Existing question types are limited to generating multiple-sense questions . we present a question type-aware question generation framework to generate open-ended questions based on multiple-phrase questions - a task that is less explored . |
| Approach: | They propose a question type-aware question generation framework which predicts question focuses and produces the question. |
| Outcome: | The proposed model improves question quality over competitive comparisons on large-scale datasets. |
Seeing but Not Thinking: Routing Distraction in Multimodal Mixture-of-Experts (2026.acl-long)
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Haolei Xu, Haiwen Hong, Hongxing Li, Rui Zhou, Yang Zhang, Longtao Huang, Hui Xue, Yongliang Shen, Weiming Lu, Yueting Zhuang
| Challenge: | Existing multimodal Mixture-of-Experts models accurately perceive image content yet fail in subsequent reasoning . Seeing but not thinking phenomenon is a puzzling phenomenon . |
| Approach: | They propose a routing-guided intervention method that enhances domain expert activation. |
| Outcome: | The proposed method achieves consistent improvements on visual reasoning tasks. |
MathCanvas: Intrinsic Visual Chain-of-Thought for Multimodal Mathematical Reasoning (2026.acl-long)
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Weikang Shi, Aldrich Yu, Rongyao Fang, Houxing Ren, Ke Wang, Aojun Zhou, Changyao Tian, Xinyu Fu, Yuxuan Hu, Zimu Lu, Linjiang Huang, Si Liu, Rui Liu, Hongsheng Li
| Challenge: | Existing approaches to visual chain-of-thought are limited by external tools or fail to generate high-fidelity diagrams. |
| Approach: | They propose a framework to enable large multimodal models with VCoT capabilities . they pre-train a model on a 15.2M-pair corpus and teach it how to leverage visual aids . |
| Outcome: | The proposed framework unlocks complex, human-like visual reasoning in large language models . it pre-trains the model on a 15.2M-pair corpus and fine-tunes it on MathCanvas-Instruct . |
PychoAgent: Psychology-driven LLM Agents for Explainable Panic Prediction on Social Media during Sudden Disaster Events (2025.emnlp-main)
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Mengzhu Liu, Zhengqiu Zhu, Chuan Ai, Chen Gao, Xinghong Li, Lingnan He, Kaisheng Lai, Yingfeng Chen, Xin Lu, Yong Li, Quanjun Yin
| Challenge: | Social media's rich information content and spatiotemporal granularity provide unique opportunities for emotion prediction and management. |
| Approach: | They propose a Psychology-driven generative Agent framework for explainable panic prediction based on emotion arousal theory. |
| Outcome: | The proposed framework improves panic emotion prediction performance by 13% to 21% compared to baseline models. |
What If Consensus Lies? Selective-Complementary Reinforcement Learning at Test Time (2026.acl-long)
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| Challenge: | Existing TTRL methods rely on positive pseudo-labeling strategies to enhance reasoning capabilities. |
| Approach: | They propose a test-time reinforcement learning framework that mitigates label noise amplification by deriving pseudo-rewards from majority voting consensus. |
| Outcome: | The proposed framework mitigates label noise amplification by implementing selective positive pseudo-labeling and entropy-gated negative p-labeled pruning. |
Encoding and Fusing Semantic Connection and Linguistic Evidence for Implicit Discourse Relation Recognition (2022.findings-acl)
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| Challenge: | Existing studies use one attention mechanism to improve contextual semantic representation learning for implicit discourse relation recognition (IDRR). |
| Approach: | They propose a Multi-Attentive Neural Fusion model to fuse linguistic evidence and semantic connection for IDRR by using a Dual Attention Network and an Offset Matrix Network. |
| Outcome: | The proposed model achieves state-of-the-art on the PDTB 3.0 corpus. |
Aligning Retrieval with Reader Needs: Reader-Centered Passage Selection for Open-Domain Question Answering (2025.coling-main)
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Chunlei Xin, Shuheng Zhou, Xuanang Chen, Yaojie Lu, Huijia Zhu, Weiqiang Wang, Zhongyi Liu, Xianpei Han, Le Sun
| Challenge: | Existing retrieval methods aim to gather relevant passages but fail to prioritize consistent and useful information for the reader. |
| Approach: | They propose a novel method which re-ranks passages based on the reader's prediction probability distribution and clusters passage according to the predicted answers. |
| Outcome: | The proposed method improves the quality of evidence passages under zero-shot scenarios. |
Leveraging Training Data in Few-Shot Prompting for Numerical Reasoning (2023.findings-acl)
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| Challenge: | Chain-of-thought (CoT) prompts can be challenging to design for arithmetic word problem solving. |
| Approach: | They propose to use training data to replace CoT with programs as the reasoning step . their results show that leveraging training data can improve generalization ability of prompts . |
| Outcome: | The proposed methods improve the generalization ability of prompts and the performance of fine-tuned smaller models in arithmetic word problem solving. |
A Survey on Training-free Alignment of Large Language Models (2025.findings-emnlp)
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Birong Pan, Yongqi Li, Weiyu Zhang, Wenpeng Lu, Mayi Xu, Shen Zhou, Yuanyuan Zhu, Ming Zhong, Tieyun Qian
| Challenge: | a survey of large language models (LLMs) aims to ensure outputs adhere to human values, ethical standards, and legal norms. |
| Approach: | They present the first systematic review of TF alignment methods . they categorize them by stages of pre-decoding, in-decoder and post-decoration . |
| Outcome: | The proposed methods are based on training-free (TF) alignment techniques . they are able to be used in open-source and closed-source environments without retraining . |
CASPER in the Machine: Insights into Character Variety in LLM-Generated Stories (2026.acl-long)
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| Challenge: | Increasing numbers of authors are using AI to assist in the process of writing stories. |
| Approach: | They analyze 8 category-pairs of character that assess how characters are portrayed in short stories . they find similarities between LLMs and human-written stories based on categories . |
| Outcome: | The analysis includes questions on popular LLMs and recently published human-written stories. |
HIPO: A Hierarchical Prompt Optimization Framework with Task Awareness and Fine-Grained Debugging (2026.findings-acl)
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| Challenge: | Existing methods for prompt optimization apply the same prompt across all samples . existing methods ignore variation in sample difficulty . |
| Approach: | They propose a framework that shifts the paradigm from dataset-level to sample-level optimization. |
| Outcome: | The proposed framework outperforms baselines on 27 tasks and reduces API calls, token consumption and overall cost by 1.2 to 80. |
TV-AfD: An Imperative-Annotated Corpus from The Big Bang Theory and Wikipedia’s Articles for Deletion Discussions (2020.lrec-1)
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| Challenge: | Detecting imperatives in oral and written communication is difficult when the user doesn't use the expected forms. |
| Approach: | They created an imperative corpus with dialogues from The Big Bang Theory and Wikipedia comments from Wikipedia . they manually annotated imperatives and used a syntax-based classifier to extract 10,624 statements that may be imperative. |
| Outcome: | The proposed model performs better in the written data compared to speech data, but has a low precision and recall for speech data. |
Enriching and Controlling Global Semantics for Text Summarization (2021.emnlp-main)
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| Challenge: | Abstractive summarization models have been proven effective in creating fluent and informative summaries, but they suffer from the short-range dependency problem, causing them to produce summary that miss the key points of document. |
| Approach: | They propose a neural topic model empowered with normalizing flow to capture global semantics of the document and integrate them into the summarization model. |
| Outcome: | The proposed model outperforms state-of-the-art summarization models on five common text summarizing datasets, namely CNN/DailyMail, XSum, Reddit TIFU, arXiv, and PubMed. |
Multi-task Learning for Paraphrase Generation With Keyword and Part-of-Speech Reconstruction (2022.findings-acl)
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| Challenge: | PGKPR is a deep learning approach to generate paraphrases using key semantics of the source sentence. |
| Approach: | They propose a model with keyword and part-of-speech reconstruction for paraphrase generation using deep learning. |
| Outcome: | The proposed model outperforms comparative models on two commonly-used datasets. |
On the Transferability of Minimal Prediction Preserving Inputs in Question Answering (2021.naacl-main)
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| Challenge: | Recent work establishes the presence of short, uninterpretable input fragments that yield high confidence and accuracy in neural models. |
| Approach: | They investigate competing hypotheses for the existence of MPPIs in question answering . they discover a perplexing invariance of MPIs to random training seed, model architecture, pretraining, and training domain. |
| Outcome: | The proposed model performance is higher than comparable short queries. |
Object-oriented Neural Programming (OONP) for Document Understanding (P18-1)
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| Challenge: | Object-oriented Neural Programming (OONP) is a framework for semantically parsing documents in domains. |
| Approach: | They propose a framework for semantically parsing documents in specific domains using OONP . OOPN parsers use a rich family of operations to represent the semantics of the document . |
| Outcome: | The proposed framework can learn to handle fairly complicated ontology with training data of modest sizes. |
CLEAR: Can Language Models Really Understand Causal Graphs? (2024.findings-emnlp)
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| Challenge: | Existing language models lack a conceptual framework for understanding causal graphs, but there is still potential for improvement. |
| Approach: | They develop a framework to define causal graph understanding by assessing language models’ behaviors through four practical criteria derived from diverse disciplines. |
| Outcome: | The proposed framework defines three complexity levels and encompasses 20 causal graph-based tasks across 20 different levels. |
Improving Knowledge Graph Completion with Structure-Aware Supervised Contrastive Learning (2024.emnlp-main)
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| 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. |
Do Language Models Think Consistently? A Study of Value Preferences Across Varying Response Lengths (2026.findings-acl)
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| Challenge: | In short-form surveys and psychometric tests, value-related risks and preferences are often underexplored in practical settings. |
| Approach: | They compare short-form responses to long-form outputs to determine whether value preferences align with those expressed in long-term outputs. |
| Outcome: | The proposed method yields only modest gains in the consistency of value expression. |
Uncovering Main Causalities for Long-tailed Information Extraction (2021.emnlp-main)
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| Challenge: | Information Extraction (IE) aims to extract structural information from unstructured texts. |
| Approach: | They propose a framework that aims to uncover the main causalities behind data in the view of causal inference. |
| Outcome: | The proposed framework can detect the main causalities behind data in the view of causal inference. |
News2vec: News Network Embedding with Subnode Information (D19-1)
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| Challenge: | Existing approaches to embed news as vectors do not integrate features and inter-textual knowledge of news. |
| Approach: | They propose a model that integrates news features and inter-textual knowledge into a dense vector representation. |
| Outcome: | The proposed model can be used to represent news as a dense vector . it is compared with existing models on stock movement prediction and news recommendation tasks . |
Don’t Lose Yourself! Empathetic Response Generation via Explicit Self-Other Awareness (2023.findings-acl)
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| Challenge: | Existing attempts to generate empathy with other-awareness ignore to include self-a awareness to consider the own views of the self in their responses. |
| Approach: | They propose to include self-awareness to consider the own views of the self in empathetic response generation by integrating three stages of self-other awareness into the process. |
| Outcome: | The proposed method is superior to existing methods on the benchmark dataset. |
Value Compass Benchmarks: A Comprehensive, Generative and Self-Evolving Platform for LLMs’ Value Evaluation (2025.acl-demo)
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Jing Yao, Xiaoyuan Yi, Shitong Duan, Jindong Wang, Yuzhuo Bai, Muhua Huang, Yang Ou, Scarlett Li, Peng Zhang, Tun Lu, Zhicheng Dou, Maosong Sun, James Evans, Xing Xie
| Challenge: | Current evaluation methods for large language models face two key challenges: 1. evaluation validity and 2. Result interpretation reduce the pluralistic and incommensurable values to one-dimensional scores. |
| Approach: | They propose a platform for comprehensive value diagnosis of large language models (LLMs) that provides a generative evaluation paradigm that automatically creates real-world test items co-evolving with ever-advancing LLMs. |
| Outcome: | The proposed platform provides a framework for comprehensive value diagnosis of large language models (LLMs) with fine-grained scores and case studies across 27 value dimensions for 33 leading LLMs, customized comparisons, and visualized analysis of LLM’s alignment with cultural values. |
Influence Paths for Characterizing Subject-Verb Number Agreement in LSTM Language Models (2020.acl-main)
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| Challenge: | LSTMs can capture syntactic rules in artificial languages, but it is unclear whether they are as capable in natural languages. |
| Approach: | They propose a causal account of structural properties as carried by paths across gates and neurons of a recurrent neural network that localizes and segments the concept into a set of gate or neuron-level paths. |
| Outcome: | The proposed model improves on a widely-studied multi-layer LSTM language model showing that it can learn subject-verb number agreement in English. |
Stephanie: Step-by-Step Dialogues for Mimicking Human Interactions in Social Conversations (2025.findings-naacl)
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Hao Yang, Hongyuan Lu, Xinhua Zeng, Yang Liu, Xiang Zhang, Haoran Yang, Yumeng Zhang, Shan Huang, Yiran Wei, Wai Lam
| Challenge: | a new paradigm for dialogue systems is being developed to mimic human interactions . the current single-step dialogue paradigm lacks the depth and fluidity of human interactions. |
| Approach: | They propose a step-by-step dialogue paradigm that mimics human interactions . they use a dataset to fine-tune existing language models . |
| Outcome: | The proposed system mimics the dynamic nature of human conversations . it is compared with existing paradigms and will be released later this year . |
A Simple LLM Framework for Long-Range Video Question-Answering (2024.emnlp-main)
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| Challenge: | a recent study has shown that short video understanding is not trivial due to the need for long-range temporal reasoning capabilities. |
| Approach: | They propose a language-based short- and long-range question-answering framework LLoVi . they propose 'multi-round summarization prompt' that asks the LLM to summarize the captions . |
| Outcome: | The proposed framework outperforms the state-of-the-art on the EgoSchema dataset and to grounded VideoQA. |
Lego-MT: Learning Detachable Models for Massively Multilingual Machine Translation (2023.findings-acl)
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| Challenge: | Existing monolithic models for multilingual neural machine translation encounter parameter interference and inefficient inference for large models. |
| Approach: | They propose a detachable multi-way model that assigns each language to an individual branch . they use data from OPUS to build a translation benchmark covering 433 languages . |
| Outcome: | The proposed model outperforms existing models in OPUS and is faster than existing models. |
Large Vision-Language Model Alignment and Misalignment: A Survey Through the Lens of Explainability (2025.findings-emnlp)
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| Challenge: | Large Vision-Language Models have demonstrated remarkable capabilities in processing both visual and textual information. |
| Approach: | They examine the challenge of alignment and misalignment in LVLMs through an explainability lens. |
| Outcome: | The findings highlight the need for standardized evaluation protocols and in-depth explainability studies. |
One Panel Does Not Fit All: Case-Adaptive Multi-Agent Deliberation for Clinical Prediction (2026.acl-srw)
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| 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. |
Inter-GPS: Interpretable Geometry Problem Solving with Formal Language and Symbolic Reasoning (2021.acl-long)
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| Challenge: | Existing methods for solving geometric problems are either small in scale or not publicly available. |
| Approach: | They propose a large-scale benchmark for geometric problem solving using formal language and symbolic reasoning. |
| Outcome: | The proposed approach parses geometry problems into formal language and performs symbolic reasoning step by step. |
Adaptive Detoxification: Safeguarding General Capabilities of LLMs through Toxicity-Aware Knowledge Editing (2025.findings-acl)
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Yifan Lu, Jing Li, Yigeng Zhou, Yihui Zhang, Wenya Wang, Xiucheng Li, Meishan Zhang, Fangming Liu, Jun Yu, Min Zhang
| Challenge: | Existing knowledge editing methods for large language models (LLMs) suffer from over-editing, where detoxified models reject legitimate queries, compromising overall performance. |
| Approach: | They propose a toxicity-aware knowledge editing approach that dynamically detects toxic activation patterns during forward propagation and then routes computations through adaptive inter-layer pathways to mitigate toxicity effectively. |
| Outcome: | The proposed method outperforms existing methods on large language models and enhances the SafeEdit benchmark. |
Optimizing Decomposition for Optimal Claim Verification (2025.acl-long)
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| Challenge: | Existing decomposition and verification paradigms ignore their interactions and potential misalignment. |
| Approach: | They propose a reinforcement learning framework that leverages verifier feedback to learn a policy for dynamically decomposing claims to verifier-preferred atomicity. |
| Outcome: | The proposed framework outperforms existing decomposition policies in verification confidence tests . it improves accuracy and confidence by 0.12 on average across varying verifiers, datasets, and atomcities of input claims. |
EasyEA: Large Language Model is All You Need in Entity Alignment Between Knowledge Graphs (2025.findings-acl)
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| Challenge: | Entity alignment (EA) aims to identify entities in different knowledge graphs (KGs) that represent the same real-world object. |
| Approach: | They propose an end-to-end EA framework based on large language models that requires no training to implement. |
| Outcome: | The proposed framework significantly reduces the reliance on seed entity pairs while achieving state-of-the-art (SOTA) performance on diverse datasets. |
Efficient Ensemble for Fine-tuning Language Models on Multiple Datasets (2025.acl-long)
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| Challenge: | Existing methods for fine-tuning language models are efficient when adapting to a single dataset. |
| Approach: | They propose to use an ensemble method for fine-tuning a language model to multiple datasets instead of a single adapter per task. |
| Outcome: | The proposed method improves performance on multiple datasets while preserving low-rank adaptation properties. |
Denoising Rationalization for Multi-hop Fact Verification via Multi-granular Explainer (2024.findings-emnlp)
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| Challenge: | Existing rationalization methods for multi-hop fact verification lack nuanced composition in the evidence, which leads to noise rationalization. |
| Approach: | They propose a method to obtain rationale by completely removing subset of input without compromising verification accuracy. |
| Outcome: | The proposed method outperforms 12 baselines on three multi-hop fact verification datasets. |
Please refuse to answer me! Mitigating Over-Refusal in Large Language Models via Adaptive Contrastive Decoding (2026.acl-long)
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| Challenge: | Existing methods for mitigating over-refusal can't maintain low refusal ratio for harmless queries while keeping high for malicious queries. |
| Approach: | They propose a model-agnostic approach to mitigate over-refusal in large language models . they propose an adaptive contrastive decoding strategy that incorporates or removes the refusal token distribution . |
| Outcome: | The proposed approach reduces the refusal ratio for over-refusal queries by 10.35% while increasing the refusal rate for malicious queries by 0.13%. |
OCNLI: Original Chinese Natural Language Inference (2020.findings-emnlp)
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| Challenge: | Recent efforts to extend natural language understanding to other languages have focused on (automatically) translating existing English datasets. |
| Approach: | They propose to use a Chinese dataset to generate annotated sentences from native speakers specializing in linguistics to elicit annotations. |
| Outcome: | The proposed dataset does not rely on automatic translation or non-expert annotation. instead, it elicits annotations from native speakers specializing in linguistics. |
I2D2: Inductive Knowledge Distillation with NeuroLogic and Self-Imitation (2023.acl-long)
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Chandra Bhagavatula, Jena D. Hwang, Doug Downey, Ronan Le Bras, Ximing Lu, Lianhui Qin, Keisuke Sakaguchi, Swabha Swayamdipta, Peter West, Yejin Choi
| Challenge: | Empirical results suggest that scale is not the only way to build commonsense capabilities. |
| Approach: | They propose a commonsense distillation framework that can achieve a competitive level of commonsensing without relying on the benefits of scale. |
| Outcome: | The proposed framework breaks the dependence on the extreme-scale teacher model with two innovations: (1) the novel adaptation of NeuroLogic Decoding to enhance the generation quality of the weak, off-the-shelf language models, and (2) self-imitation learning to iteratively learn from the model’s own enhanced commonsense acquisition capabilities. |
Diagnosing Hidden Instabilities in Model Editing via Uncertainty Quantification (2026.acl-long)
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Zihan Gu, TianYi Zhang, Xinyan Zhang, Zhiyuan Wang, Han Zhang, Yuhao Wei, Jiacheng Lu, Tianyi Ma, Xingsheng Zhang, Hua Zhang, Yue Hu
| Challenge: | Existing methods to update large language models (LLMs) without expensive retraining are fragile under single-edit evaluation protocols. |
| Approach: | They propose a framework that characterizes activation-based editing as a constrained intervention on intermediate representations. |
| Outcome: | The proposed method reveals local knowledge conflicts invisible to existing benchmarks. |
Knowledgeable Preference Alignment for LLMs in Domain-specific Question Answering (2024.findings-acl)
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| Challenge: | Domain-specific question answering (QA) requires a comprehensive understanding of a specific domain to answer specialized questions. |
| Approach: | They propose a new alignment objective to align the LLM preference with different human preferences uniformly to optimize LLM performance in real-world, domain-specific QA settings. |
| Outcome: | The proposed pipeline is superior for real-scenario domain-specific question answering with LLMs. |
When Models Outthink Their Safety: Unveiling and Mitigating Self-Jailbreak in Large Reasoning Models (2026.findings-acl)
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Yingzhi Mao, Chunkang Zhang, Junxiang Wang, Xinyan Guan, Boxi Cao, Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun
| 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. |
Sailor: Open Language Models for South-East Asia (2024.emnlp-demo)
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| 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. |
FloorPlan-LLaMa: Aligning Architects’ Feedback and Domain Knowledge in Architectural Floor Plan Generation (2025.acl-long)
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| Challenge: | Existing evaluation methods for floor plan generation rely on statistical metrics like FID, GED, and PSNR, which fail to evaluate using domain knowledge. |
| Approach: | They propose to use a first floor plan dataset to train a floor plan generation model based on a multi-dimensional preference score and a textual analysis to integrate architects’ professional expertise and preferences. |
| Outcome: | The proposed model outperforms baseline models in text-conditional and class-condition tasks and is more rational and aligns better with human preferences. |
BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization (P19-1)
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| Challenge: | Existing text summarization datasets are compiled from news articles, where summary-worthy content often appears in the beginning of input articles. |
| Approach: | They present a novel dataset, BIGPATENT, consisting of 1.3 million records of U.S. patent documents along with human written abstractive summaries. |
| Outcome: | The proposed dataset is compared with existing summarization datasets and demonstrates that salient content is evenly distributed in the input. |
Linear-Time Demonstration Selection for In-Context Learning via Gradient Estimation (2025.emnlp-main)
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| Challenge: | Existing methods to select demonstration examples for in-context learning are based on token embeddings. |
| Approach: | They propose an algorithm to select demonstration examples for in-context learning of a query set . they use gradients of the output taken in the input embedding space to estimate model outputs . |
| Outcome: | The proposed algorithm outperforms existing methods based on token embeddings by 11% . it scales up subset selection that would otherwise run full inference by 37.7 on models with 34 billion parameters . |
Unstructured Evidence Attribution for Long Context Query Focused Summarization (2025.emnlp-main)
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| Challenge: | Existing systems struggle to copy and properly cite unstructured evidence, which also tends to be “lost-in-the-middle”. |
| Approach: | They propose to extract unstructured evidence spans to improve the trustworthiness of large language models by citing unstructure . they propose to use this dataset as a training supervision for unstructure-based evidence summarization. |
| Outcome: | The proposed pipeline generates more relevant and factually consistent evidence than baselines with no fine-tuning and fixed granularity evidence. |
SweetieChat: A Strategy-Enhanced Role-playing Framework for Diverse Scenarios Handling Emotional Support Agent (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) have demonstrated promising potential in providing empathetic support during interactions, but their responses are often verbose or overly formulaic, failing to adequately address the diverse emotional support needs of real-world scenarios. |
| Approach: | They propose a strategy-enhanced role-playing framework that emulates real-world interactions and a dataset that is used to develop an emotional support agent. |
| Outcome: | The proposed framework emulates real-world interactions and promotes a broader range of dialogues and Emotional Support Agent training. |
Preconditioned Test-Time Adaptation for Out-of-Distribution Debiasing in Narrative Generation (2026.acl-long)
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| Challenge: | Debiased large language models excel at handling known or low-bias prompts, but fail on unfamiliar and high-biased prompts. |
| Approach: | They propose a debiasing framework that detects high-bias prompts and triggers context-aware LoRA updates only when a bias-risk score exceeds a threshold. |
| Outcome: | The proposed framework reduces toxicity/bias score with significantly lower latency than standard optimization methods. |
Comprehensive Benchmarking of Long-Form Speech Generation in Diverse Scenarios (2026.findings-acl)
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Changhao Pan, Rui Yang, Han Wang, Zhuan Zhou, Xuming He, Wenxiang Guo, Ziyue Jiang, Ruiqi Li, Yu Zhang, Chenyuhao Wen, Ke Lei, Xiang Yin, Jingyu Lu, Zhiyuan Zhu, Zhou Zhao
| Challenge: | Existing evaluation benchmarks for long-form speech are limited to limited domains, creating a significant gap with the diverse downstream applications. |
| Approach: | They propose a benchmark that decomposes "long-form speech quality" into specific, disentangled dimensions. |
| Outcome: | The proposed benchmark decomposes “long-form speech quality” into specific, disentangled dimensions. |
Is the Attention Matrix Really the Key to Self-Attention in Multivariate Long-Term Time Series Forecasting? (2026.acl-long)
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| Challenge: | In multivariate long-term time series forecasting, it is widely believed that the effectiveness of self-attention arises from its attention matrix. |
| Approach: | They propose a multi-branch MLP that isolates the ‘multi-brain mapping with element-wise operation’ structure from the Transformer and shows that it achieves competitive performance. |
| Outcome: | The proposed model outperforms three classic and three latest Transformer models and shows that it achieves competitive performance. |
Experience Retrieval-Augmentation with Electronic Health Records Enables Accurate Discharge QA (2026.acl-long)
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| Challenge: | Existing methods to improve the reliability of Large Language Models (LLMs) in clinical applications require factual knowledge from open-ended datasets and clinical case-based knowledge to provide context grounded in real-world patient experiences. |
| Approach: | They propose a retrieval-augmented generation framework based on the electronic health record to offer contextual information from other patients’ discharge reports. |
| Outcome: | The proposed framework outperforms a text-based ranker in a clinical QA dataset with 1,280 discharge-related questions . |
Multimodal Generation with Consistency Transferring (2025.findings-naacl)
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| Challenge: | Existing methods for multimodal content generation are limited to unimodal content production due to high training complexity, significant costs, and inadequate emphasis on model constraints. |
| Approach: | They propose a method to generate multimodal content with constraints on adjacent steps and a layer-based layer-constrained transfer between adjacent steps to improve denoising capabilities. |
| Outcome: | The proposed method improves the model’s ability to capture actions and depict backgrounds more effectively and improves video generation speed by approximately 40% and quality by about 39.3%. |
LNE-Blocking: An Efficient Framework for Contamination Mitigation Evaluation on Large Language Models (2025.findings-emnlp)
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| Challenge: | a problem of data contamination is now almost inevitable during the development of large language models, with the training data often integrating evaluation benchmarks even unintentionally. |
| Approach: | They propose a framework to restore model performance prior to data contamination on potentially leaked datasets by using contamination detection and disruption operation. |
| Outcome: | The proposed framework restores model performance prior to contamination on potentially leaked datasets. |
DialogBench: Evaluating LLMs as Human-like Dialogue Systems (2024.naacl-long)
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| Challenge: | Existing benchmarks only evaluate LLMs' abilities for task completion as assistant AI. |
| Approach: | They propose a dialogue evaluation benchmark that contains 12 dialogue tasks to evaluate LLMs' capabilities as human-like dialogue systems. |
| Outcome: | The proposed benchmark contains 12 tasks to evaluate LLMs' capabilities . it shows that instruction tuning improves human likeness, but not as human-like systems . |
LACMA: Language-Aligning Contrastive Learning with Meta-Actions for Embodied Instruction Following (2023.emnlp-main)
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| Challenge: | Embodied Instruction Following has shown an impressive success rate when the environment has been seen in training, but when deployed in an unseen environment, it tends to struggle when deployed with an unsightly environment. |
| Approach: | They propose to explicitly align the agent’s hidden states with the instructions via contrastive learning to bridge the semantic gap between high-level language instructions and the agent's low-level action space. |
| Outcome: | The proposed meta-actions achieve a 4.5% success rate in unseen environments compared to a strong multi-modal Transformer baseline . |
Efficient Context and Schema Fusion Networks for Multi-Domain Dialogue State Tracking (2020.findings-emnlp)
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| Challenge: | Existing methods to track dialogue state are limited due to data sparsity and long dialogues. |
| Approach: | They propose to use the previous dialogue state and current dialogue utterance as input for DST. |
| Outcome: | The proposed approach outperforms existing methods and improves existing ones. |
Better Transition-Based AMR Parsing with a Refined Search Space (D18-1)
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| Challenge: | Abstract Meaning Representation (AMR) parsers require a pipeline approach to learn concepts and relationships. |
| Approach: | They propose to use a transition-based search space to conduct a new compact AMR graph and an improved oracle to perform the search. |
| Outcome: | The proposed system achieves the state-of-the-art performance on various datasets with minimal additional information. |
Iterative Refinement of Project-Level Code Context for Precise Code Generation with Compiler Feedback (2024.findings-acl)
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Zhangqian Bi, Yao Wan, Zheng Wang, Hongyu Zhang, Batu Guan, Fangxin Lu, Zili Zhang, Yulei Sui, Hai Jin, Xuanhua Shi
| Challenge: | Large Language Models (LLMs) generate code for given contexts, such as incomplete code, class, data structure, or project-specific information. |
| Approach: | They propose a compiler feedback-based code generation approach that leverages static analysis to identify mismatches between the generated code and the project's context. |
| Outcome: | The proposed model outperforms retrieval-based code generation baselines and significantly outperfies the existing large language models. |
Improving Event Coreference Resolution by Learning Argument Compatibility from Unlabeled Data (N19-1)
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| Challenge: | Argument compatibility is a linguistic condition that is often used in event coreference resolution systems. |
| Approach: | They propose a transfer learning framework that uses unlabeled data to learn argument compatibility of event mentions. |
| Outcome: | The proposed model improves the performance of the overall event coreference model on the English dataset. |
Handling Homographs in Neural Machine Translation (N18-1)
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| Challenge: | Existing methods for MT have problems with translating homographs, as it is difficult to select the correct translation based on the context. |
| Approach: | They propose to model the context of the input word with context-aware word embeddings that help to differentiate the word sense before feeding it into the encoder. |
| Outcome: | The proposed models improve translation accuracy and BLEU score on three language pairs. |
MuG: A Multimodal Classification Benchmark on Game Data with Tabular, Textual, and Visual Fields (2023.findings-emnlp)
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| Challenge: | Existing multimodal classification systems use tabular, textual, and visual data to provide efficient and scalable services. |
| Approach: | They propose a multimodal classification benchmark MuG with eight datasets . they analyze label balance ratios, percentages of missing features, distributions of data within each modality . |
| Outcome: | The proposed benchmark is available on https://github.com/lujiaying/MUG-Bench . it includes eight datasets that allow researchers to evaluate and improve their models . |
CA-GAR: Context-Aware Alignment of LLM Generation for Document Retrieval (2025.findings-acl)
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| Challenge: | Recent techniques such as Generation-Augmented Retrieval (GAR) and Generative Document Retrieleval (GDR) leverage LLMs to enhance retrieval performance but face key challenges: GAR’s generated content may not always align with the target document corpus, while GDR limits the generative capacity of LLM. |
| Approach: | They propose a Context-Aware Generation-Augmented Retrieval approach which integrates corpus information into their generation process. |
| Outcome: | Experimental results show that CA-GAR outperforms existing methods on seven tasks and four non-English languages. |
DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environments (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) with web search capabilities show significant potential for deep research. |
| Approach: | They introduce a framework for end-to-end training of LLM-based deep research agents . they implement a specialized multi-agent architecture where browsing agents extract relevant information from various webpage structures. |
| Outcome: | The proposed framework improves on open-domain research tasks by 28.9 points over prompt engineering and 7.2 points over RAG-based RL agents. |
Eliminating Biased Length Reliance of Direct Preference Optimization via Down-Sampled KL Divergence (2024.emnlp-main)
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| Challenge: | Existing studies attributed verbosity to biased labels, but new research shows that DPO can be effective in mitigating verboses. |
| Approach: | They propose to use a method to reduce the amount of verbosity in LLMs by using a downsampling approach. |
| Outcome: | The proposed approach overcomes the problem of verbosity by reducing the length reliance of the proposed algorithm. |
TRACE: A Framework for Analyzing and Enhancing Stepwise Reasoning in Vision-Language Models (2026.eacl-long)
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| Challenge: | Evaluating large vision-language models has focused on final-answer correctness, but this metric is often insufficient and misleading. |
| Approach: | They propose a framework that decomposes complex multimodal tasks into Auxiliary Reasoning Sets (ARS) ARS decomposition reveals how consistently a model reasons across sub-questions with structured dependencies. |
| Outcome: | a new framework improves diagnostic evaluation of large vision-language models . it decomposes complex multimodal tasks into auxiliary reasoning sets with structured dependencies . the framework pinpoints reasoning failures and exposes errors overlooked by standard evaluation . |
Enhancing Large Language Models in Coding Through Multi-Perspective Self-Consistency (2024.acl-long)
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| Challenge: | Existing work utilizes verification properties to verify and re-rank solutions in a majority voting manner, but this assumption may not hold. |
| Approach: | They propose a multi-perspective self-consistency framework that incorporates both inter- and intra-consistency across outputs from multiple perspectives. |
| Outcome: | The proposed framework significantly boosts performance of foundation models on various benchmarks, including HumanEval (+15.91%), MBPP (+6.43%) and CodeContests (+9.37%). |
Detect All Abuse! Toward Universal Abusive Language Detection Models (2020.coling-main)
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| Challenge: | Existing work on online abusive language detection focused on detecting a single abusive language problem in a domain, like Twitter, but none of them was successfully transferable to general ALD in different online communities. |
| Approach: | They propose a generic ALD framework that can address multiple types of ALD tasks across different domains and use a textual graph embedding to analyse the user’s linguistic behaviour. |
| Outcome: | The proposed framework surpasses the current state-of-the-art ALD algorithms across seven datasets covering multiple aspects of abusive language and different online community domains. |
Igniting Creative Writing in Small Language Models: LLM-as-a-Judge versus Multi-Agent Refined Rewards (2025.emnlp-main)
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| Challenge: | Existing methods for enhancing Large Language Models (LLMs) struggle with novelty and Reinforcement Learning from human feedback (RLHF) is costly. |
| Approach: | They propose to use a Reward Model (RM) and a principle-guided LLM-as-a-Judge to enhance creative output over baselines. |
| Outcome: | The proposed approach significantly enhances creative output over baselines, but the principle-guided LLM-as-a-Judge yields superior generation quality. |
NeuSym-RAG: Hybrid Neural Symbolic Retrieval with Multiview Structuring for PDF Question Answering (2025.acl-long)
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Ruisheng Cao, Hanchong Zhang, Tiancheng Huang, Zhangyi Kang, Yuxin Zhang, Liangtai Sun, Hanqi Li, Yuxun Miao, Shuai Fan, Lu Chen, Kai Yu
| Challenge: | Existing approaches to retrieval augmented generation neglect PDF structure and layout . individual PDFs often exceed prompt limits and user queries may span multiple documents. |
| Approach: | They propose a hybrid neural symbolic retrieval framework which combines both paradigms in an interactive process. |
| Outcome: | The proposed framework organizes semi-structured PDF content into relational database and vectorstore . it defeats both RAG and structured baselines on three PDF-based QA datasets . |
UniGeM: Unifying Data Selection and Mixing via Geometric Exploration and Mining (2026.findings-acl)
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Changhao Wang, null Yunfeiyu, Xinhao Yao, Jiaolong Yang, Lu Yu, Junpeng Fang, Chaobo Li, Riccardo Cantoro, Qing Cui, Jun Zhou
| Challenge: | Large Language Models (LLMs) scaling is limited by data quality and domain mixing and instance selection are two separate problems. |
| Approach: | They propose a framework that unifies mixing and selection without training proxy models or relying on external reference datasets. |
| Outcome: | The proposed framework achieves 2.0 data efficiency over a random baseline and further improves overall performance compared to SOTA methods in reasoning-heavy evaluations and multilingual generalization. |
MMErroR: A Benchmark for Erroneous Reasoning in Vision-Language Models (2026.acl-long)
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Yang Shi, Yifeng Xie, Minzhe Guo, Liangsi Lu, Mingxuan Huang, Jingchao Wang, Zhihong Zhu, Boyan Xu, Zhiqi Huang
| Challenge: | Recent advances in vision-language models have improved performance in multi-modal learning. |
| Approach: | They propose a multi-modal benchmark that embeds a single coherent reasoning error in 1997 samples. |
| Outcome: | The proposed benchmark is based on a set of 1997 samples embedding a single coherent reasoning error. |
Spec-o3: A Tool-Augmented Vision-Language Agent for Rare Celestial Object Candidate Vetting via Automated Spectral Inspection (2026.acl-long)
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| Challenge: | Spec-o3 is a tool-augmented vision-language agent that performs astronomer-aligned spectral inspection. |
| Approach: | They propose a tool-augmented vision-language agent that performs astronomer-aligned spectral inspection via interleaved multimodal chain-of-thought reasoning. |
| Outcome: | Spec-o3 outperforms traditional visual inspection methods on rare-object inspection tasks. |
An Exploration of Data Augmentation and Sampling Techniques for Domain-Agnostic Question Answering (D19-58)
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| Challenge: | XLNet model is domain-agnostic for the MRQA 2019 Shared Task . a negative sampling technique is particularly effective for datasets that include unanswerable questions . |
| Approach: | They develop a domain-agnostic question answering model for the MRQA 2019 Shared Task . they use large pre-trained language models, various data sampling strategies and query and context paraphrases generated by back-translation . |
| Outcome: | The proposed model achieves second best Exact Match and F1 in the MRQA leaderboard competition. |
Scaling LLMs’ Social Reasoning: Sprinkle Cognitive “Aha Moment” into Fundamental Long-thought Logical Capabilities (2025.findings-acl)
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| Challenge: | Existing studies have examined how large language models’ social reasoning capabilities evolve during model size scaling or reasoning tokens scaling. |
| Approach: | They propose to optimize evaluation of Large Language Models from both data and model perspectives and to analyze their reasoning trajectories to identify notable cognitive "Aha Moments" |
| Outcome: | The proposed model outperforms the o1-preview model by 19.0 points in the evaluation of large language models. |
LLMs for Now, Fine-Tuning for Later: An Ensemble Approach to Data Drift in Domain-Specific Tasks (2026.acl-srw)
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Yuxuan Lu, Bingsheng Yao, Shao Zhang, Yisi Sang, Yun Wang, Hansu Gu, Peng Zhang, Tun Lu, Toby Jia-Jun Li, Dakuo Wang
| Challenge: | Deploying machine learning models in domain-specific scenarios is challenged by data drift and the scarcity of expert annotations. |
| Approach: | They propose a system that combines an LLM, an AL-assisted compact model and an automatic switch module to assist the active learning process. |
| Outcome: | The proposed system achieves 96–98% switch accuracy and outperforms both models used alone. |
EchoVLM: Dynamic Mixture-of-Experts Vision-Language Model for Universal Ultrasound Intelligence (2026.acl-long)
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| Challenge: | Ultrasound is the preferred early cancer screening modality due to non-ionizing radiation, cost-effectiveness, and real-time imaging. |
| Approach: | They propose to use ultrasound-tailored vision-language models with a mixture-of-experts architecture to train ultrasound-specific knowledge across seven anatomical systems. |
| Outcome: | The proposed model outperforms Qwen2-VL by 7.58 BLEU-1 and 3.45 ROUGE-1 points in report generation. |
A Speaker-Aware Co-Attention Framework for Medical Dialogue Information Extraction (2022.emnlp-main)
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Yuan Xia, Zhenhui Shi, Jingbo Zhou, Jiayu Xu, Chao Lu, Yehui Yang, Lei Wang, Haifeng Huang, Xia Zhang, Junwei Liu
| Challenge: | With the development of medical digitization, the extraction and structuring of electronic medical records (EMRs) have become challenging but fundamental tasks. |
| Approach: | They propose a speaker-aware dialogue encoder with multi-task learning which takes the speaker's identity into account and a co-attention fusion network to aggregate the utterance information. |
| Outcome: | The proposed framework outperforms the state-of-the-art methods on the public medical dialogue extraction datasets to demonstrate its superiority. |
OPAL: Ontology-Aware Pretrained Language Model for End-to-End Task-Oriented Dialogue (2023.tacl-1)
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| Challenge: | Existing task-oriented dialogue systems lack ontology-aware pretraining methods for task-orientated dialogue. |
| Approach: | They propose an ontology-aware pretrained language model (OPAL) for end-to-end task-oriented dialogue (TOD) . they propose to pretrain on large-scale contextual text data to bridge the gap between the pretraining method and downstream tasks. |
| Outcome: | The proposed model achieves an exciting boost and obtains competitive performance even without any TOD data on CamRest676 and MultiWOZ benchmarks. |
Low-Resource Language Expansion and Translation Capacity Enhancement for LLM: A Study on the Uyghur (2025.coling-main)
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Kaiwen Lu, Yating Yang, Fengyi Yang, Rui Dong, Bo Ma, Aihetamujiang Aihemaiti, Abibilla Atawulla, Lei Wang, Xi Zhou
| Challenge: | Extensive experiments have shown that our strategy effectively expands the low-resource languages supported by large language models and significantly enhances the model’s translation ability in Uyghur with less parallel data. |
| Approach: | They propose a direct preference optimization based on translation self-evolution to expand low-resource languages into large language models by using Uyghur as an example. |
| Outcome: | The proposed strategy expands low-resource languages supported by large language models and significantly enhances the model’s translation ability in Uyghur with less parallel data. |
Rethinking Multiple-Choice Questions for RLVR: Unlocking Potential via Distractor Design (2026.findings-acl)
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Xu Guo, Qiming Ge, Jian Tong, Kedi Chen, Jin Zhang, Xiaogui Yang, Xuan Gao, Haijun Lv, Zhihui Lu, Yicheng Zou, Qipeng Guo
| Challenge: | Existing approaches to RLVR use multiple-choice questions as verifiable rewards . however, not all tasks provide reliable verification . |
| Approach: | They propose a framework that actively constructs high-quality distractors to block elimination shortcuts and promote deep reasoning. |
| Outcome: | The proposed method significantly improves reasoning capabilities of Large Language Models. |
UniGeo: Unifying Geometry Logical Reasoning via Reformulating Mathematical Expression (2022.emnlp-main)
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| 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. |
Named Entity and Relation Extraction with Multi-Modal Retrieval (2022.findings-emnlp)
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| Challenge: | Existing approaches to name entity recognition and relation extraction are knowledge-based and may not be highly relevant. |
| Approach: | They propose a multi-modal named entity recognition framework that leverages image information to improve the performance of NER and relation extraction. |
| Outcome: | The proposed framework can achieve state-of-the-art on four multi-modal named entity recognition datasets and one multi-module relation extraction dataset. |
PerfCoder: Large Language Models for Interpretable Code Performance Optimization (2026.findings-acl)
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Jiuding Yang, Shengyao Lu, Hongxuan Liu, Shayan Shirahmad Gale Bagi, Zahra Fazel, Tomasz Czajkowski, Di Niu
| Challenge: | Large language models (LLMs) have advanced automatic code generation, but their ability to produce high-performance code remains limited. |
| Approach: | They propose a family of large language models that generate performance-enhanced code through interpretable and customized optimization strategies. |
| Outcome: | The proposed model outperforms existing models on the PIE code performance benchmark and produces interpretable feedback that can guide larger LLMs in a planner–optimizer workflow. |
Empowering Oneida Language Revitalization: Development of an Oneida Verb Conjugator (2024.lrec-main)
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| Challenge: | Oneida is a polysynthetic North American Indigenous language . currently, there are only 45 native speakers in Canada and 102 worldwide . |
| Approach: | They propose to use the Gramble framework to develop a digital Oneida verb conjugator that can demonstrate its users the correct conjugations of verbs and let learners generate practice materials tailored to their unique learning trajectories. |
| Outcome: | The proposed system can demonstrate its users the correct conjugations of verbs and can also let learners generate practice materials tailored to their unique learning trajectories. |
Does Chain-of-Thought Reasoning Really Reduce Harmfulness from Jailbreaking? (2025.findings-acl)
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| Challenge: | Existing jailbreak attacks fail against reasoning models enhanced by Chain-of-Thought (CoT) reasoning. |
| Approach: | They propose a jailbreak method that uses Chain-of-Thought reasoning to reduce harmfulness from jailbreaking. |
| Outcome: | The proposed jailbreak method performs well against open AI models and deepseek-R1 reasoning models. |
Words Worth a Thousand Pictures: Measuring and Understanding Perceptual Variability in Text-to-Image Generation (2024.emnlp-main)
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Raphael Tang, Crystina Zhang, Lixinyu Xu, Yao Lu, Wenyan Li, Pontus Stenetorp, Jimmy Lin, Ferhan Ture
| 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. |
ChildTalk: A Multi-Dialect Chinese Child Speech Corpus with Full-Length Child–Caregiver Conversations for Speech Recognition (2026.findings-acl)
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| Challenge: | Automatic speech recognition (ASR) for children remains challenging due to developmental variability and the scarcity of high-quality corpora. |
| Approach: | They propose a large-scale Chinese child speech corpus that contains 112.5 hours of speech from 498 children and 500 caregivers. |
| Outcome: | The proposed model improves in-domain and cross-domain performance on children's speech. |
Real-time Ad Retrieval via LLM-generative Commercial Intention for Sponsored Search Advertising (2025.emnlp-main)
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| Challenge: | Existing methods for retrieving documents and ads use one-to-few mappings and time-consuming content extraction. |
| Approach: | They propose a framework that leverages LLM-generated commercial intents as an intermediate semantic representation to directly retrieve ads for queries in real-time. |
| Outcome: | The proposed framework has been implemented in a real-world online system, handling daily search volumes in billions. |
Don’t Use English Dev: On the Zero-Shot Cross-Lingual Evaluation of Contextual Embeddings (2020.emnlp-main)
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| Challenge: | Multilingual contextual embeddings have demonstrated state-of-the-art performance in zero-shot cross-lingual transfer learning. |
| Approach: | They show that English dev accuracy makes it difficult to obtain reproducible results . they recommend providing oracle scores alongside zero-shot results if possible . |
| Outcome: | mBERT and XLM have shown strong performance on cross-lingual recognition, text classification, dependency parsing, and other tasks. |
Improving Multi-label Emotion Classification by Integrating both General and Domain-specific Knowledge (D19-55)
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| Challenge: | Text in domains like social media has its own salient characteristics. |
| Approach: | They propose a method to obtain domain knowledge and integrate it with general knowledge to improve emotion classification. |
| Outcome: | The proposed method improves performance of emotion classification on Twitter data. |
From Generation to Judgment: Opportunities and Challenges of LLM-as-a-judge (2025.emnlp-main)
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Dawei Li, Bohan Jiang, Liangjie Huang, Alimohammad Beigi, Chengshuai Zhao, Zhen Tan, Amrita Bhattacharjee, Yuxuan Jiang, Canyu Chen, Tianhao Wu, Kai Shu, Lu Cheng, Huan Liu
| Challenge: | Recent advances in Large Language Models (LLMs) inspire the "LLM-as-a-judge" paradigm . traditional methods of assessment and evaluation fail in dynamic and open-ended scenarios . |
| Approach: | They propose a paradigm where LLMs are leveraged to perform scoring, ranking, or selection for machine learning evaluation scenarios. |
| Outcome: | The proposed model-based judgment and evaluation paradigms are based on large language models and are compared to the current model-driven evaluation paradigm. |
InfoEnh: Towards Multimodal Sentiment Analysis via Information Bottleneck Filter and Optimal Transport Alignment (2024.lrec-main)
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| Challenge: | Existing methods for multi-modal sentiment analysis have been developed to overcome these challenges. |
| Approach: | They propose a method that utilizes a masking technique as the bottleneck for information filtering and integrates all modalities into a common feature space via domain adaptation. |
| Outcome: | Extensive experiments on two benchmark MSA datasets show the proposed method performs better than baselines. |
HisDoc-OCR: Restoring Visual Grounding in MLLMs for Chinese Historical Document OCR (2026.findings-acl)
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| Challenge: | Despite multimodal large language models' strong performance on modern document OCR, their application to historical Chinese texts suffers from severe hallucinations, character fabrication, uncontrolled repetition, and semantic drift. |
| Approach: | They propose a multimodal large language model which restores visual grounding through three synergistic strategies: Layout Injection, First-Occurrence Boost, Self-Distilled Attention Focusing and HisDoc-OCR. |
| Outcome: | The proposed model outperforms general-purpose and OCR-specific models on Chinese historical documents. |
Noisy Positive-Unlabeled Learning with Self-Training for Speculative Knowledge Graph Reasoning (2023.findings-acl)
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Ruijie Wang, Baoyu Li, Yichen Lu, Dachun Sun, Jinning Li, Yuchen Yan, Shengzhong Liu, Hanghang Tong, Tarek Abdelzaher
| Challenge: | State-of-the-art methods fail in speculative reasoning task on knowledge graphs . state-of the-art approaches assume correctness of fact is determined by its presence in KG . |
| Approach: | They propose a speculative reasoning task on real-world knowledge graphs . they propose nPUGraph that estimates correctness of both collected and uncollected facts . |
| Outcome: | The proposed framework improves the robustness of a label posterior-aware graph encoder against false positive links and identifies missing facts to provide high-quality grounds of reasoning. |
Separate the Wheat from the Chaff: A Post-Hoc Approach to Safety Re-Alignment for Fine-Tuned Language Models (2025.findings-acl)
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| Challenge: | Large language models achieve effective safety alignment at the time of release, but fine-tuning often compromises safety mechanisms. |
| Approach: | They propose a method that performs safety realignment for large language models . they identify unsafe delta parameters from the fine-tuned models and recalibrate the retained parameters . |
| Outcome: | The proposed method improves safety performance on safety benchmarks and jailbreak attacks while maintaining their performance on downstream tasks. |
The Linguistic Connectivities Within Large Language Models (2025.findings-acl)
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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
| 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. |
Nonlinear Structural Equation Model Guided Gaussian Mixture Hierarchical Topic Modeling (2023.acl-long)
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| Challenge: | Existing topic models assume that topics are independent and that they are not a tree structure, which complicates the analysis. |
| Approach: | They propose a neural topic model with a Gaussian mixture prior distribution to improve the model’s ability to adapt to sparse data. |
| Outcome: | The proposed model outperforms baseline models on sparse data on a set of widely used datasets and generates more coherent topics and rational topic structures. |
FLARE: Fine-Grained Length-Aware Routing for Resource-Efficient Heterogeneous LLM Serving (2026.acl-long)
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| Challenge: | Existing routers lack fine-grained resource awareness across deployment settings, which degrades efficiency metrics in real-world serving. |
| Approach: | They propose a length-centric, resource-aware multi-LLM routing framework that uses length-based models to estimate per-query latency and cost. |
| Outcome: | Experiments show that FLARE reduces latency and cost by up to 68% and 75% while maintaining competitive accuracy. |
Fair Abstractive Summarization of Diverse Perspectives (2024.naacl-long)
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Yusen Zhang, Nan Zhang, Yixin Liu, Alexander Fabbri, Junru Liu, Ryo Kamoi, Xiaoxin Lu, Caiming Xiong, Jieyu Zhao, Dragomir Radev, Kathleen McKeown, Rui Zhang
| Challenge: | Existing work on summarization metrics and large language models has not explored fair abstractive summarizing. |
| Approach: | They propose four reference-free automatic metrics to measure the differences between target and source perspectives. |
| Outcome: | The proposed methods alleviate fair abstractive summarization on user-generated data. |
MMAU: A Holistic Benchmark of Agent Capabilities Across Diverse Domains (2025.findings-naacl)
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Guoli Yin, Haoping Bai, Shuang Ma, Feng Nan, Yanchao Sun, Zhaoyang Xu, Shen Ma, Jiarui Lu, Xiang Kong, Aonan Zhang, Dian Ang Yap, Yizhe Zhang, Karsten Ahnert, Vik Kamath, Mathias Berglund, Dominic Walsh, Tobias Gindele, Juergen Wiest, Zhengfeng Lai, Xiaoming Simon Wang, Jiulong Shan, Meng Cao, Ruoming Pang, Zirui Wang
| Challenge: | Existing benchmarks focus on specific application scenarios, emphasizing task completion but failing to dissect the underlying skills that drive these outcomes. |
| Approach: | They propose a Massive Multitask Agent Understanding benchmark that evaluates LLMs across five domains and offline tasks. |
| Outcome: | The Massive Multitask Agent Understanding (MMAU) benchmark evaluates models across five domains including Tool-use, Directed Acyclic Graph (DAG) QA, Data Science and Machine Learning coding, Contest-level programming and Mathematics. |
Towards Universal Debiasing for Language Models-based Tabular Data Generation (2025.findings-emnlp)
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| Challenge: | Existing large language models have exacerbated fairness issues in tabular data generation . inherent historical biases in tabulated data cause LLMs to exacerbate fairness problems . |
| Approach: | They propose a universal debiasing framework that minimizes group-level dependencies . it leverages the autoregressive structure and analytic sampling distributions of LLM-based tabular data generators . |
| Outcome: | The proposed framework minimizes group-level dependencies while reducing mutual information between advantaged and protected attributes. |
Direct Large Language Model Alignment Through Self-Rewarding Contrastive Prompt Distillation (2024.acl-long)
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| Challenge: | Existing methods to evaluate preference data without human annotations are difficult . et al., 2022b) is effective for aligning large language models with human expectations . |
| Approach: | They propose a method to evaluate the response preference using output probabilities under contrastive prompts. |
| Outcome: | The proposed method could surpass the RLHF method without human-annotated preference data. |
LayoutLMv2: Multi-modal Pre-training for Visually-rich Document Understanding (2021.acl-long)
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Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou
| Challenge: | Existing pre-training tasks for text and layout are effective in visually-rich document understanding tasks. |
| Approach: | They propose to combine pre-training tasks with a multi-modal model to model interaction between text, layout and image in a single multi-module framework. |
| Outcome: | The proposed model outperforms LayoutLM by a large margin on visual-rich document understanding tasks. |
MASPO: Unifying Gradient Utilization, Probability Mass, and Signal Reliability for Robust and Sample-Efficient LLM Reasoning (2026.acl-long)
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Xiaoliang Fu, Jiaye Lin, Yangyi Fang, Binbin Zheng, Chaowen Hu, Zekai Shao, Cong Qin, Lu Pan, Ke Zeng, Xunliang Cai
| 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. |
MultiConIR: Towards Multi-Condition Information Retrieval (2025.findings-emnlp)
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Xuan Lu, Sifan Liu, Bochao Yin, Yongqi Li, Xinghao Chen, Hui Su, Yaohui Jin, Wenjun Zeng, Xiaoyu Shen
| Challenge: | MultiConIR is a benchmark designed to evaluate retrieval and reranking models under nuanced multi-condition query scenarios. |
| Approach: | They propose a benchmark to evaluate retrieval and reranking models under nuanced multi-condition query scenarios. |
| Outcome: | The proposed benchmark evaluates retrieval and reranking models under nuanced multi-condition query scenarios across five domains. |
JAMDEC: Unsupervised Authorship Obfuscation using Constrained Decoding over Small Language Models (2024.naacl-long)
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| Challenge: | Existing methods to protect the identity and privacy of online authorship are lacking supervision data for diverse authorship and domains. |
| Approach: | They propose an unsupervised inference-time approach to authorship obfuscation that uses a user-controlled, inference time algorithm to oblige the authorship. |
| Outcome: | The proposed method outperforms state-of-the-art methods while performing competitively against a propriety model two orders of magnitudes larger. |
Wrong-of-Thought: An Integrated Reasoning Framework with Multi-Perspective Verification and Wrong Information (2024.findings-emnlp)
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| Challenge: | Chain-of-Thought (CoT) is a key technique for enhancing the performance of Large Language Models. |
| Approach: | They propose a framework that optimizes outputs by utilizing wrong information and multi-perspective verification. |
| Outcome: | The proposed framework surpasses all baselines on 8 datasets and 5 LLMs. |
CityCube: Benchmarking Cross-view Spatial Reasoning on Vision-Language Models in Urban Environments (2026.acl-long)
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Haotian Xu, Yue Hu, Zhengqiu Zhu, Chen Gao, Ziyou Wang, Junreng Rao, Wenhao Lu, Weishi Li, Quanjun Yin, Yong Li
| Challenge: | Existing benchmarks focus on indoor or street settings, overlooking challenges of open-ended urban spaces. |
| Approach: | They propose a benchmark to probe cross-view spatial reasoning capabilities of current VLMs in urban settings. |
| Outcome: | The citycube benchmark examines the performance of current vision-language models in urban environments. |
Deploying Multi-task Online Server with Large Language Model (2025.coling-industry)
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| Challenge: | In the industry, numerous natural language processing tasks are deployed online . traditional approaches tackle each task separately by its own network and pipeline . |
| Approach: | They propose a three-stage multi-task learning framework for large language models . it involves task filtering, fine-tuning on high-resource tasks, and finally fine- tuning on all tasks . |
| Outcome: | The proposed framework reduces up to 90% of overhead while reducing latency and resource usage. |
Generative Entity-to-Entity Stance Detection with Knowledge Graph Augmentation (2022.emnlp-main)
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| Challenge: | a new task is needed to understand the interaction between entities when inferring stances. |
| Approach: | They propose a task that primes models to identify entities in their canonical names and discern stances jointly. |
| Outcome: | The proposed model outperforms strong comparisons by large margins. |
An Intent-based and Annotation-free Method for Duplicate Question Detection in CQA Forums (2023.findings-emnlp)
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| Challenge: | Existing methods for detecting duplicate questions in CQA rely on generic text-pair matching models, overlooking the intent behind the questions. |
| Approach: | They propose a new intent-based duplication detector that leverages intent information to address the problem of duplicate question detection in CQA. |
| Outcome: | The proposed detector leverages the characteristics of CQA forums and extracts training labels to recognize and match intents without human annotation. |
Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models (2024.acl-long)
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| Challenge: | Mixture-of-Experts (MoE) LLMs achieve higher performance with fewer active parameters, but are still difficult to deploy due to their immense parameter sizes. |
| Approach: | They propose expert-level sparsification techniques to enhance the deployment efficiency of large language models by introducing plug-and-play expert pruning and skipping techniques. |
| Outcome: | The proposed methods reduce model sizes and increase inference speed while maintaining satisfactory performance across a wide range of tasks. |
SWE-QA-Pro: A Representative Benchmark and Scalable Training Recipe for Repository-Level Code Understanding (2026.findings-acl)
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Songcheng Cai, Zhiheng Lyu, Yuansheng Ni, Xiangchao Chen, Baichuan Zhou, Shenzhe Zhu, Yi Lu, Haozhe Wang, Chi Ruan, Benjamin Schneider, Weixu Zhang, Xiang Li, Andy Zheng, Yuyu Zhang, Ping Nie, Wenhu Chen
| Challenge: | Existing benchmarks for agentic repository-level code understanding overlook long tail topics and rely on memorized knowledge. |
| Approach: | They propose a repository-level agentic code understanding benchmark that uses long-tail repositories with executable environments to enforce topical balance. |
| Outcome: | Empirically, a Qwen3-8B model trained with the proposed benchmark outperforms GPT-4o by 2.3 points. |
A Lexicon-Based Approach for Detecting Hedges in Informal Text (2020.lrec-1)
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| Challenge: | Existing studies on hedging detection have focused on structured texts and formal communications. |
| Approach: | They propose to use hedging words and phrases to identify tensions between interviewees during a survivor interview to help researchers understand the dynamics of the interview. |
| Outcome: | The proposed algorithm detects sentence-level hedges in informal conversations such as survivor interviews. |
DoCIA: An Online Document-Level Context Incorporation Agent for Speech Translation (2025.findings-acl)
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Xinglin Lyu, Wei Tang, Yuang Li, Xiaofeng Zhao, Ming Zhu, Junhui Li, Yunfei Lu, Min Zhang, Daimeng Wei, Hao Yang, Min Zhang
| Challenge: | Document-level context is crucial for speech translation due to noise from ASR . incorporating document-level contextual information into ST remains a challenge . |
| Approach: | They develop an online framework that integrates document-level context into machine translation . they use document-based modules to integrate document- level context into ST . |
| Outcome: | The proposed framework outperforms baselines in sentence and discourse metrics . it can correct ASR transcription errors and improve translation performance . |
Quantity Tagger: A Latent-Variable Sequence Labeling Approach to Solving Addition-Subtraction Word Problems (P19-1)
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| Challenge: | Existing methods to solve arithmetic word problems require additional annotations. |
| Approach: | They propose a method that automatically discovers hidden mathematical relations by tagging each quantity with a sign corresponding to one type of mathematical operation. |
| Outcome: | Empirical results show that the proposed method achieves 5 and 8 points of accuracy gains on two datasets compared to prior approaches. |
Take a Closer Look at Multilinguality! Improve Multilingual Pre-Training Using Monolingual Corpora Only (2023.findings-emnlp)
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| Challenge: | Recent studies have demonstrated remarkable cross-lingual capability of pre-trained language models . however, semantic alignments may be the reason behind such capability but remain under-explored. |
| Approach: | They propose token-level and semantic-level code-switched masked language modeling to improve cross-lingual interactions over mono-mPLMs without parallel sentences. |
| Outcome: | The proposed method outperforms mono-mPLMs on natural language understanding and unsupervised machine translation tasks. |
TempCompass: Do Video LLMs Really Understand Videos? (2024.findings-acl)
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| Challenge: | Existing benchmarks on video large language models lack a comprehensive feedback on temporal perception ability . current models cannot distinguish between different temporal aspects and are limited in task formats . |
| Approach: | They propose a benchmark to evaluate temporal perception ability of video large language models . they construct conflicting videos that share the same static content but differ in a specific temporal aspect . |
| Outcome: | The proposed benchmarks show that video large language models exhibit poor temporal perception ability. |
Multimodal Procedural Planning via Dual Text-Image Prompting (2024.findings-emnlp)
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| Challenge: | Embodied agents have demonstrated performance in following instructions informed by texts and images . however, the potential of models providing useful guidelines for humans to complete tasks remains underexplored . |
| Approach: | They propose a multimodal procedural planning task that generates paired text-image plans . this task provides more complementary and informative guidance than unimodal plans a . authors propose modality prompting methods that leverage zero-shot reasoning ability . |
| Outcome: | The proposed method improves the interaction in dual modalities and provides more information than unimodal plans. |
From log 𝜋 to 𝜋: Taming Divergence in Soft Clipping via Bilateral Decoupled Decay of Probability Gradient Weight (2026.acl-long)
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Xiaoliang Fu, Jiaye Lin, Yangyi Fang, Chaowen Hu, Cong Qin, Zekai Shao, Binbin Zheng, Lu Pan, Ke Zeng
| 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. |
CNNSum: Exploring Long-Context Summarization with Large Language Models in Chinese Novels (2025.findings-acl)
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| Challenge: | Currently, long-context summarization mainly relies on memory ability. |
| Approach: | They propose a multi-scale long-context summarization benchmark based on Chinese novels . they use human-driven annotations to analyze long-constituency models . |
| Outcome: | The proposed benchmark features human-driven annotations across four subsets with lengths ranging from 16k to 128k. |
A New Task and Dataset on Detecting Attacks on Human Rights Defenders (2023.findings-acl)
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| Challenge: | Existing datasets for event extraction cannot extract information from textual sources. |
| Approach: | They propose to use crowdsourced annotations on 500 online news articles to train and evaluate baseline models to predict annotated characteristics. |
| Outcome: | The proposed dataset includes crowdsourced annotations on 500 online news articles and includes fine-grained information about the type and location of attacks, as well as information about victims. |
READoc: A Unified Benchmark for Realistic Document Structured Extraction (2025.findings-acl)
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Zichao Li, Aizier Abulaiti, Yaojie Lu, Xuanang Chen, Jia Zheng, Hongyu Lin, Xianpei Han, Shanshan Jiang, Bin Dong, Le Sun
| 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. |
Measuring Correlation-to-Causation Exaggeration in Press Releases (2020.coling-main)
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| Challenge: | Recent studies have found that press releases are a major source of exaggeration in science communication, which is later spread to mainstream media. |
| Approach: | They propose an NLP approach to identify exaggerated causal claims in health press releases that report on observational studies. |
| Outcome: | The proposed approach can identify causal claims in press releases that report on observational studies. |
LearnAct: Few-Shot Mobile GUI Agent with a Unified Demonstration Benchmark (2026.findings-acl)
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Guangyi Liu, Pengxiang Zhao, Liang Liu, Zhiming Chen, Yuxiang Chai, Yaozhen Liang, WenHao Wang, Siheng Chen, Zhengxi Lu, Shuai Ren, Hao Wang, Shibo He, Yong Liu, Wenchao Meng
| Challenge: | Mobile GUI agents show promise in automating tasks but face significant generalization challenges in long-tail scenarios. |
| Approach: | They propose a benchmark framework for mobile GUI agents that measures the performance of GUI agents by analyzing their performance. |
| Outcome: | The LearnGUI benchmark outperforms existing methods in offline and online evaluations and demonstrates consistent gains across model architectures. |
Plan Dynamically, Express Rhetorically: A Debate-Driven Rhetorical Framework for Argumentative Writing (2025.emnlp-main)
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| Challenge: | Argumentative essay generation (AEG) is a complex task that requires advanced semantic understanding, logical reasoning, and organized integration of perspectives. |
| Approach: | They propose a debate-driven rhetorical framework for argumentative writing that integrates Bitzer’s rhetorical situation theory to improve logical depth, argumentative diversity, and rhetorical persuasiveness. |
| Outcome: | The proposed framework improves logical depth, argumentative diversity, and rhetorical persuasiveness over existing state-of-the-art models. |
Reasoning with Latent Structure Refinement for Document-Level Relation Extraction (2020.acl-main)
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| Challenge: | Existing methods for document-level relation extraction capture non-local interactions but are not able to capture rich non-linguistic interactions. |
| Approach: | They propose a document-level relation extraction model that empowers relational reasoning across sentences by automatically inducing the latent document- level graph. |
| Outcome: | The proposed model achieves an F1 score of 59.05 on a large-scale document-level dataset (DocRED), significantly improving over the previous results. |
YuLan-Mini: Pushing the Limits of Open Data-efficient Language Model (2025.acl-long)
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Hu Yiwen, Huatong Song, Jie Chen, Jia Deng, Jiapeng Wang, Kun Zhou, Yutao Zhu, Jinhao Jiang, Zican Dong, Yang Lu, Xu Miao, Xin Zhao, Ji-Rong Wen
| Challenge: | prevailing pre-training approaches for large language models involve several complexities. |
| Approach: | They propose a low-cost training recipe and a robust optimization approach to mitigate training instability . they also propose synthesis, curriculum, and data selection pipelines to integrate data . |
| Outcome: | The proposed model achieves top-tier performance among models with similar parameter scale . it is comparable to industry-leading models that require significantly more data . |
FIPO: Free-form Instruction-oriented Prompt Optimization with Preference Dataset and Modular Fine-tuning Schema (2025.coling-main)
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| Challenge: | naive prompts can enhance the task performance of large language models, but they are resource-intensive. |
| Approach: | They propose an automatic prompt optimization method that refines naive prompts according to task outputs from in-box testing models. |
| Outcome: | The proposed method is based on a large-scale dataset and performed fairly across multiple 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
| 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. |
GAMEBoT: Transparent Assessment of LLM Reasoning in Games (2025.acl-long)
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| 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. |
Character-Based Models for Adversarial Phone Extraction: Preventing Human Sex Trafficking (D19-55)
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Nathanael Chambers, Timothy Forman, Catherine Griswold, Kevin Lu, Yogaish Khastgir, Stephen Steckler
| Challenge: | Illicit activity on the Web often obscures information between client and seller, such as the seller’s phone number. |
| Approach: | They propose to use a dataset to model adversarial noise in a text extraction system and propose a visual character language model to interpret unseen unicode characters. |
| Outcome: | The proposed model improves number recognition by 89% over a CRF with a CNN and shows that unicode characters can be translated to unicoding. |
UniSpeaker: A Unified Approach for Multimodality-driven Speaker Generation (2025.findings-emnlp)
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| Challenge: | a new framework for speaker generation is proposed to enable multimodal speaker generation . multimodal cues such as visual appearance, textual descriptions, and other biometric signals are still in its early stages. |
| Approach: | a new framework is proposed to enable multimodal speaker generation . the framework uses self-distillation to apply speaker disentanglement to speech generation a model is developed . |
| Outcome: | The proposed framework is the first to support unified voice generation from arbitrary modality combinations. |
The Essence of Contextual Understanding in Theory of Mind: A Study on Question Answering with Story Characters (2025.acl-long)
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Chulun Zhou, Qiujing Wang, Mo Yu, Xiaoqian Yue, Rui Lu, Jiangnan Li, Yifan Zhou, Shunchi Zhang, Jie Zhou, Wai Lam
| Challenge: | Theory-of-Mind (ToM) is a psychological capability that allows humans to understand and interpret the mental states of others. |
| Approach: | They propose a CharToM-QA benchmark to assess the importance of comprehensive contextual understanding about personal backgrounds in ToM. |
| Outcome: | The proposed model outperforms existing models on 1,035 ToM questions based on classic novels and shows that educated participants perform better when they have read the novels than non-educated participants. |
Evidence-Aligned Entity Verification for Hallucination Detection in Retrieval-Augmented Generation (2026.findings-acl)
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| Challenge: | Existing methods for hallucination detection depend on internal signals like uncertainty and self-consistency checks to identify unreliable outputs. |
| Approach: | They propose a retrieval-augmented generation method to enhance hallucination detection by addressing information updating challenges. |
| Outcome: | The proposed method improves on existing methods with strong generalization capabilities. |
DeMAC: Enhancing Multi-Agent Coordination with Dynamic DAG and Manager-Player Feedback (2025.findings-emnlp)
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| Challenge: | Multi-agent systems (MAS) powered by large language models struggle to adapt to evolving task dependencies and to handle uncertainties. |
| Approach: | They propose a Dynamic Environment-Aware Manager-Player Agents Coordination framework that enhances multi-agent coordination through long-term strategic planning. |
| Outcome: | The proposed framework outperforms traditional reinforcement learning and human-agent collaboration in the Overcooked simulation. |
Fine-tuning Pre-trained Language Models for Few-shot Intent Detection: Supervised Pre-training and Isotropization (2022.naacl-main)
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| Challenge: | Recent studies show that fine-tuning pre-trained language models with a small set of labeled utterances in a supervised manner is helpful, but it yields an anisotropic feature space, which may suppress the expressive power of the semantic representations. |
| Approach: | They propose to regularize supervised pre-training towards isotropy by contrastive learning and correlation matrix regularizers. |
| Outcome: | The proposed methods improve supervised pre-training by regularizing the feature space towards isotropy. |
OmAgent: A Multi-modal Agent Framework for Complex Video Understanding with Task Divide-and-Conquer (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have expanded their capabilities to multimodal contexts, including comprehensive video understanding. |
| Approach: | They propose to store and retrieve relevant video frames for specific queries and a Divide-and-Conquer loop capable of autonomous reasoning. |
| Outcome: | The proposed model efficiently stores and retrieves relevant video frames for specific queries, preserving the detailed content of videos. |
Entity-Aware Abstractive Multi-Document Summarization (2021.findings-acl)
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| Challenge: | Existing models for multidocument summarization do not focus on explicitly modeling the underlying semantic information across documents. |
| Approach: | They propose an entityaware model for abstractive multi-document summarization that augments the classical Transformer-based encoder-decoder framework with a heterogeneous graph consisting of text units and entities as nodes. |
| Outcome: | The proposed model can deal with saliency and redundancy issues explicitly and can be used with pre-trained language models, arriving at improved performance. |
Let’s Simplify Step by Step: Guiding LLM Towards Multilingual Unsupervised Proficiency-Controlled Sentence Simplification (2026.findings-eacl)
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| Challenge: | Large language models demonstrate limited capability in proficiency-controlled sentence simplification when simplifying across large readability levels. |
| Approach: | They propose a framework that decomposes complex simplifications into manageable steps through dynamic path planning, semantic-aware exemplar selection, and chain-of-thought generation with conversation history for coherent reasoning. |
| Outcome: | The proposed framework reduces computational steps while improving simplification effectiveness on five languages across two benchmarks. |
Neural Graph Matching Networks for Chinese Short Text Matching (2020.acl-main)
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| Challenge: | Chinese word segmentation can be erroneous, ambiguous or inconsistent, causing performance problems. |
| Approach: | They propose a sentence matching framework that uses paired word lattices as input instead of a character sequence. |
| Outcome: | The proposed framework outperforms the state-of-the-art short text matching models on two Chinese datasets. |
Structured Dialogue Policy with Graph Neural Networks (C18-1)
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| Challenge: | Recent advances focus on improving DRL-based dialogue policy optimization. |
| Approach: | They propose to design a graph neural network structure that is better suited for dialogue management. |
| Outcome: | The proposed approach outperforms state-of-the-art approaches in 18 tasks of the PyDial benchmark. |
Multi-View Reasoning: Consistent Contrastive Learning for Math Word Problem (2022.findings-emnlp)
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| Challenge: | Existing methods for generating complex semantics and diverse equations are limited by a fixed view. |
| Approach: | They propose a multi-view consistent contrastive learning approach that decouples human reasoning into two independent but consistent views. |
| Outcome: | The proposed approach significantly outperforms existing baselines on complex problems on multiple languages. |
Affection Driven Neural Networks for Sentiment Analysis (2020.lrec-1)
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| Challenge: | Existing deep neural network models lack mechanisms to highlight important sentiment terms. |
| Approach: | They propose a method to incorporate affective knowledge into deep neural network models by mapping affective influence vectors to an affective impact value and integrating them into long-term memory models to highlight affective terms. |
| Outcome: | The proposed approach improves on three large datasets by 1.0% to 1.5% on the benchmark datasets. |
Attention Head Masking for Inference Time Content Selection in Abstractive Summarization (2021.naacl-main)
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| Challenge: | Existing studies show that multi-heads attentions at the same layer collectively guide the summarization. |
| Approach: | They propose an inference-time attention head masking mechanism that works on encoder-decoder attentions to pinpoint salient content at inference time. |
| Outcome: | The proposed technique outperforms state-of-the-art models on CNN/DailyMail and New York Times datasets and is data-efficient. |
Beyond Static Artifacts: An Evolutionary Framework for Synthetic Claim Generation (2026.acl-long)
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| 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 . |
Harnessing LLMs for Temporal Data - A Study on Explainable Financial Time Series Forecasting (2023.emnlp-industry)
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| Challenge: | Recent advances in machine learning and artificial intelligence have opened up numerous opportunities and challenges in financial time series forecasting. |
| Approach: | They propose to use Large Language Models for explainable financial time series forecasting to leverage cross-sequence information and extract insights from text and price time series. |
| Outcome: | The proposed model outperforms ARMA-GARCH and gradient-boosting tree models while underperforming on other models. |
SlotRefine: A Fast Non-Autoregressive Model for Joint Intent Detection and Slot Filling (2020.emnlp-main)
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| Challenge: | Slot filling and intent detection are two main tasks in spoken language understanding systems. |
| Approach: | They propose a non-autoregressive slot filling model with two-pass iteration mechanism to handle uncoordinated slots problem. |
| Outcome: | The proposed model significantly outperforms previous models in slot filling task while speeding up decoding. |
Neural Segmental Hypergraphs for Overlapping Mention Recognition (D18-1)
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| Challenge: | Existing models that assume that mentions are non-overlapping spans in text may not perform well in practice. |
| Approach: | They propose a segmental hypergraph representation to model overlapping entity mentions that are prevalent in many practical datasets. |
| Outcome: | The proposed representation achieves state-of-the-art performance in three benchmark datasets annotated with overlapping mentions. |
Constructing Your Model’s Value Distinction: Towards LLM Alignment with Anchor Words Tuning (2025.findings-emnlp)
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| Challenge: | a study of large language models (LLMs) shows that they can generate outputs that are honest, positive, harmless, etc. |
| Approach: | They propose a method that amplifies logits difference between positive and negative tokens . they propose to use the logits gap to generate positive and positive tokens after alignment . |
| Outcome: | The proposed method achieves effective alignment, but requires fewer computational resources compared to training-time alignment methods. |
Precedent-Enhanced Legal Judgment Prediction with LLM and Domain-Model Collaboration (2023.emnlp-main)
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Yiquan Wu, Siying Zhou, Yifei Liu, Weiming Lu, Xiaozhong Liu, Yating Zhang, Changlong Sun, Fei Wu, Kun Kuang
| Challenge: | Recent advances in deep learning have enabled a variety of techniques to be used to solve the LJP task. |
| Approach: | They propose a framework that leverages the strength of both LLMs and domain-specific models in the context of precedents. |
| Outcome: | The proposed framework leverages the strength of both LLM and domain models in the context of precedents. |
Can LLM Agents Simulate Multi-Turn Human Behavior? Evidence from Real Online Customer Behavior Data (2026.acl-long)
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Yuxuan Lu, Jing Huang, Yan Han, Bingsheng Yao, Sisong Bei, Yaochen Xie, Yisi Sang, Qi He, Dakuo Wang
| Challenge: | Recent research shows that LLM Agents can generate “believable” human behaviors via prompt-only methods, leaving open questions of whether they can accurately generate step-by-step actions in multi-turn interaction tasks. |
| Approach: | They propose to use shopping data to evaluate LLMs' ability to accurately generate step-by-step actions in a multi-turn interaction task. |
| Outcome: | The proposed model achieves 17.26% action generation accuracy and 33.86% F1 score on final purchase prediction, representing improvements of 5.4% and 13.85% over baselines. |
Unexpected Phenomenon: LLMs’ Spurious Associations in Information Extraction (2024.findings-acl)
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Weiyan Zhang, Wanpeng Lu, Jiacheng Wang, Yating Wang, Lihan Chen, Haiyun Jiang, Jingping Liu, Tong Ruan
| Challenge: | Information extraction (IE) tasks require a limited number of example instructions to achieve effective performance. |
| Approach: | They propose two strategies to find spurious associations in large language models (LLMs) they use forward label extension and backward label validation to leverage extended labels to improve model performance. |
| Outcome: | The proposed methods improve performance on Chinese and English datasets and 9.55%, 11.42%, and 21.27% in F1 scores on SciERC, ACE05, and DuEE datasets. |
Benchmarking Language Model Creativity: A Case Study on Code Generation (2025.naacl-long)
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| Challenge: | Recent studies on LLM creativity evaluation focus on open-ended generation tasks . however, the degree to which LLMs possess and utilize creativity for problem-solving remains unclear . |
| Approach: | They propose a framework for quantifying LLM creativity that incorporates design ingredients . they introduce DENIAL PROMPTING which pushes LLMs to develop more creative solutions . |
| Outcome: | The proposed framework quantifies creativity in LLMs on Codeforces problems . it also finds that even the most creative model fails to demonstrate human-like creativity . |
Crossroads of Optimization under Uncertainty: How to Choose the Optimal Model (2026.findings-acl)
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| Challenge: | Existing approaches to Optimization under Uncertainty (OuU) have inherent limitations and advantages. |
| Approach: | They propose a framework that automates the modeling and solving of six types of uncertainty models and generates mapping pairs to explore the potential relationship between optimization problems and optimal models. |
| Outcome: | The proposed framework achieves superior performance even on specific model types, with correlation analysis showing that data scale and specific scenario significantly influence model selection. |
AfriMTE and AfriCOMET: Enhancing COMET to Embrace Under-resourced African Languages (2024.naacl-long)
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Jiayi Wang, David Adelani, Sweta Agrawal, Marek Masiak, Ricardo Rei, Eleftheria Briakou, Marine Carpuat, Xuanli He, Sofia Bourhim, Andiswa Bukula, Muhidin Mohamed, Temitayo Olatoye, Tosin Adewumi, Hamam Mokayed, Christine Mwase, Wangui Kimotho, Foutse Yuehgoh, Anuoluwapo Aremu, Jessica Ojo, Shamsuddeen Muhammad, Salomey Osei, Abdul-Hakeem Omotayo, Chiamaka Chukwuneke, Perez Ogayo, Oumaima Hourrane, Salma El Anigri, Lolwethu Ndolela, Thabiso Mangwana, Shafie Mohamed, Hassan Ayinde, Oluwabusayo Awoyomi, Lama Alkhaled, Sana Al-azzawi, Naome Etori, Millicent Ochieng, Clemencia Siro, Njoroge Kiragu, Eric Muchiri, Wangari Kimotho, Toadoum Sari Sakayo, Lyse Naomi Wamba, Daud Abolade, Simbiat Ajao, Iyanuoluwa Shode, Ricky Macharm, Ruqayya Iro, Saheed Abdullahi, Stephen Moore, Bernard Opoku, Zainab Akinjobi, Abeeb Afolabi, Nnaemeka Obiefuna, Onyekachi Ogbu, Sam Ochieng’, Verrah Otiende, Chinedu Mbonu, Yao Lu, Pontus Stenetorp
| Challenge: | Recent advances in machine translation (MT) have focused on scaling multilingual machine translation models and evaluation data to hundreds of languages, including multiple under-resourced languages. |
| Approach: | They propose to use n-gram matching metrics to measure progress in multilingual machine translation to 13 typologically diverse African languages to create high-quality human evaluation data with simplified MQM guidelines. |
| Outcome: | The proposed metrics have a higher correlation with human judgments than n-gram matching metrics such as BLEU and METEOR. |
WebAgent-R1: Training Web Agents via End-to-End Multi-Turn Reinforcement Learning (2025.emnlp-main)
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Zhepei Wei, Wenlin Yao, Yao Liu, Weizhi Zhang, Qin Lu, Liang Qiu, Changlong Yu, Puyang Xu, Chao Zhang, Bing Yin, Hyokun Yun, Lihong Li
| Challenge: | Existing work on reinforcement learning has focused on single-turn tasks such as solving math problems. |
| Approach: | They propose a framework that learns directly from online interactions by asynchronously generating diverse trajectories, guided by binary rewards depending on task success. |
| Outcome: | Experiments on the WebArena-Lite benchmark show that the framework outperforms state-of-the-art methods and strong proprietary models. |
QSpec: Speculative Decoding with Complementary Quantization Schemes (2025.emnlp-main)
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| Challenge: | Quantization is widely adopted to accelerate inference and reduce memory consumption in large language models. |
| Approach: | They propose a quantization paradigm that decouples efficiency from quality by integrating two complementary schemes via speculative decoding. |
| Outcome: | The proposed approach achieves 1.64x speedup without quality degradation and outperforms state-of-the-art speculative decoding methods by 1.55x in batched settings. |
Imagination-Augmented Natural Language Understanding (2022.naacl-main)
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| Challenge: | Existing methods for Natural Language Understanding focus on textual signals, which hinders models from learning efficiently from limited data samples. |
| Approach: | They propose an Imagination-Augmented Cross-modal Encoder to solve natural language understanding tasks from a novel learning perspective. |
| Outcome: | The proposed learning paradigm bridges the gap between human and agent language understanding in both linguistic and perceptual procedures. |
Sampling Better Negatives for Distantly Supervised Named Entity Recognition (2023.findings-acl)
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| Challenge: | Existing supervised named entity recognition approaches rely on human annotations. |
| Approach: | They propose a method to select negative samples with high similarities with positive samples . they propose to use automatically labeled training data instead of human annotations . |
| Outcome: | The proposed method achieves consistent performance improvements on four distantly supervised NER datasets. |
Reward Modeling Requires Automatic Adjustment Based on Data Quality (2024.findings-emnlp)
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Binghai Wang, Rui Zheng, Lu Chen, Zhiheng Xi, Wei Shen, Yuhao Zhou, Dong Yan, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Reinforcement Learning from Human Feedback (RLHF) is a method for aligning language models with human values. |
| Approach: | They propose a method that automatically adjusts reward modeling based on data quality . they use preference data to train a reward model that is more aligned with human values . |
| Outcome: | The proposed method stabilizes reward model training and significantly improves alignment performance on human preference datasets. |
Plot2Code: A Comprehensive Benchmark for Evaluating Multi-modal Large Language Models in Code Generation from Scientific Plots (2025.findings-naacl)
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| Challenge: | Multi-modal Large Language Models have shown remarkable progress in visual contexts, yet their ability to convert visual figures into executable code remains underexplored. |
| Approach: | They propose to use a set of visual coding metrics to assess MLLMs' visual . pass rate, text-match ratio, and GPT-4V rating judgement to assess the quality of generated code and rendered images. |
| Outcome: | The proposed benchmark includes 132 high-quality matplotlib plots across six plot types, as well as 150 and 86 plots from Python’s and R’s plotly libraries respectively, totaling 368 plots. |
FlexGuard: Continuous Risk Scoring for Strictness-Adaptive LLM Content Moderation (2026.acl-long)
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| Challenge: | Existing guardrail models for content moderation assume a fixed definition of harmfulness, but enforced strictness varies across platforms and evolves over time, resulting in brittle moderators. |
| Approach: | They propose a strictness-adaptive LLM moderation benchmark that enables controlled evaluation under multiple strictness regimes. |
| Outcome: | The proposed moderator performs better under one regime and under another, and is more robust under varying strictness. |
Diverging Divergences: Examining Variants of Jensen Shannon Divergence for Corpus Comparison Tasks (2020.lrec-1)
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| Challenge: | Jensen-Shannon divergence (JSD) is a distribution similarity measurement widely used in natural language processing. |
| Approach: | They propose to use a weighted version of Jensen-Shannon divergence to compare corpora . they argue this weighting is unnecessary and can lead to misleading results . |
| Outcome: | The proposed weighting is unnecessary and can lead to misleading results. |
Olá, Bonjour, Salve! XFORMAL: A Benchmark for Multilingual Formality Style Transfer (2021.naacl-main)
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| Challenge: | XFORMAL benchmarks formal reformulations of informal text in Brazilian Portuguese, French, and Italian . most work on style transfer within English, while covering different languages has received disproportional interest. |
| Approach: | They create a benchmark of multiple formal reformulations of informal text in Brazil, Brazil, and Italy. |
| Outcome: | XFORMAL benchmarks formal reformulations of informal text in Brazilian Portuguese, French, and Italian . results show that state-of-the-art approaches perform close to simple baselines . |
Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction (2021.acl-long)
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| 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. |
Can LLM Watermarks Robustly Prevent Unauthorized Knowledge Distillation? (2025.acl-long)
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| Challenge: | Large Language Model (LLM) watermarking is radioactive and enables the detection of watermarks inherited by student models when trained on the outputs of watermarked teacher models. |
| Approach: | They propose two types of watermark removal attacks that allow student models to perform untraceable knowledge distillation while avoiding watermark inheritance. |
| Outcome: | The proposed attacks eliminate inherited watermarks while maintaining knowledge transfer efficiency and low computational overhead. |
Doctor Recommendation in Online Health Forums via Expertise Learning (2022.acl-long)
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| Challenge: | Currently, manual doctor allocations are used to handle large volumes of queries, limiting the efficiency to help patients in sheer quantities. |
| Approach: | They propose to use patient queries to model doctor recommendation using their profiles and past dialogues to estimate their capabilities. |
| Outcome: | The proposed model outperforms baseline models on a Chinese online health forum, outperforming baseline models. |
Learning Confidence for Transformer-based Neural Machine Translation (2022.acl-long)
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| Challenge: | A well-calibrated confidence estimate is not sufficient for neural machine translation (NMT) where probabilities from softmax distribution fail to describe when the model is probably mistaken. |
| Approach: | They propose an unsupervised confidence estimate learning jointly with the training of a neural machine translation model to quantify confidence. |
| Outcome: | The proposed model outperforms standard label smoothing and can predict failures in two real-world scenarios. |
View Dialogue in 2D: A Two-stream Model in Time-speaker Perspective for Dialogue Summarization and beyond (2022.coling-1)
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| Challenge: | Existing models for dialogue summarization focus on document summarizing on time and speaker-centered points, but this approach is limited in understanding the dialogue. |
| Approach: | They propose a 2D view of dialogue based on a time-speaker perspective where the time and speaker streams of dialogue can be obtained as strengthened input. |
| Outcome: | The proposed model outperforms existing models on the QMSum dataset and improves summary faithfulness and human evaluation. |
AutoRAG-HP: Automatic Online Hyper-Parameter Tuning for Retrieval-Augmented Generation (2024.findings-emnlp)
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Jia Fu, Xiaoting Qin, Fangkai Yang, Lu Wang, Jue Zhang, Qingwei Lin, Yubo Chen, Dongmei Zhang, Saravan Rajmohan, Qi Zhang
| 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. |
Speculative Contrastive Decoding (2024.acl-short)
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| Challenge: | Large language models (LLMs) exhibit exceptional performance in language tasks, yet their auto-regressive inference is limited due to high computational requirements and is sub-optimal due to the exposure bias. |
| Approach: | They propose a decoding approach that leverages predictions from smaller language models to achieve both decoding acceleration and quality improvement. |
| Outcome: | The proposed method achieves both decoding acceleration and quality improvement on four diverse language tasks. |
ReGLA: Refining Gated Linear Attention (2025.naacl-long)
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| Challenge: | Recent advances in Large Language Models (LLMs) are known for their computational and storage requirements due to the quadratic computation complexity of softmax attention. |
| Approach: | They propose to reduce the quadratic computation complexity of softmax attention by using feature maps, normalization and the gating mechanism to improve performance. |
| Outcome: | The proposed model outperforms existing gated linear attention models in extensive tasks including training from scratch and post-linearization with continual pre-training. |
SimANS: Simple Ambiguous Negatives Sampling for Dense Text Retrieval (2022.emnlp-industry)
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Kun Zhou, Yeyun Gong, Xiao Liu, Wayne Xin Zhao, Yelong Shen, Anlei Dong, Jingwen Lu, Rangan Majumder, Ji-rong Wen, Nan Duan
| Challenge: | Existing methods for sapping negatives from large document pool suffer from the uninformative or false negative problem. |
| Approach: | They propose a method to sample negatives from a large document pool using a new sampling probability distribution. |
| Outcome: | The proposed method can be used to sample more ambiguous negatives on four public and one industry datasets. |
Discrete Argument Representation Learning for Interactive Argument Pair Identification (2021.naacl-main)
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| Challenge: | Existing research on monological argumentation covers claims generation, argument structure prediction, and essay scoring. |
| Approach: | They propose to identify argument pairs from two posts with opposite stances to a certain topic. |
| Outcome: | The proposed framework outperforms competing models on a large-scale dataset . it also proves that it is useful for analyzing argument pairs from two posts . |
ASL STEM Wiki: Dataset and Benchmark for Interpreting STEM Articles (2024.emnlp-main)
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Kayo Yin, Chinmay Singh, Fyodor Minakov, Vanessa Milan, Hal Daumé Iii, Cyril Zhang, Alex Lu, Danielle Bragg
| Challenge: | Deaf and hard-of-hearing students face significant barriers in accessing STEM education due to the scarcity of STEM resources in signed languages. |
| Approach: | They develop models to identify fingerspelled words in American Sign Language (ASL) given an English sentence and a video, the model detects which English phrase is fingerspelled in the clip. |
| Outcome: | ASL STEM Wiki is the first continuous signing dataset focused on STEM . it detects fingerspelled words and queries them for appropriate signs to suggest to interpreters. |
Learning Like Humans: Advancing LLM Reasoning Capabilities via Adaptive Difficulty Curriculum Learning and Expert-Guided Self-Reformulation (2025.emnlp-main)
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| 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. |
Web Sitemap Knowledge Can Enhance Autonomous Browsing (2026.findings-acl)
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Yuyao Zhang, Hongyu Lu, Jiajie Jin, Hongjin Qian, Shiyu Li, Zhao Yang, Yutao Zhu, Ji-Rong Wen, Zhicheng Dou
| Challenge: | Existing web agents suffer from limited robustness, efficiency and task success due to lack of structural understanding of websites and lack of browsing priors in pre-trained models. |
| Approach: | They propose an agent-oriented sitemap protocol that integrates structured website knowledge into web agents. |
| Outcome: | The proposed agent-oriented sitemap improves robustness, efficiency and effectiveness without extra training. |
FaStFact: Faster, Stronger Long-Form Factuality Evaluations in LLMs (2025.findings-emnlp)
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Yingjia Wan, Haochen Tan, Xiao Zhu, Xinyu Zhou, Zhiwei Li, Qingsong Lv, Changxuan Sun, Jiaqi Zeng, Yi Xu, Jianqiao Lu, Yinhong Liu, Zhijiang Guo
| Challenge: | Prior evaluation pipelines fail to evaluate factuality of long-form LLMs due to inefficiency and costly human assessment. |
| Approach: | They propose a fast and strong evaluation pipeline that can evaluate factuality of long-form LLMs . they propose 'faStFact' to reduce cost of web searching and inference calling . |
| Outcome: | The proposed evaluation pipeline achieves highest alignment with human evaluation and efficiency among existing baselines. |
Decoupled Dialogue Modeling and Semantic Parsing for Multi-Turn Text-to-SQL (2021.findings-acl)
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| Challenge: | Recent work on Text-to-SQL for multi-turn dialogue has attracted great interest . current approaches mostly employ end-to end models and face data sparsity problems . |
| Approach: | They propose a decoupled multi-turn text-to-SQL framework where dialogue context is explicitly solved by an utterance rewrite model and a single-turn Text-toSQl parser are proposed. |
| Outcome: | The proposed method outperforms existing models on SParC and CoSQL datasets without annotated in-domain data. |
Role-Guided Annotation and Prototype-Aligned Representation Learning for Historical Literature Sentiment Classification (2025.findings-emnlp)
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| Challenge: | Prior work focused on using sentiment lexicons or leveraging large language models for annotation . lexiconics are often unavailable for historical texts due to limited linguistic resources . |
| Approach: | They propose a role-guided annotation strategy that prompts LLMs to simulate historical perspectives when labeling sentiment. |
| Outcome: | The proposed method outperforms state-of-the-art baselines across historical literature datasets. |
Toward Human-Like Evaluation for Natural Language Generation with Error Analysis (2023.acl-long)
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| Challenge: | Pre-trained language models (PLMs) have been used to evaluate language generation tasks . pretrained error analysis can be used to refine the generated sentence toward higher confidence . |
| Approach: | They propose to combine pretrained language model based metrics with human-like error analysis to improve sentence confidence. |
| Outcome: | The proposed method outperforms top-scoring metrics in 19/25 settings. |
A Query-Response Framework for Whole-Page Complex-Layout Document Image Translation with Relevant Regional Concentration (2025.findings-acl)
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| Challenge: | Existing methods for document image translation rely on the vanilla encoder-decoder paradigm . a novel dynamic aggregation mechanism is designed to enhance the text semantics in query features toward translation. |
| Approach: | They propose a Query-Response DIT framework that reformulates the DIT task into a parallel response/translation process of multiple queries. |
| Outcome: | The proposed framework improves translation quality on four translation directions on three benchmarks. |
Answer Convergence as a Signal for Early Stopping in Reasoning (2025.emnlp-main)
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| Challenge: | a systematic study suggests that chain-of-thought prompting is unnecessary for producing correct answers. |
| Approach: | They propose three inference-time strategies to improve model efficiency by boosting end-of-reasoning signals and early stopping . they propose a method that learns when to stop based on internal activations . |
| Outcome: | The proposed methods reduce token usage with little or no accuracy drop on natural questions . the proposed methods also reduce tokens by over 40% on naturalquestions . |
Enhancing Taiwanese Hokkien Dual Translation by Exploring and Standardizing of Four Writing Systems (2024.lrec-main)
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| Challenge: | Currently, machine translation systems cater to high-resource languages (HRLs), while low-resourced languages (LRLs) like Taiwanese Hokkien are relatively under-explored. |
| Approach: | They propose to use a pre-trained LLaMA 2-7B model specialized in Traditional Mandarin Chinese to leverage orthographic similarities between Taiwanese Hokkien Han and Traditional Mandarin China. |
| Outcome: | The proposed model bridges the gap between Taiwanese Hokkien and other low-resource languages by using a pre-trained LLaMA 2-7B model and a monolingual corpus. |
DUET: Joint Exploration of User–Item Profiles in Recommendation System (2026.findings-acl)
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Yue Chen, Yifei Sun, Lu Wang, Fangkai Yang, Pu Zhao, Minjie Hong, Yifei Dong, Minghua He, Nan Hu, Jianjin Zhang, Zhiwei Dai, Yuefeng Zhan, Weihao Han, Hao Sun, Qingwei Lin, Weiwei Deng, Feng Sun, Qi Zhang, Saravan Rajmohan, Dongmei Zhang
| Challenge: | Existing LLMs are opaque and difficult to interpret, resulting in limited interpretability. |
| Approach: | They propose an interaction-aware profile generator that jointly produces user and item profiles conditioned on both user history and item evidence. |
| Outcome: | The proposed model outperforms baselines on three real-world datasets. |
OPeRA: A Dataset of Observation, Persona, Rationale, and Action for Evaluating LLMs on Human Online Shopping Behavior Simulation (2026.acl-long)
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Ziyi Wang, Yuxuan Lu, Wenbo Li, Amirali Amini, Bo Sun, Yakov Bart, Weimin Lyu, Jiri Gesi, Tian Wang, Jing Huang, Yu Su, Upol Ehsan, Malihe Alikhani, Toby Jia-Jun Li, Lydia Chilton, Dakuo Wang
| Challenge: | evaluating LLMs' ability to mimic real user behavior remains an open challenge due to the lack of high-quality, publicly available datasets that capture both the observable actions and the internal reasoning of an actual user. |
| Approach: | They propose a dataset of Observation, Persona, Rationale, and Action collected from real human participants during online shopping sessions. |
| Outcome: | The proposed dataset is the first to evaluate how well current LLMs can accurately simulate the next web action of a specific user. |
Verifiable Generation with Subsentence-Level Fine-Grained Citations (2024.findings-acl)
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| Challenge: | Existing work mainly targets the generation of sentence-level citations, lacking specificity about which parts of a sentence are backed by the cited sources. |
| Approach: | They propose to use subsentence-level fine-grained citations to generate more precise location of generated content supported by the cited sources. |
| Outcome: | The proposed model improves the accuracy and trustworthiness of large language models by allowing users to trace the information back to its source and verify its correctness. |
Generated Knowledge Prompting for Commonsense Reasoning (2022.acl-long)
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Jiacheng Liu, Alisa Liu, Ximing Lu, Sean Welleck, Peter West, Ronan Le Bras, Yejin Choi, Hannaneh Hajishirzi
| Challenge: | Existing methods for commonsense reasoning rely on high-quality knowledge, but they are often dominated by large-scale pretrained models that are fine-tuned on a target benchmark. |
| Approach: | They develop generated knowledge prompting which generates knowledge from a language model and provides it as additional input when answering a question. |
| Outcome: | The proposed method improves state-of-the-art models on four commonsense reasoning tasks. |
MoDification: Mixture of Depths Made Easy (2025.naacl-long)
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Chen Zhang, Meizhi Zhong, Qimeng Wang, Xuantao Lu, Zheyu Ye, Chengqiang Lu, Yan Gao, Yao Hu, Kehai Chen, Min Zhang, Dawei Song
| Challenge: | Long-context efficiency is a trending topic in large language model (LLM) serving. |
| Approach: | They propose a method to combine long-context efficiency and mixture of depths to bring down both latency and memory. |
| Outcome: | The proposed method achieves 1.2 speedup in latency and 1.8 reduction in memory compared to original LLMs especially in long-context applications. |
PreAct: Prediction Enhances Agent’s Planning Ability (2025.coling-main)
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| Challenge: | Existing methods to analyze Markov decision processes (MDPs) are based on chain-of-thought (COT) and historical thought, action, and observation. |
| Approach: | They propose a model that integrates prediction, reasoning, and action with other models to provide a wider range of reasoning and more efficient actions. |
| Outcome: | The proposed model outperforms the ReAct method in completing complex tasks and is more efficient when paired with other memory or selection strategy techniques. |
AdvAug: Robust Adversarial Augmentation for Neural Machine Translation (2020.acl-main)
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| Challenge: | Recent work in neural machine translation has led to dramatic improvements in both research and commercial systems. |
| Approach: | They propose a adversarial augmentation method for Neural Machine Translation that minimizes vicinal risk over virtual sentences . they use a novel vicinity distribution for adversarials to describe a smooth interpolated embedding space . |
| Outcome: | The proposed method outperforms the current method on Chinese-English, English-French, and English-German translation benchmarks. |
Can We Trust the Performance Evaluation of Uncertainty Estimation Methods in Text Summarization? (2024.emnlp-main)
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| Challenge: | Text summarization is a key natural language generation task, but the high cost of inaccurate summaries raises concerns about the reliability of uncertainty estimation on text summarisation (UE-TS) evaluation methods. |
| Approach: | They propose a UE-TS benchmark that evaluates the uncertainty estimation capabilities of two large language models and one pre-trained language model on three datasets. |
| Outcome: | The proposed benchmark evaluates the uncertainty estimation capabilities of two large language models and one pre-trained language model on three datasets, with human-annotation analysis incorporated where applicable. |
Save the Good Prefix: Precise Error Penalization via Process-Supervised RL to Enhance LLM Reasoning (2026.findings-acl)
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| Challenge: | Existing reinforcement learning methods rely on sparse outcome rewards, which fail to credit correct intermediate steps in partially successful solutions. |
| Approach: | They propose a process reward model that rewards correct steps only when they detect errors . they propose VPPO, which rewards the correct prefix and an erroneous suffix . |
| Outcome: | a new approach outperforms sparse-reward RL and prior PRM-guided baselines on Pass@1 and Pass@K . a process reward model (PRM) outperformed sparser-rebound RL on multiple reasoning benchmarks . |
De-Bias for Generative Extraction in Unified NER Task (2022.acl-long)
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| Challenge: | Existing methods for Named entity recognition (NER) are not consistent with the task, which makes the model vulnerable to incorrect biases. |
| Approach: | They propose to use generative model to recognize entities from sentences . they analyze incorrect biases in the generation process from a causal perspective . |
| Outcome: | The proposed method improves the performance of the generative NER model in various datasets. |
AgentRM: Enhancing Agent Generalization with Reward Modeling (2025.acl-long)
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Yu Xia, Jingru Fan, Weize Chen, Siyu Yan, Xin Cong, Zhong Zhang, Yaxi Lu, Yankai Lin, Zhiyuan Liu, Maosong Sun
| Challenge: | Existing LLM-based agents have strong performance on held-in tasks, but their generalizability to unseen tasks remains poor. |
| Approach: | They propose a reward-based generalizable reward model to guide the policy model for effective test-time search. |
| Outcome: | The proposed agentRM outperforms existing agents on held-in tasks by 8.8 points on average. |
Selene: Pioneering Automated Proof in Software Verification (2024.acl-long)
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| Challenge: | Currently, software verification is resource-intensive and manpower-consuming. |
| Approach: | They propose a project-level automated proof benchmark based on the seL4 operating system . they propose augmentations to enhance the flexibility of the framework and lightweight verification environment . |
| Outcome: | The proposed framework provides a comprehensive framework for end-to-end proof generation and a lightweight verification environment. |
XMC-Agent : Dynamic Navigation over Scalable Hierarchical Index for Incremental Extreme Multi-label Classification (2024.findings-acl)
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Yanjiang Liu, Tianyun Zhong, Yaojie Lu, Hongyu Lin, Ben He, Shuheng Zhou, Huijia Zhu, Weiqiang Wang, Zhongyi Liu, Xianpei Han, Le Sun
| Challenge: | Existing methods for XMC struggle with the growing set of labels due to their static label assumptions, and embedding-based methods struggle with complex mapping relationships due to late interaction paradigm. |
| Approach: | They propose a large language model (LLM) powered agent framework for extreme multi-label classification, XMC-Agent, which can effectively learn, manage and predict the extremely large and dynamically increasing set of labels. |
| Outcome: | The proposed framework can learn, manage and predict the extremely large and dynamically growing set of labels and achieves state-of-the-art performance on three standard datasets. |
Do Large Language Models Truly Grasp Addition? A Rule-Focused Diagnostic Using Two-Integer Arithmetic (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) excel at complex math but fail on basic addition, raising the question of whether they grasp rules or are merely reproducing patterns. |
| Approach: | They systematically probe LLMs’ understanding of two-integer addition by testing three crucial properties: commutativity (A+B=B+A), representation invariance via symbolic remapping and consistent accuracy scaling with operand length. |
| Outcome: | The proposed models achieve high numeric accuracy but fail basic addition tasks. |
Syntactic and Semantic-driven Learning for Open Information Extraction (2020.findings-emnlp)
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| Challenge: | Experimental results show that our approach significantly outperforms the supervised counterparts, and can even achieve competitive performance to supervised state-of-the-art (SoA) model. |
| Approach: | They propose a syntactic and semantic-driven learning approach that can learn open IE models without human-labelled data by leveraging syntakic and semantic knowledge as noisier, higher-level supervision. |
| Outcome: | The proposed approach outperforms supervised counterparts and can achieve competitive performance to supervised state-of-the-art models. |
HiChunk: Evaluating and Enhancing Retrieval Augmented Generation with Hierarchical Chunking (2026.acl-long)
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| Challenge: | Existing evaluation benchmarks for document chunking are inadequate due to evidence sparsity . evaluators are unable to evaluate different chunking methods due to the evidence sparing . |
| Approach: | They propose a QA benchmark for document chunking and a hierarchical document structuring framework for it. |
| Outcome: | The proposed framework improves document chunking quality within reasonable time consumption. |
LoRE: Enhancing Search Relevance with Progressive Chain-of-Thought and Preference Alignment (2026.findings-acl)
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Chenji Lu, Zhuo Chen, Hui Zhao, Zhiyuan Zeng, Gang Zhao, Junjie Ren, null Lihaoran, Songyan Liu, Pengjie Wang, Chuan Yu, Jian Xu, Bo Zheng
| Challenge: | E-commerce search relevance is a critical component of retrieval systems. |
| Approach: | They propose a large-generative model for search relevance that trains reasoning knowledge, multi-modal understanding and rule awareness into three core competencies. |
| Outcome: | The proposed model outperforms GPT-5 in Macro-F1 and achieves 27% online gain. |
FastAdaSP: Multitask-Adapted Efficient Inference for Large Speech Language Model (2024.emnlp-industry)
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| Challenge: | Unlike other modalities, speech has unique temporal dependencies, making efficient inference methods unexplored. |
| Approach: | They propose a weighted token merging framework specifically designed for speech-related tasks to improve the trade-off between efficiency and performance. |
| Outcome: | The proposed method achieves state-of-the-art efficiency-performance trade-off on speech-related tasks. |
Meta-Cognitive Analysis: Evaluating Declarative and Procedural Knowledge in Datasets and Large Language Models (2024.lrec-main)
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| Challenge: | Recent advances in large language models have push NLP into a new era, moving away from traditional task-specific pre-train finetuning paradigm. |
| Approach: | They provide a comprehensive analysis of declarative and procedural knowledge for large language models and evaluate their effectiveness. |
| Outcome: | The proposed model can perform better with both kinds of knowledge, but at different speeds. |
Bounding the Capabilities of Large Language Models in Open Text Generation with Prompt Constraints (2023.findings-eacl)
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| Challenge: | Existing and potential applications of open-ended text generation are farreaching, spanning domains such as QA, story generation, open-end dialogue, and ChatGPT 1 . |
| Approach: | They propose a prompt-centric approach to analyzing and bounding the abilities of open-ended generative models by a set of structural and stylistic prompts. |
| Outcome: | The proposed method can be generalized to other large models like BLOOM and OPT. |
Reasoning-Oriented and Analogy-Based Methods for Locating and Editing in Zero-Shot Event-Relational Reasoning (2025.coling-main)
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| Challenge: | Existing methods for zero-shot event-relational reasoning require large computational resources and lack interpretability. |
| Approach: | They propose a method for Reasoning-Oriented Locating and Editing which locates and edits key modules of the language model for reasoning about event relations. |
| Outcome: | The proposed method improves interpretability and efficiency with reduced computational cost and achieves SOTA results in zero-shot event-relational reasoning. |
MoleculeQA: A Dataset to Evaluate Factual Accuracy in Molecular Comprehension (2024.findings-emnlp)
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| Challenge: | Existing models generate erroneous information and evaluations fail to assess factual correctness of models. |
| Approach: | They propose to use MoleculeQA to evaluate molecular factual correctness in large language models by organizing molecules into a taxonomy and building QA pairs through human and LLM efforts. |
| Outcome: | The proposed model improves the factual correctness of generated information and enables the development of new models. |
NOTA: Multimodal Music Notation Understanding for Visual Large Language Model (2025.findings-naacl)
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| Challenge: | Existing general-domain visual language models lack ability of music notation understanding . Symbolic music is represented in two distinct forms: auditory music and symbolic music . |
| Approach: | They propose to train a multimodal music notation model using a large-scale dataset . they use cross-modal alignment to train the model for music notations analysis . |
| Outcome: | The proposed model improves on music understanding by training with a multimodal music notation model. |
MuggleMath: Assessing the Impact of Query and Response Augmentation on Math Reasoning (2024.acl-long)
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Chengpeng Li, Zheng Yuan, Hongyi Yuan, Guanting Dong, Keming Lu, Jiancan Wu, Chuanqi Tan, Xiang Wang, Chang Zhou
| Challenge: | In math reasoning with large language models, fine-tuning data augmentation by query evolution and diverse reasoning paths is empirically verified effective. |
| Approach: | They propose to fine-tune data augmentation by query evolution and diverse reasoning paths. |
| Outcome: | The proposed model achieves new state-of-the-art on GSM8K and MATH. |
Identifying Tension in Holocaust Survivors’ Interview: Code-switching/Code-mixing as Cues (2022.lrec-1)
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| Challenge: | Using CS/CM as a linguistic phenomenon could be a sign of tension in Holocaust survivors’ interviews. |
| Approach: | They annotated CS/CM codes and annotate silence situations in an open corpus . they found that most annotations were captured in the tension places . |
| Outcome: | The proposed method shows that annotations are captured in the tension places . the study calls for more research endeavors on tension detection . |
DIAG-NRE: A Neural Pattern Diagnosis Framework for Distantly Supervised Neural Relation Extraction (P19-1)
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| Challenge: | Existing methods for labeling relational facts require significant expert labor to write relation-specific patterns, which makes them too sophisticated to generalize quickly. |
| Approach: | They propose a neural pattern diagnosis framework that can summarize and refine relation-specific patterns with human experts in the loop. |
| Outcome: | The proposed framework can summarize and refine high-quality relational patterns from noise data with human experts in the loop. |
Reducing Token Redundancy in LVLMs: A Systematic Review of Token Pruning Methods (2026.acl-long)
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| Challenge: | Large Vision-Language Models (LVLMs) excel at visual understanding but face severe computational bottlenecks when processing high-resolution images and long videos due to massive visual token counts. |
| Approach: | They propose a taxonomy categorizing methods into vision-side, LLM-side and hybrid paradigms and analyze token selection mechanisms and pruning strategy. |
| Outcome: | The proposed method selectively removes less informative tokens while maintaining performance. |
Robust Hate Speech Detection via Mitigating Spurious Correlations (2022.aacl-short)
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| Challenge: | a novel hate speech detection model can be used to detect word- and character-level adversarial attacks . existing adversarials assume that attackers replace the target words with other names to evade detection . |
| Approach: | They propose a robust hate speech detection model that can defend against adversarial attacks . they describe the process of hate speech recognition by a causal graph and a regularized entropy loss function to quantify spurious correlation . |
| Outcome: | The proposed model can defend against word- and character-level adversarial attacks. |
Is C4 Dataset Optimal for Pruning? An Investigation of Calibration Data for LLM Pruning (2024.emnlp-main)
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Abhinav Bandari, Lu Yin, Cheng-Yu Hsieh, Ajay Jaiswal, Tianlong Chen, Li Shen, Ranjay Krishna, Shiwei Liu
| Challenge: | Existing approaches to prune LLMs rely on the C4 dataset as calibration data . arithmetic datasets perform better than pre-training datasets for pruning, whereas chain-of-thought is only useful on certain tasks. |
| Approach: | They evaluate the selection of calibration data for LLM pruning across a wide range of datasets . they find that C4 is not the optimal calibration data, and that CoT is only useful on certain tasks. |
| Outcome: | The chosen calibration data significantly impacts the performance of pruned LLMs, the authors found . their results shed light on the importance of carefully selecting calibration data for LLM pruning . |
Enhance Multimodal Consistency and Coherence for Text-Image Plan Generation (2025.findings-acl)
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| Challenge: | Existing studies on textual plan generation only focus on LLMs, enabling applications in robotics, virtual assistants, and instruc. |
| Approach: | They propose a framework that generates and refines text-image plans step-by-step . they collect a new benchmark consisting of 1,100 tasks and their text- image pair solutions covering 11 daily topics. |
| Outcome: | The proposed framework generates and refines text-image plans step-by-step and improves on existing models. |
RecMind: Large Language Model Powered Agent For Recommendation (2024.findings-naacl)
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Yancheng Wang, Ziyan Jiang, Zheng Chen, Fan Yang, Yingxue Zhou, Eunah Cho, Xing Fan, Yanbin Lu, Xiaojiang Huang, Yingzhen Yang
| Challenge: | Existing recommendations systems are limited in generalizing to new tasks due to model scale and data size constraints. |
| Approach: | They propose an LLM-powered autonomous recommender agent, RecMind, which is capable of leveraging external knowledge to provide zero-shot personalized recommendations. |
| Outcome: | The proposed model outperforms existing zero/few-shot LLM-based recommendation baseline methods in various tasks and achieves comparable performance to a fully trained recommendation model P5. |
UI-Copilot: Advancing Long-Horizon GUI Automation via Tool-Integrated Policy Optimization (2026.acl-long)
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Zhengxi Lu, Fei Tang, Guangyi Liu, Jin Ma, Kaitao Song, Xu Tan, Wenqi Zhang, Weiming Lu, Jun Xiao, Yueting Zhuang, Yongliang Shen
| Challenge: | Experimental results show that UI-Copilot-7B achieves state-of-the-art performance on challenging MemGUI-Bench, outperforming strong 7B-scale GUI agents such as GUI-Owl-7B and UITARS-1.5-7B. |
| Approach: | They propose a collaborative framework where the GUI agent focuses on task execution while a lightweight copilot provides on-demand assistance for memory retrieval and numerical computation. |
| Outcome: | The proposed framework outperforms GUI-Owl-7B and UI-TARS-1.5-7B on MemGUI-Bench and delivers 17.1% improvement on AndroidWorld over the base Qwen model. |
Summarization as Indirect Supervision for Relation Extraction (2022.findings-emnlp)
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| Challenge: | Relation extraction (RE) models rely on training data with expensive annotations . et al., 2018; Zhao e.t al, 2018) . |
| Approach: | They propose a method that converts RE into a summarization formulation by using constraint decoding techniques. |
| Outcome: | The proposed method improves relation extraction models with high-resource and high-contrast inferences. |
Advancing SMoE for Continuous Domain Adaptation of MLLMs: Adaptive Router and Domain-Specific Loss (2025.acl-long)
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| Challenge: | Recent studies have explored Continual Instruction Tuning (CIT) in Multimodal Large Language Models (MLLMs), with a primary focus on Task-incremental CIT, where MLLM are required to continuously acquire new tasks. |
| Approach: | They propose a Sparse Mixture of Expert (SMoE) based method for domain-incremental CIT in Multimodal Large Language Models (MLLMs) . they equip the SMoA module with a domain-specific autoregressive loss (DSAL) they establish a new benchmark to evaluate the efficacy of their method . |
| Outcome: | The proposed method outperforms all baselines and is based on a Sparse Mixture of Experts (SMoE) module . |
Debiasing In-Context Learning by Instructing LLMs How to Follow Demonstrations (2024.findings-acl)
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Lvxue Li, Jiaqi Chen, Xinyu Lu, Yaojie Lu, Hongyu Lin, Shuheng Zhou, Huijia Zhu, Weiqiang Wang, Zhongyi Liu, Xianpei Han, Le Sun
| Challenge: | In-context learning (ICL) has gained considerable attention due to its data efficiency and task adaptability. |
| Approach: | They propose to de-biase demonstration bias in in-context learning by focusing on semantic ambiguity induced by demonstrations and reducing the semantic hazard. |
| Outcome: | The proposed methods significantly improve performance on six datasets. |
Exploring Compositional Generalization of Large Language Models (2024.naacl-srw)
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| Challenge: | a recent study has found that large language models can generalize compositional instructions from simple instructions to complex ones. |
| Approach: | They study the generalization ability of large language models with respect to compositional instructions . they first construct a dataset with the help of ChatGPT guided by the self-instruct technique . |
| Outcome: | The proposed model can generalize from simple instructions to more intricate ones, the authors show . their results show that training LLMs on higher-order compositional instructions improves performance on lower-order ones, but not on higher order ones. |
Large Language Models Meet NL2Code: A Survey (2023.acl-long)
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Daoguang Zan, Bei Chen, Fengji Zhang, Dianjie Lu, Bingchao Wu, Bei Guan, Wang Yongji, Jian-Guang Lou
| Challenge: | generating code from a natural language description is a pressing and significant challenge in code intelligence. |
| Approach: | They propose to survey 27 existing large language models for NL2Code and compare them to humanEval benchmarks. |
| Outcome: | The proposed model is compared with existing models on the HumanEval benchmark. |
DaMo: Data Mixing Optimizer in Fine-tuning Multimodal LLMs for Mobile Phone Agents (2026.findings-acl)
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Kai Shi, Jun Yang, Ni Yang, Binqiang Pan, Qingsong Xie, null Zhangchao, Zhenyu Yang, Tianhuang Su, Haonan Lu
| Challenge: | Mobile Phone Agents (MPAs) have attracted huge attention due to their practicability in a multitude of scenarios. |
| Approach: | They propose a data mixture optimization solution that extrapolates optimal data mixtures from a trainable network. |
| Outcome: | The proposed model outperforms existing methods on open-source benchmarks and on open source benchmarks. |
ClinicalT5: A Generative Language Model for Clinical Text (2022.findings-emnlp)
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| Challenge: | Recent generative language models like BART and T5 are gaining popularity with their competitive performance on text generation and tasks cast as generative problems. |
| Approach: | They propose to build domain-specific PLMs through fine-tuning or pre-training from scratch over domain corpora. |
| Outcome: | The proposed model outperforms existing models on domain-specific tasks and compares favorably with its close baselines. |
Take Care of Your Prompt Bias! Investigating and Mitigating Prompt Bias in Factual Knowledge Extraction (2024.lrec-main)
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| Challenge: | Recent research shows that pre-trained language models suffer from “prompt bias” in factual knowledge extraction. |
| Approach: | They propose a representation-based approach to mitigate prompt bias during inference time by querying the model and removing it from its internal representations to generate debiased representations. |
| Outcome: | The proposed approach corrects the overfitted performance caused by prompt bias and significantly improves prompt retrieval capability. |
SeqAR: Jailbreak LLMs with Sequential Auto-Generated Characters (2025.naacl-long)
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| Challenge: | Existing studies have focused on the potential misuse of large language models (LLMs) however, the ability to align LLMs with human values is still vulnerable to malicious attacks. |
| Approach: | They propose a red-teaming strategy to enhance LLM safety by using a framework to design jailbreak prompts automatically. |
| Outcome: | The proposed framework achieves attack success rates of 88% and 60% in cold-start scenarios. |
Error Analysis of Uyghur Name Tagging: Language-specific Techniques and Remaining Challenges (L18-1)
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| Challenge: | despite efforts at name tagging, there is limited understanding on the performance ceiling . despite the high-resource language, there are very few natural language processing tools available . |
| Approach: | They propose to use a machine learning model to identify Uyghur name tagger errors . they conclude that such a model is unlikely to be effective for Uygur, or low-resource languages . |
| Outcome: | The proposed model is unlikely to be effective for Uyghur, or low-resource languages in general, the authors argue . they show that the proposed model can be used for high-res languages with superficial features . |
Decoding the Market’s Pulse: Context-Enriched Agentic Retrieval Augmented Generation for Predicting Post-Earnings Price Shocks (2026.eacl-long)
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| Challenge: | Existing methods for forecasting large stock price movements after corporate earnings calls are prone to **narrative bias** Existing approaches lack temporal-causal reasoning and are unable to predict large stock prices. |
| Approach: | They propose a retrieval-augmented framework that deploys a team of cooperative LLM agents . they retrieve structured evidence from a Causal-Temporal Knowledge Graph built from financial statements and earnings calls . |
| Outcome: | The proposed framework outperforms larger LLMs and fine-tuned models in macro-F1, MCC, and Sharpe for the same forecasting horizon. |
Unraveling Feature Extraction Mechanisms in Neural Networks (2023.emnlp-main)
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| Challenge: | Neural networks have become indispensable across a variety of natural language processing tasks. |
| Approach: | They propose a theoretical approach based on Neural Tangent Kernels to investigate neural networks' internal mechanisms. |
| Outcome: | The proposed approach can be applied to analyze language modeling tasks . it shows that the choice of activation function can affect feature extraction . |
Improved Knowledge Distillation for Pre-trained Language Models via Knowledge Selection (2022.findings-emnlp)
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| Challenge: | Existing studies on knowledge distillation have shown that not all knowledge is necessary for learning a good student model. |
| Approach: | They propose an actor-critic approach to selecting appropriate knowledge to transfer during the process of knowledge distillation. |
| Outcome: | The proposed method outperforms several strong knowledge distillation baselines significantly on the GLUE datasets. |
From Query to Counsel: Structured Reasoning with a Multi-Agent Framework and Dataset for Legal Consultation (2026.acl-long)
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| Challenge: | Legal consultation question answering presents unique challenges compared to traditional legal QA tasks . |
| Approach: | They propose a framework that converts queries into a legal element graph . jurisMA supports dynamic routing, statutory grounding, and stylistic optimization . |
| Outcome: | The proposed framework outperforms general-purpose and legal-domain LLMs across multiple lexical and semantic metrics. |
MultiFinBen: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application (2026.acl-long)
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Xueqing Peng, Lingfei Qian, Yan Wang, Ruoyu Xiang, Yueru He, Yang Ren, Mingyang Jiang, Vincent Jim Zhang, Yuqing Guo, Jeff Zhao, Huan He, Yi Han, Yun Feng, Yuechen Jiang, Yupeng Cao, Haohang Li, Yangyang Yu, Xiaoyu Wang, Penglei Gao, Shengyuan Lin, Keyi Wang, Shanshan Yang, Yilun Zhao, Zhiwei Liu, Peng Lu, Jerry Huang, Suyuchen Wang, Triantafillos Papadopoulos, Polydoros Giannouris, Efstathia Soufleri, Nuo Chen, Zhiyang Deng, Heming Fu, Yijia Zhao, Mingquan Lin, Meikang Qiu, Kaleb E Smith, Arman Cohan, Xiao-Yang Liu, Jimin Huang, Guojun Xiong, Alejandro Lopez-Lira, Xi Chen, Junichi Tsujii, Jian-Yun Nie, Sophia Ananiadou, Qianqian Xie
| Challenge: | Existing evaluations of LLMs in finance are text-only, monolingual, and largely saturated by current models. |
| Approach: | They propose a multilingual and multimodal benchmark for evaluating LLMs in real financial contexts. |
| Outcome: | The first expert-annotated multilingual and multimodal benchmark is released . it evaluates 21 leading LLMs and shows they perform better in multilingual settings . |
STRUX: An LLM for Decision-Making with Structured Explanations (2025.naacl-short)
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| Challenge: | a new LLM decision-making framework is designed to help users understand how and why decisions are made. |
| Approach: | They introduce a new LLM decision-making framework called STRUX that provides structured explanations for LLM decisions. |
| Outcome: | The proposed framework improves decision-making by providing structured explanations . it has been evaluated on the task of forecasting stock investment decisions based on earnings call transcripts - superior performance against strong baselines compared with previous frameworks based upon earnings call transcriptions demonstrating superior performance . |
StereoRel: Relational Triple Extraction from a Stereoscopic Perspective (2021.acl-long)
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| Challenge: | Existing methods for relational triple extraction still face challenges, including information loss and error propagation. |
| Approach: | They propose a model which maps relational triples to a three-dimensional space and leverages three decoders to extract them. |
| Outcome: | The proposed model outperforms the baselines on five public datasets. |
Better Feature Integration for Named Entity Recognition (2021.naacl-main)
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| Challenge: | Existing approaches to named entity recognition (NER) focus on stacking the LSTM and graph neural networks (GCNs) however, the exact interaction mechanism between the two types of features is not clear and the performance gain is not significant. |
| Approach: | They propose a model that incorporates both types of features with a Synergized-LSTM which captures how the two types of feature interact. |
| Outcome: | The proposed model achieves better performance than previous approaches while requiring fewer parameters. |
Logit Arithmetic Elicits Long Reasoning Capabilities Without Training (2026.findings-acl)
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Yunxiang Zhang, Muhammad Khalifa, Lechen Zhang, Xin Liu, Ayoung Lee, Xinliang Frederick Zhang, Farima Fatahi Bayat, Lu Wang
| Challenge: | Large reasoning models exhibit long chain-of-thought reasoning with complex strategies such as backtracking and self-verification, yet, these capabilities typically require resource-intensive post-training. |
| Approach: | They propose a decoding-time approach which transfers long chain-of-thought reasoning capabilities from a substantially smaller reasoning guider to a large non-reasoning target. |
| Outcome: | The proposed method improves performance over a model 21x smaller than the target model by 21.5% and 24.2% over the model. |
Multi-Channel Convolutional Neural Network for Twitter Emotion and Sentiment Recognition (N19-1)
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| Challenge: | Existing methods to analyze tweets are based on lexical features and a multi-channel convolutional neural architecture. |
| Approach: | They propose a neural network which can use different emotion and sentiment indicators such as hashtags, emoticons and emojis present in tweets to improve the performance of emotion and feelings identification. |
| Outcome: | The proposed model can use hashtags, emoticons and emojis present in tweets and improves emotion and sentiment identification. |
Metaphor Reasoning is Meta-reasoning (2026.acl-long)
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Qianyu He, Junting Lu, Yikai Zhang, Siyu Yuan, Xiaojun Meng, Jiansheng Wei, Jiaqing Liang, Yanghua Xiao
| Challenge: | Existing work on metaphor reasoning's impact on reasoning abilities is limited. |
| Approach: | They propose a system for synthesizing metaphorical riddles that satisfy five quality dimensions: diverse, balanced, reasoning-oriented, challenging, and verifiable. |
| Outcome: | The proposed system improves reasoning abilities across six domains using only thousands of metaphorical riddles. |
Single-to-mix Modality Alignment with Multimodal Large Language Model for Document Image Machine Translation (2025.acl-long)
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| Challenge: | Document Image Machine Translation (DIMT) faces generalization challenges due to limited training data and the complex interplay between visual and textual information. |
| Approach: | They propose a single-to-mix Modality alignment framework leveraging Multimodal Large Language Models (MLLMs) this framework aligns an imageonly encoder with multimodal representations of an MLLM pre-trained on large-scale document image datasets. |
| Outcome: | The proposed framework improves translation quality in cross-domain generalization and challenging document image scenarios. |
LLaMA Pro: Progressive LLaMA with Block Expansion (2024.acl-long)
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| Challenge: | Existing studies have demonstrated that pre-trained LLMs are limited in certain domains, such as programming, mathematics, biomedical, or finance. |
| Approach: | They propose a new post-pretraining method with an expansion of Transformer blocks to tune the expanded blocks using only new corpus, efficiently and effectively improving the model’s knowledge while mitigating forgetting. |
| Outcome: | The proposed model outperforms existing models in programming and math and its instruction-following counterpart LLaMA Pro-8.3B in general tasks, programming, and mathematics. |
CIF-PT: Bridging Speech and Text Representations for Spoken Language Understanding via Continuous Integrate-and-Fire Pre-Training (2023.findings-acl)
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| Challenge: | Speech-to-text training and language model distillation are used to bridge the representations between speech and text. |
| Approach: | They propose a pre-training paradigm that integrates speech and text into a single frame-to-token alignment. |
| Outcome: | The proposed paradigm outperforms the state-of-the-art model on intent classification and slot filling tasks. |
Beyond Cross-Modal Alignment: Measuring and Leveraging Modality Gap in Vision-Language Models (2026.findings-acl)
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| Challenge: | a recent study shows that vision-language models have modality gaps that persist even in well-aligned models. |
| Approach: | They propose a modality-dominance score to measure and leverage modality gaps . they propose automatic interpretability metrics to evaluate these features in a scalable manner . |
| Outcome: | The proposed framework allows for training-free probing and editing methods for understanding model perception across genders and generating adversarial examples. |
Reflective RAG: Self-Evaluation Driven Strategy Optimization in Agentic Retrieval-Augmented Generation (2026.findings-acl)
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| Challenge: | Recent agentic RAG systems lack the capacity to evaluate the utility of retrieved information, leading to brittle reasoning and suboptimal decision-making. |
| Approach: | They propose a framework that integrates self-evaluation to dynamically optimize retrieval and generation strategy. |
| Outcome: | The proposed framework outperforms strong agentic baselines on five knowledge-intensive QA benchmarks and improves training stability and generalization to multi-hop reasoning tasks. |
Non-Autoregressive Machine Translation as Constrained HMM (2024.findings-acl)
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| Challenge: | Autoregressive (AR) models have some drawbacks due to slow inference speed and label bias due to local normalization. |
| Approach: | They propose to use a left-to-right Hidden Markov Model (HMM) to control label bias in non-autoregressive translation (NAT) They propose a bi-directional HMM, which can regularize each other's biases via shared parameters. |
| Outcome: | The proposed models can achieve comparable performance to autoregressive Transformers using various decoding methods. |
Simple or Complex? Complexity-controllable Question Generation with Soft Templates and Deep Mixture of Experts Model (2021.findings-emnlp)
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| Challenge: | Existing work on complex questions does not consider controlling complexity of generated questions. |
| Approach: | They propose an end-to-end neural complexity-controllable question generation model that incorporates a mixture of experts as the selector of soft templates to capture question similarity while avoiding the expensive construction of actual templates. |
| Outcome: | The proposed model is superior to state-of-the-art methods in both automatic and manual evaluations on two benchmark QA datasets. |
Adversarial Preference Learning for Robust LLM Alignment (2025.findings-acl)
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Yuanfu Wang, Pengyu Wang, Chenyang Xi, Bo Tang, Junyi Zhu, Wenqiang Wei, Chen Chen, Chao Yang, Jingfeng Zhang, Chaochao Lu, Yijun Niu, Keming Mao, Zhiyu Li, Feiyu Xiong, Jie Hu, Mingchuan Yang
| Challenge: | Modern language models rely on Reinforcement Learning from Human Feedback (RLHF) to encourage safe behaviors, but they remain vulnerable to adversarial attacks due to three key limitations: (1) the inefficiency and high cost of human annotation; (2) the vast diversity of potential adversarials; and (3) the risk of feedback bias and reward hacking. |
| Approach: | They propose an iterative adversarial training method that incorporates three key innovations to address these challenges. |
| Outcome: | Experiments on Mistral-7B-Instruct-v0.3 show that the proposed method significantly enhances robustness and reduces harmful outputs from 5.88% to 0.43%. |
Event Coreference Resolution with Non-Local Information (2020.aacl-main)
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| Challenge: | Existing joint models for event coreference resolution are understudied and underexploited . current models only learn trigger detection and event coreference from annotated training data . |
| Approach: | They propose to add a topic-based trigger detection module and a preprocessing module to improve event coreference. |
| Outcome: | The proposed model yields the best results on the KBP 2017 English and Chinese datasets. |
Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation (2024.findings-acl)
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Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang
| Challenge: | Recent advances in Large Language Models (LLMs) have greatly advanced problem solving in diverse domains such as mathematical reasoning and knowledge reasoning. |
| Approach: | They propose a thought prompting approach called 'Everything of Thoughts' it leverages pretrained reinforcement learning and Monte Carlo Tree Search to incorporate external domain knowledge and planning capability into thoughts. |
| Outcome: | The proposed approach outperforms existing approaches on game of 24, 8-Puzzle, and Pocket Cube. |
MultiTEND: A Multilingual Benchmark for Natural Language to NoSQL Query Translation (2025.findings-acl)
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| Challenge: | Recent advances in NoSQL database support focus on English . however, the intricacy and heterogeneity of NoSqL query languages present a formidable challenge . |
| Approach: | They propose a multilingual benchmark for natural language to NoSQL query generation that covers six languages. |
| Outcome: | The proposed framework improves performance in English and non-English settings, while ignoring lexical and syntactic differences. |
Self-Steering Optimization: Autonomous Preference Optimization for Large Language Models (2025.findings-acl)
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Hao Xiang, Bowen Yu, Hongyu Lin, Keming Lu, Yaojie Lu, Xianpei Han, Ben He, Le Sun, Jingren Zhou, Junyang Lin
| Challenge: | Prior research focused on developing data generation methods, while insufficient attention has been paid to quality control mechanisms and often produces inaccurate and unhelpful data. |
| Approach: | They propose an algorithm that automatically generates high-quality preference data, eliminating manual annotation requirements. |
| Outcome: | The proposed algorithm outperforms baselines in human preference alignment and reward optimization. |
CoGR-MoE: Concept-Guided Expert Routing with Consistent Selection and Flexible Reasoning for Visual Question Answering (2026.findings-acl)
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| Challenge: | Recent work has begun to address routing instability in VQA models by grouping similar concepts or routing based on examples. |
| Approach: | They propose a Concept-Guided Routing framework which incorporates semantics of the answer options to guide expert selection in the training phase. |
| Outcome: | The proposed framework delivers strong performance across multiple VQA tasks, demonstrating the effectiveness of the proposed framework. |
MCMH: Learning Multi-Chain Multi-Hop Rules for Knowledge Graph Reasoning (2020.findings-emnlp)
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| Challenge: | Existing work on knowledge graphs infers a missing relationship between entities with a multi-hop rule . Empirical results show that our multi-chain multi-homing (MCMH) rules yield superior results compared to the standard single-chain approaches. |
| Approach: | They propose to use a generalized form of multi-hop rules to learn generalized rules efficiently . they propose to select a small set of relation chains as a rule and evaluate confidence . |
| Outcome: | The proposed method outperforms the existing methods and the existing frameworks. |
Event-Centric Question Answering via Contrastive Learning and Invertible Event Transformation (2022.findings-emnlp)
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| Challenge: | Existing QA frameworks that use event-centric reasoning are lacking. |
| Approach: | They propose a novel QA model with contrastive learning and invertible event transformation . they use an invertable transformation matrix to project event vectors into a common event embedding space . |
| Outcome: | The proposed model achieves 8.4% gain in token-level F1 score and 3.0% gain in Exact Match score on the ESTER dataset. |
TextFusion: Privacy-Preserving Pre-trained Model Inference via Token Fusion (2022.emnlp-main)
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Xin Zhou, Jinzhu Lu, Tao Gui, Ruotian Ma, Zichu Fei, Yuran Wang, Yong Ding, Yibo Cheung, Qi Zhang, Xuanjing Huang
| Challenge: | Existing methods to preserve inference privacy are available as cloud services . however, the risk of privacy leakage remains, according to recent studies . |
| Approach: | They propose a method to preserve inference privacy by fusing token representations in the cloud. |
| Outcome: | The proposed method preserves inference privacy without sacrificing performance on different scenarios. |
Defending Against Alignment-Breaking Attacks via Robustly Aligned LLM (2024.acl-long)
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| Challenge: | Large Language Models (LLMs) have made significant advancements but can be misused to generate harmful content. |
| Approach: | They propose a Robustly Aligned LLM to defend against alignment-breaking attacks by retraining existing LLMs and using adversarial or handcrafted jailbreaking prompts. |
| Outcome: | The proposed model reduces attack success rates from nearly 100% to around 10% or less. |
Adam’s Law: Textual Frequency Law on Large Language Models (2026.acl-long)
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| Challenge: | Textual frequency is a topic of understudied research, but its relevance to Large Language Models is not well understood. |
| Approach: | They propose a framework to estimate textual data frequency using a paraphraser and a textual distillation method to refine LLMs. |
| Outcome: | The proposed framework can be used to estimate sentence-level frequency with word-level frequencies. |
Dependency-Guided LSTM-CRF for Named Entity Recognition (D19-1)
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| Challenge: | Named entity recognition (NER) is one of the most important and fundamental tasks in natural language processing (NLP). |
| Approach: | They propose a dependency-guided model to encode dependency trees and capture their properties for named entity recognition. |
| Outcome: | The proposed model improves named entity recognition performance on standard datasets. |
DaMSTF: Domain Adversarial Learning Enhanced Meta Self-Training for Domain Adaptation (2023.acl-long)
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| Challenge: | Existing approaches to domain adaptation only use reliable pseudo instances, i.e., pseudo instances with high prediction confidence, to retrain the model. |
| Approach: | They propose a domain adversarial learning enhanced self-training framework that uses meta-learning to estimate the importance of each pseudo instance and a meta constructor to construct the meta-validation set. |
| Outcome: | The proposed framework reduces label noise and preserves hard examples while maintaining accuracy. |
Detection of Propaganda Using Logistic Regression (D19-50)
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| Challenge: | Various propaganda techniques are used to manipulate peoples perspectives to foster a predetermined agenda. |
| Approach: | They propose a Logistic Regression-based tool that automatically classifies whether a sentence is propagandistic or not. |
| Outcome: | The proposed tool outperforms the baseline on linguistic and semantic features. |
TVWorld: Foundations for Remote-Control TV Agents (2026.findings-acl)
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| Challenge: | Existing work on large vision–language models focuses on point-and-click interaction, while remote-control interaction is underexplored. |
| Approach: | They propose a topology-aware training framework that injects topology awareness into LVLMs. |
| Outcome: | The proposed model achieves 68.3% success rate on TVWorld-N, surpassing closed-source benchmarks and state-of-the-art (SOTA) benchmarks show that existing agents lack topology awareness for focus-based, long-horizon TV navigation. |
ToolSandbox: A Stateful, Conversational, Interactive Evaluation Benchmark for LLM Tool Use Capabilities (2025.findings-naacl)
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Jiarui Lu, Thomas Holleis, Yizhe Zhang, Bernhard Aumayer, Feng Nan, Haoping Bai, Shuang Ma, Shen Ma, Mengyu Li, Guoli Yin, Zirui Wang, Ruoming Pang
| Challenge: | Recent advances in large language models have led to a growing interest in tool assisted LLMs . toolSandbox includes stateful tool execution, implicit state dependencies between tools . |
| Approach: | a new tool-based evaluation tool is released to help LLMs evaluate their tool-use capabilities. a tool-driven evaluation tool includes stateful tool execution, implicit state dependencies between tools and a built-in user simulator. |
| Outcome: | the toolSandbox evaluation benchmark shows that open source and proprietary models have a performance gap . the benchmarks show that even the most capable LLMs are challenged by state dependent tasks . |
Modeling the Relationship between User Comments and Edits in Document Revision (D19-1)
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| Challenge: | Managing collaborative documents can be difficult due to the profusion of edits and comments that multiple authors make during a document’s evolution. |
| Approach: | They propose a hierarchical multi-layer deep neural network to model the relationship between edits and comments by encoding specific edit actions such as additions and deletions while accounting for document context. |
| Outcome: | The proposed model outperforms baselines in a number of evaluation settings and achieves a precision@1 of 71.0% and precision@3 of 94.4% for Comment Ranking while achieving 74.4% accuracy on Edit Anchoring. |
MQM-APE: Toward High-Quality Error Annotation Predictors with Automatic Post-Editing in LLM Translation Evaluators (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) have shown significant potential as judges for Machine Translation (MT) quality assessment. |
| Approach: | They propose a framework that automatically post-edits the original translation based on each error, thereby filtering out non-impactful errors. |
| Outcome: | The proposed framework improves reliability and quality of error spans against GEMBA-MQM, across eight LLMs in both high- and low-resource languages. |
SAM3-I: Segment Anything with Instructions (2026.acl-long)
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Jingjing Li, Yue Feng, Yuchen Guo, Jincai Huang, Wei Ji, Qi Bi, Yongri Piao, Miao Zhang, Xiaoqi Zhao, Qiang Chen, Shihao Zou, Huchuan Lu, Li Cheng
| Challenge: | Existing methods for concept-level grounding and instruction-level reasoning use coarse representations and iterative mask filtering. |
| Approach: | They propose an instruction-following extension of the Segment Anything Model 3 family that unifies concept-level grounding and instruction-level reasoning within a single segmentation framework. |
| Outcome: | Experiments show that SAM3-I achieves appealing performance across referring and reasoning-based segmentation while maintaining its strong concept recall ability. |
Bidirectional Hierarchical Attention Networks based on Document-level Context for Emotion Cause Extraction (2021.findings-emnlp)
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| Challenge: | Emotion cause extraction (ECE) aims to extract the causes behind certain emotion in text. |
| Approach: | They propose a bidirectional hierarchical attention network corresponding to the specified candidate cause clause to capture document-level context in a structured and dynamic manner. |
| Outcome: | The proposed method achieves competitive performances on two public datasets in Chinese and English. |
Document Image Machine Translation with Dynamic Multi-pre-trained Models Assembling (2024.naacl-long)
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| Challenge: | Existing TIMT tasks focus on text-line-level images. |
| Approach: | They propose to extend the existing TIMT task and introduce a new framework to translate a source document image to markdown-formatted target translation. |
| Outcome: | The proposed task aims to translate a source document image with long context and complex layout structure to markdown-formatted target translation. |
ShortGPT: Layers in Large Language Models are More Redundant Than You Expect (2025.findings-acl)
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Xin Men, Mingyu Xu, Qingyu Zhang, Qianhao Yuan, Bingning Wang, Hongyu Lin, Yaojie Lu, Xianpei Han, Weipeng Chen
| Challenge: | Recent studies have identified significant redundancy in large language models . quantization and pruning are two methods that reduce computational resources . |
| Approach: | They propose simple pruning methods that prune redundant layers based on their BI scores. |
| Outcome: | The proposed pruning methods demonstrate superior performance over previous pruning methods. |
Robust Unsupervised Neural Machine Translation with Adversarial Denoising Training (2020.coling-main)
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| Challenge: | Unsupervised neural machine translation (UNMT) has attracted great interest in the machine translation community. |
| Approach: | They propose to explicitly take noisy data into consideration to improve the robustness of UNMT based systems. |
| Outcome: | The proposed methods significantly improved the robustness of the conventional UNMT systems in noisy scenarios. |
Task-Agnostic Detector for Insertion-Based Backdoor Attacks (2024.findings-naacl)
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| Challenge: | Existing methods for textual backdoor detection are task-specific and less effective beyond sentence classification. |
| Approach: | They propose a task-agnostic method for backdoor detection that leverages final layer logits and an efficient pooling technique. |
| Outcome: | TABDet can jointly learn from diverse task-specific models, demonstrating superior detection efficacy over traditional methods. |
TurnBack: A Geospatial Route Cognition Benchmark for Large Language Models through Reverse Route (2025.emnlp-main)
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Hongyi Luo, Qing Cheng, Daniel Matos, Hari Krishna Gadi, Yanfeng Zhang, Lu Liu, Yongliang Wang, Niclas Zeller, Daniel Cremers, Liqiu Meng
| Challenge: | Existing studies on large language models have limited evaluation of their geospatial cognition . a unified framework for evaluating geospcial cognition in LLMs remains absent . |
| Approach: | They propose a benchmark to evaluate the geospatial route cognition of Large Language Models . they propose 'pathbuilder' tool for converting natural language instructions into navigation routes . |
| Outcome: | The proposed framework and metrics evaluate 9 state-of-the-art LLMs on route reversal task. |
Debiasing Word Embeddings with Nonlinear Geometry (2022.coling-1)
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| Challenge: | Existing methods for debiasing word embeddings are limited to individual social categories . however, real-world corpora typically present multiple social categories that may correlate or intersect with each other. |
| Approach: | They propose a method to debias word embeddings using nonlinear geometry of individual biases. |
| Outcome: | Empirical results show that the proposed method mitigates biases associated with individual social categories and treats each category in isolation. |
How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition (2024.acl-long)
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Guanting Dong, Hongyi Yuan, Keming Lu, Chengpeng Li, Mingfeng Xue, Dayiheng Liu, Wei Wang, Zheng Yuan, Chang Zhou, Jingren Zhou
| Challenge: | supervised fine-tuning (SFT) is a technique used to enhance multiple abilities in large language models. |
| Approach: | They propose to study the interplay of data composition between mathematical reasoning, code generation, and general human-aligning abilities during supervised fine-tuning. |
| Outcome: | The proposed model improves math reasoning and code generation with increasing data amount . the proposed model size and SFT strategies can be used to learn multiple skills with different scaling patterns. |
SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization (2023.emnlp-main)
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Hyunwoo Kim, Jack Hessel, Liwei Jiang, Peter West, Ximing Lu, Youngjae Yu, Pei Zhou, Ronan Bras, Malihe Alikhani, Gunhee Kim, Maarten Sap, Yejin Choi
| Challenge: | a dataset of 1.5 million conversations distilled from everyday spoken situations is limited in scale due to its associated costs. |
| Approach: | They propose to make SODA a publicly available, million-scale high-quality social dialogue dataset . they contextualize social commonsense knowledge from a knowledge graph to distill broad spectrum of social interactions . |
| Outcome: | The proposed dataset is the first publicly available, million-scale high-quality social dialogue dataset. |
An Expression Tree Decoding Strategy for Mathematical Equation Generation (2023.emnlp-main)
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| Challenge: | Existing approaches to generate mathematical equations from natural language ignore parallel or dependent relations between math expressions. |
| Approach: | They propose to integrate tree structure into the expression-level generation and advocate an expression tree decoding strategy. |
| Outcome: | The proposed method outperforms baseline methods for generating mathematical equations from natural language. |
LILA: A Unified Benchmark for Mathematical Reasoning (2022.emnlp-main)
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Swaroop Mishra, Matthew Finlayson, Pan Lu, Leonard Tang, Sean Welleck, Chitta Baral, Tanmay Rajpurohit, Oyvind Tafjord, Ashish Sabharwal, Peter Clark, Ashwin Kalyan
| Challenge: | Towards evaluating and improving AI systems in this domain, we propose a mathematical reasoning benchmark based on 23 diversetasks . |
| Approach: | They propose a mathematical reasoning benchmark that includes 23 diverse tasks . they extend the benchmark by collecting task instructions and solutions in the form of Python programs . |
| Outcome: | The proposed model improves on multi-tasking while the best performing model only achieves 60.40%. |
Graph of Trace: Visualizing Execution Traces of Scientific Agents (2026.acl-demo)
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| Challenge: | Scientific AI agents can perform complex research tasks, but these unfolded workflows are difficult for humans to inspect and review, limiting interpretable, controllable and effective human–AI collaboration. |
| Approach: | They propose a monitoring and visualization framework that records fine-grained execution events and organizes them into a directed graph that makes agent workflows explicit as they proceed. |
| Outcome: | The proposed framework records intermediate steps (e.g. tool calls and code executions) and renders them as real-time updated visual traces that expose workflow structure. |
Negating Negatives: Alignment with Human Negative Samples via Distributional Dispreference Optimization (2024.findings-emnlp)
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| Challenge: | Existing methods to steer LLMs towards human preference suffer from noisy positive-negative training pairs. |
| Approach: | They propose a distributional preference optimization method which maximizes discrepancy between dispreferred responses and generated non-negative ones. |
| Outcome: | The proposed method achieves comparable generation quality and surpasses the latest strong baselines in producing less harmful and more informative responses with better training stability and faster convergence. |
Transferable Post-training via Inverse Value Learning (2025.naacl-long)
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| Challenge: | Existing algorithms for post-training large datasets are requiring a large computational effort. |
| Approach: | They propose to model the changes at logits level during post-training using a separate neural network . they demonstrate that the value network can be seamlessly integrated with another pre-trained model . |
| Outcome: | The proposed model can be integrated with another pre-trained model during inference, enabling similar capability enhancements. |
Grammatical Error Correction Systems for Automated Assessment: Are They Susceptible to Universal Adversarial Attacks? (2022.aacl-main)
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| Challenge: | With advances in deep learning, GEC systems are susceptible to adversarial attacks, in which a small change at the input can cause large undesired changes at the output. |
| Approach: | They propose to use a concatenative universal attack to deceive the system into not correcting grammatical errors to create the perception of higher language ability. |
| Outcome: | The proposed attack can deceive the system into not correcting (concealing) grammatical errors to create the perception of higher language ability. |
Towards LLM-driven Dialogue State Tracking (2023.emnlp-main)
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| Challenge: | emergence of large language models (LLMs) such as GPT3 and ChatGPT has sparked considerable interest in assessing their efficacy across diverse applications. |
| Approach: | They present a framework for a domain-slot instruction tuning method that allows LDST to achieve performance on par with ChatGPT. |
| Outcome: | The proposed framework performs better in zero-shot and few-shot settings than previous SOTA methods. |
Streaming Hallucination Detection in Long Chain-of-Thought Reasoning (2026.findings-acl)
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Haolang Lu, Minghui Pan, Ripeng LI, Guoshun Nan, Jialin Zhuang, Zijie Zhao, Zhongxiang Sun, Kun Wang, Yang Liu
| Challenge: | Long chain-of-thought reasoning improves performance of large language models, yet hallucinations in such settings often emerge subtly and propagate across reasoning steps. |
| Approach: | They propose to treat step-level hallucination judgments as local observations and introduce a cumulative prefix-level signal that tracks the global evolution of the reasoning state over the entire trajectory. |
| Outcome: | The proposed method enables streaming hallucination detection in long CoT reasoning, providing real-time, interpretable evidence. |
Miracle: Towards Personalized Dialogue Generation with Latent-Space Multiple Personal Attribute Control (2023.findings-emnlp)
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| Challenge: | Personalized dialogue systems aim to endow the chatbot agent with more anthropomorphic traits for human-like interactions. |
| Approach: | They propose a method to generate personalized dialogues using latent-space energy-based models by using a latent space energy-model. |
| Outcome: | The proposed method outperforms baselines in personality controllability and response quality. |
InternalInspector I2: Robust Confidence Estimation in LLMs through Internal States (2024.findings-emnlp)
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Mohammad Beigi, Ying Shen, Runing Yang, Zihao Lin, Qifan Wang, Ankith Mohan, Jianfeng He, Ming Jin, Chang-Tien Lu, Lifu Huang
| Challenge: | Large Language Models (LLMs) often struggle with generating reliable outputs, often producing high-confidence inaccuracies known as hallucinations. |
| Approach: | They propose a framework that leverages contrastive learning on internal states including attention states, feed-forward states, and activation states of all layers to enhance confidence estimation in LLMs. |
| Outcome: | The framework outperforms existing methods in the hallucination detection benchmark HaluEval and the previous methods at the same time. |
Weakly Supervised Contrastive Learning for Chest X-Ray Report Generation (2021.findings-emnlp)
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| Challenge: | Radiology report generation aims at generating descriptive text from radiology images automatically. |
| Approach: | They propose a weakly supervised contrastive loss method that generates descriptive text from radiology images automatically. |
| Outcome: | The proposed method outperforms previous work on correctness and text generation metrics for two public benchmarks. |
Probing the Multi-turn Planning Capabilities of LLMs via 20 Question Games (2024.acl-long)
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| Challenge: | Large language models (LLMs) are effective at answering clear questions but when faced with ambiguous queries they act unpredictably and produce incorrect outputs. |
| Approach: | They propose to use a surrogate problem to assess an LLMs’s ability to deduce an entity unknown to itself, but revealed to a judge, by asking the judge a series of queries. |
| Outcome: | The proposed model outperforms human players on the entity-deducing task by a large margin. |
Culture-Aware Machine Translation in Large Language Models: Benchmarking and Investigation (2026.acl-long)
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| Challenge: | Existing studies on large language models focus on literal-level translation quality, such as adequacy and fluency. |
| Approach: | They propose a Culture-Aware Novel-Driven Parallel Dataset for Machine Translation and a multi-dimensional evaluation framework for assessing cultural translation quality. |
| Outcome: | The proposed model improves evaluation reliability in LLM-as-a-judge scenarios under culture-aware constraints. |
Task-Related In-Context Learning (2026.findings-acl)
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| Challenge: | Standard in-context learning assumes identical output spaces between test and retrieval datasets . however, in practice, these datasets can be fully aligned, partially alignes, or fully disjoint in label space . |
| Approach: | They propose a framework for in-context learning under output-space mismatch . they identify demonstrations relevant to the test label space via a Bayesian probabilistic criterion . |
| Outcome: | The proposed framework achieves state-of-the-art results across three LLMs, three task types, and four datasets. |
LLMs know their vulnerabilities: Uncover Safety Gaps through Natural Distribution Shifts (2025.acl-long)
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Qibing Ren, Hao Li, Dongrui Liu, Zhanxu Xie, Xiaoya Lu, Yu Qiao, Lei Sha, Junchi Yan, Lizhuang Ma, Jing Shao
| Challenge: | Current safety training focuses on teaching models to reject harmful queries, but recent research shows that adversarial attacks or jailbreak methods bypass these safety mechanisms. |
| Approach: | They propose to use a new attack method to craft multi-turn toxic prompts that gradually lead LLMs to reveal unsafe content. |
| Outcome: | The proposed method outperforms existing methods in diversity, effectiveness, and efficiency across aligned LLMs. |
ProsocialDialog: A Prosocial Backbone for Conversational Agents (2022.emnlp-main)
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Hyunwoo Kim, Youngjae Yu, Liwei Jiang, Ximing Lu, Daniel Khashabi, Gunhee Kim, Yejin Choi, Maarten Sap
| Challenge: | Existing dialogue systems fail to respond properly to potentially unsafe user utterances . existing systems either ignore or passively agree with unsafe content . |
| Approach: | They introduce a dataset to teach conversational agents to respond to problematic content following social norms. |
| Outcome: | The proposed dataset shows that ProsocialDialog generates more socially acceptable dialogues than existing models. |
Crossing the Aisle: Unveiling Partisan and Counter-Partisan Events in News Reporting (2023.findings-emnlp)
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| Challenge: | Prior work in NLP has only studied media bias via linguistic style and word usage. |
| Approach: | They annotate a dataset containing 8,511 (counter-)partisan event annotations in 304 news articles from ideologically diverse media outlets. |
| Outcome: | The proposed dataset contains 8,511 (counter-)partisan event annotations in 304 news articles from ideologically diverse media outlets. |
Layer-Aware Representation Filtering: Purifying Finetuning Data to Preserve LLM Safety Alignment (2025.emnlp-main)
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| Challenge: | Recent studies show that fine-tuning with benign data can compromise safety of aligned LLMs. |
| Approach: | They propose a Layer-Aware Representation Filtering method that detects safety-degrading layers within the LLM and leverages their representations to detect them. |
| Outcome: | The proposed method can detect safety-degrading features in benign data and remove them from the model. |
Multiple Text Style Transfer by using Word-level Conditional Generative Adversarial Network with Two-Phase Training (D19-1)
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| Challenge: | Generative adversarial network (GAN) is a popular model for text style transfer . but, training GAN often suffers from mode collapse problem, which causes that the transferred text is little related to the original text. |
| Approach: | They propose a non-parallel text style transfer model with a word-level conditional architecture and a two-phase training procedure to maintain style-unrelated words while changing others. |
| Outcome: | The proposed model outperforms state-of-the-art models on three real-world datasets in transfer accuracy and fluency. |
PDR: A Plug-and-Play Positional Decay Framework for LLM Pre-training Data Detection (2026.acl-long)
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| Challenge: | Existing likelihood-based methods for detecting pretraining data are limited in black-box, zero-shot settings. |
| Approach: | They propose a training-free and plug-and-play framework that reweights token-level scores to amplify distinct signals from early positions while suppressing noise from later ones. |
| Outcome: | The proposed framework amplifys signals from early positions while suppressing noise from later positions. |
RedOne 2.0: Rethinking Domain-specific LLM Post-Training in Social Networking Services (2026.acl-industry)
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Fei zhao, Chonggang Lu, Haofu Qian, Fangcheng Shi, Zijie Meng, Jianzhao Huang, Zheyong Xie, Shaosheng Cao
| Challenge: | Social networking services (SNS) are critical infrastructure for global interaction . supervised fine-tuning (SFT) can improve in-domain performance, but it often induces a ”seesaw” trade-off with out-of-domain robustness . |
| Approach: | They propose an SNS-oriented LLM with a progressive, RL-prioritized post-training paradigm for fast and stable adaptation. |
| Outcome: | The proposed model improves over the previous 7B model by 2.41 on average . it also yields an 8.74 average gain over its Qwen3-4B base . |
Alignment with Fill-In-the-Middle for Enhancing Code Generation (2025.emnlp-main)
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Houxing Ren, Zimu Lu, Weikang Shi, Haotian Hou, Yunqiao Yang, Ke Wang, Aojun Zhou, Junting Pan, Mingjie Zhan, Hongsheng Li
| Challenge: | Existing methods for generating test cases with limited training data are not reliable and may be counterproductive. |
| Approach: | They propose a method that splits code snippets into smaller, granular blocks, creating more diverse DPO pairs from the same test cases. |
| Outcome: | The proposed approach shows significant improvements in code generation tasks on benchmark datasets such as HumanEval (+), MBPP (+), and APPS. |
LABO: Towards Learning Optimal Label Regularization via Bi-level Optimization (2023.findings-acl)
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| Challenge: | Existing methods for regularizing deep neural networks rely on weight decay, dropout, batch/layer normalization to converge faster and generalize. |
| Approach: | They propose a framework for training with label regularization which includes conventional LS but can also model instance-specific variants. |
| Outcome: | The proposed approach consistently yields better results than conventional regularization on seven machine translation and three image classification tasks while maintaining training efficiency. |
Inference Time Style Control for Summarization (2021.naacl-main)
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| Challenge: | Existing methods to generate summaries of different styles without training separate models are lacking parallel data and expensive (re)training. |
| Approach: | They propose two methods that can be deployed during summary decoding on any pre-trained Transformer-based summarization model. |
| Outcome: | The proposed methods generate news headlines with various ideological leanings while still informative. |
MONTROSE: LLM-driven Monte Carlo Tree Search Self-Refinement for Cross-Domain Rumor Detection (2025.findings-acl)
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| Challenge: | Existing feature alignment methods are susceptible to task interference during training. |
| Approach: | MONTROSE is a cross-domain rumor detection method that generates high-quality synthetic data for the target domain and a domain-sharpness-aware approach to train models with these synthetic data. |
| Outcome: | Experiments show that MONTROSE improves in cross-domain rumor detection. |
ViDove: A Translation Agent System with Multimodal Context and Memory-Augmented Reasoning (2025.emnlp-demos)
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Yichen Lu, Wei Dai, Jiaen Liu, Ching Wing Kwok, Zongheng Wu, Xudong Xiao, Ao Sun, Sheng Fu, Jianyuan Zhan, Yian Wang, Takatomo Saito, Sicheng Lai
| Challenge: | Recent advances in Large Language Models (LLMs) have demonstrated remarkable capabilities in Machine Translation (MT) tasks. |
| Approach: | They propose a translation agent system designed for multimodal input that leverages visual and contextual background information to enhance the translation process. |
| Outcome: | The proposed translation agent achieves significantly higher translation quality in subtitle generation and general translation tasks compared to previous state-of-the-art systems. |
Mementos: A Comprehensive Benchmark for Multimodal Large Language Model Reasoning over Image Sequences (2024.acl-long)
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Xiyao Wang, Yuhang Zhou, Xiaoyu Liu, Hongjin Lu, Yuancheng Xu, Feihong He, Jaehong Yoon, Taixi Lu, Fuxiao Liu, Gedas Bertasius, Mohit Bansal, Huaxiu Yao, Furong Huang
| Challenge: | Multimodal Large Language Models (MLLMs) have demonstrated proficiency in handling a variety of visual-language tasks, but their ability to extrapolate from image sequences has been less investigated. |
| Approach: | They propose a new benchmark to assess MLLMs’ sequential image reasoning abilities. |
| Outcome: | The proposed benchmark features 4,761 diverse image sequences with varying lengths. |
BehaviorSFT: Behavioral Token Conditioning for Health Agents Across the Proactivity Spectrum (2025.findings-emnlp)
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Yubin Kim, Zhiyuan Hu, Hyewon Jeong, Eugene W Park, Shuyue Stella Li, Chanwoo Park, Shiyun Xiong, MingYu Lu, Hyeonhoon Lee, Xin Liu, Daniel McDuff, Cynthia Breazeal, Samir Tulebaev, Hae Won Park
| Challenge: | Large Language Models (LLMs) struggle with proactive engagement, authors say . a blind clinical evaluation confirmed that trained agents exhibit more realistic clinical behavior . |
| Approach: | They propose a training strategy using behavioral tokens to explicitly condition LLMs for dynamic behavioral selection. |
| Outcome: | The proposed training strategy boosts performance on both benchmarks. |
From Generalist to Specialist: A Survey of Large Language Models for Chemistry (2025.coling-main)
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| Challenge: | Existing studies on pretraining of LLMs on extensive web-based texts are insufficient for advanced scientific discovery, especially in chemistry. |
| Approach: | They outline methodologies for incorporating domain-specific chemistry knowledge and multi-modal information into LLMs and conceptualize chemistry LLM agents using chemistry tools. |
| Outcome: | The proposed models are based on domain-specific chemistry knowledge and multi-modal information and are capable of accelerating scientific research. |
Coarse-to-Fine Multimodal Information Selection for Video Speaking Style Recognition with Large Language Models (2026.findings-acl)
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| Challenge: | Video speaking style recognition (VSSR) aims to classify conversations into different types . integrating all multimodal data yields suboptimal results, authors say . |
| Approach: | They propose a framework that allows users to obtain multimodal data via coarse-to-fine selection . they propose to use visual captions and textual dialogues to integrate multimodal information . |
| Outcome: | The proposed framework outperforms existing training-free approaches and most training-based methods on multiple datasets. |
LPO: Towards Accurate GUI Agent Interaction via Location Preference Optimization (2026.findings-acl)
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Jiaqi Tang, Yu Xia, Yi-Feng Wu, Yuwei Hu, Chen Yuhui, Qing-Guo Chen, Xiaogang Xu, Xiangyu Wu, Hao LU, Yanqing Ma, Shiyin Lu, Qifeng Chen
| Challenge: | Existing strategies for spatial localization are limited due to their limited capacity to perceive positional data. |
| Approach: | They propose a location-based approach that leverages locational data to optimize interaction preferences. |
| Outcome: | The proposed approach achieves SOTA results across offline benchmarks and real-world evaluations. |
Rule or Story, Which is a Better Commonsense Expression for Talking with Large Language Models? (2024.acl-long)
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| Challenge: | Experimental results show that stories outperform rules as the expression for retrieving commonsense from LLMs, exhibiting higher generation confidence and commonsensense accuracy. |
| Approach: | They investigate the commonsense ability of large language models expressed through stories and rules to retrieve commonsensing knowledge from LLMs. |
| Outcome: | The stories outperform rules as commonsense expressions on 28 commonsensense QA datasets, exhibiting higher generation confidence and commonsence accuracy. |
Handling Missing Entities in Zero-Shot Named Entity Recognition: Integrated Recall and Retrieval Augmentation (2025.naacl-long)
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| Challenge: | Zero-shot Named Entity Recognition (ZS-NER) aims to recognize entities in unseen domains without specific annotated data. |
| Approach: | They propose a novel two-stage framework leveraging large language model techniques to improve the ZS-NER’s recall rate. |
| Outcome: | The proposed framework improves the ZS-NER’s recall rate and accuracy by incorporating a large language model. |
Microblog Conversation Recommendation via Joint Modeling of Topics and Discourse (N18-1)
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| Challenge: | Existing methods for recommendation focus on content of individual posts, but we exploit both context and user content and behavior preferences. |
| Approach: | They propose a method that captures conversational context and user content and behavior preferences. |
| Outcome: | The proposed method outperforms methods that only model content without considering discourse on two Twitter datasets. |
Neural Chinese Address Parsing (N19-1)
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| Challenge: | Recent research shows that systems that perform address parsing can be useful for building e-commerce or product recommendation systems. |
| Approach: | They propose a task of parsing Chinese addresses into semantically meaningful chunks using a linear-chain structure. |
| Outcome: | The proposed model is able to capture complex dependencies between labels that cannot be readily captured by a simple linear-chain structure. |
Skill-Aware Data Selection and Fine-Tuning for Data-Efficient Reasoning Distillation (2026.acl-short)
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| Challenge: | Large reasoning models such as DeepSeek-R1 and their distilled variants achieve impressive performance on complex reasoning tasks, yet their costs remain substantial. |
| Approach: | They propose a skill-centric distillation framework that efficiently transfers reasoning ability to weaker models with two components: (1) Skill-based data selection, which prioritizes examples targeting the student model’s weaker skills, and (2) Skillaware fine-tuning, which encourages explicit skill decomposition during problem solving. |
| Outcome: | The proposed framework surpasses baselines on Qwen3-4B and Qwend3-8B and focuses on skills emphasized during training. |
Word-Conditioned 3D American Sign Language Motion Generation (2024.findings-emnlp)
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| Challenge: | Sign words are the building blocks of any sign language. |
| Approach: | They propose a word-conditioned 3D American Sign Language (ASL) generation model that synthesizes real-time motion sequences for sign words. |
| Outcome: | The proposed model outperforms the baseline model in the task of sign word generation. |
Tree Representations in Transition System for RST Parsing (2020.coling-main)
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| Challenge: | Existing studies have proposed a series of actions to build a right-heavy binarized tree for RST parsing, but the nodes of the binary-nuclear relations have the same nuclear type as those of the multi-nullar relations. |
| Approach: | They propose a nuclear type for multi-nuclear relations and a new action to construct a multi-branch tree. |
| Outcome: | The proposed nuclear type and action are more capable of capturing multi-nuclear relation and the joint action is more suitable than the separate one. |
Enhanced Language Model Truthfulness with Learnable Intervention and Uncertainty Expression (2024.findings-acl)
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| Challenge: | Large language models (LLMs) generate long-form and coherent text, yet they often hallucinate facts, which undermines their reliability. |
| Approach: | They propose a Learnable Intervention method for Truthfulness Optimization that automatically identifies the optimal intervention intensity tailored to each query context. |
| Outcome: | Experiments on multiple LLMs and question-answering datasets show that LITO improves truthfulness while preserving task accuracy. |
Towards Robust Real-World Spreadsheet Understanding with Multi-Agent Multi-Format Reasoning (2026.acl-long)
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| Challenge: | Spreadsheets are among the most widely used data formats in real-world applications . existing large language models treat tables as plain text, overlooking layout cues and visual semantics. |
| Approach: | They propose a two-stage multi-agent framework for spreadsheet understanding that adopts a step-by-step reading and reasoning paradigm. |
| Outcome: | Extensive experiments on two spreadsheet datasets show the proposed framework outperforms existing methods on Spreadsheet Bench. |
MAS-Bench: A Unified Benchmark for Shortcut-Augmented Hybrid Mobile GUI Agents (2026.acl-long)
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Pengxiang Zhao, Guangyi Liu, Yaozhen Liang, Weiqing He, Zhengxi Lu, WenHao Wang, Yuehao Huang, Yuxiang Chai, Zhaolu Kang, Yaxuan Guo, Hao Wang, Kexin Zhang, Liang Liu, Yong Liu
| Challenge: | Shortcuts such as APIs and deep-links have emerged as efficient complements to flexible GUI operations, but systematic evaluation of GUI–shortcut hybrid agents remains underexplored. |
| Approach: | They propose a benchmark that evaluates GUI-shortcut hybrid agents with a specific focus on the mobile domain. |
| Outcome: | MAS-Bench evaluates agent's ability to generate shortcuts by discovering and creating reusable, low-cost workflows. |
UER: An Open-Source Toolkit for Pre-training Models (D19-3)
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Zhe Zhao, Hui Chen, Jinbin Zhang, Xin Zhao, Tao Liu, Wei Lu, Xi Chen, Haotang Deng, Qi Ju, Xiaoyong Du
| Challenge: | Existing work on pre-training models have shown that it is important to use a framework to deploy various pre- training models efficiently. |
| Approach: | They propose an assemble-on-demand pre-training toolkit that assembles pre-trained models on demand and encapsulates them with rich modules. |
| Outcome: | The proposed framework can reproduce state-of-the-art models or develop models that remain unexplored. |
SELF-GUARD: Empower the LLM to Safeguard Itself (2024.naacl-long)
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| Challenge: | Recent studies have investigated methods to improve the safety of large language models (LLMs) safety training involves fine-tuning the LLM with adversarial samples, which activate the LRM’s capabilities against jailbreak. |
| Approach: | They propose a safety training approach that integrates safety training and safeguards to train the LLM to perform harmfulness detection on its own outputs. |
| Outcome: | The proposed method reduces harmful output and adds a [harmful] or [harmless] tag to the end of the LLM's response. |
Towards Universal Dialogue State Tracking (D18-1)
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| Challenge: | Existing approaches to dialogue state tracking are difficult to scale to large dialogue domains. |
| Approach: | They propose a universal dialogue state tracker that is independent of the number of values and shares parameters across all slots. |
| Outcome: | The proposed system significantly outperforms state-of-the-art approaches on two datasets. |
A Survey of Deep Learning for Mathematical Reasoning (2023.acl-long)
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| Challenge: | a survey of deep learning for mathematical reasoning examines the field . a comprehensive reading list is provided to assist readers interested in the field. |
| Approach: | They present a survey of deep learning for mathematical reasoning over the past decade . they outline directions for future research and highlight potential for further exploration . |
| Outcome: | The proposed framework is based on the results of a decade-long survey of deep learning for mathematical reasoning. |
CSDS: A Fine-Grained Chinese Dataset for Customer Service Dialogue Summarization (2021.emnlp-main)
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| Challenge: | Existing summarization methods are prone to generate redundant and incoherent summaries, causing the performance to be worse. |
| Approach: | They propose a Chinese dataset for Customer Service Dialogue Summarization (CSDS) that provides role-oriented summaries to acquire different speakers' viewpoints. |
| Outcome: | The proposed dataset improves the abstractive summaries in two aspects . it also provides role-oriented summary to acquire different speakers’ viewpoints . |
Learning Global Controller in Latent Space for Parameter-Efficient Fine-Tuning (2024.acl-long)
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Zeqi Tan, Yongliang Shen, Xiaoxia Cheng, Chang Zong, Wenqi Zhang, Jian Shao, Weiming Lu, Yueting Zhuang
| Challenge: | Large language models (LLMs) have shown remarkable performance, but their training costs are exorbitant. |
| Approach: | They propose a parameter-efficient method for exploring optimal solutions within latent space by using latent units to extract input representations from LLMs. |
| Outcome: | The proposed method improves performance on a range of natural language processing tasks. |
Shoes-ACOSI: A Dataset for Aspect-Based Sentiment Analysis with Implicit Opinion Extraction (2024.findings-emnlp)
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| Challenge: | Prior work in ABSA has investigated opinion extraction as an important subtask, but these works only label concise, *explicitly*-stated opinion spans. |
| Approach: | They propose a new ABSA dataset with implicit opinion span annotations . they use paragraph-length inputs and prompted-LLM baselines to evaluate the dataset . |
| Outcome: | The proposed dataset presents significant challenges for fully-supervised models and LLMs. |
On Controlling Fallback Responses for Grounded Dialogue Generation (2022.findings-acl)
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| Challenge: | Existing knowledge grounded dialogue frameworks assume that the user intention is always answerable. |
| Approach: | They propose a framework that automatically generates a control token with the generator to bias the succeeding response towards informativeness for answerable contexts and fallback for unanswerable context. |
| Outcome: | The proposed framework incorporates fallback responses to respond to unanswerable contexts in an informative manner while retaining informativeness for answerable context. |
On the Editability of Delta Parameters in Post-Trained Models (2026.findings-acl)
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| Challenge: | Several studies have explored delta parameter properties via pruning, quantization, low-rank approximation, and extrapolation, but what properties of delta parameters are essential for maintaining performance? |
| Approach: | They propose to examine delta parameter properties along magnitude and sign . they propose to use a loss-based local surrogate analysis to examine editing effects . |
| Outcome: | The proposed analysis shows that delta parameters can be edited while maintaining performance. |
Query-based Instance Discrimination Network for Relational Triple Extraction (2022.emnlp-main)
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| Challenge: | Recent approaches to extract relational triples from open domain texts suffer from error propagation, relation redundancy and lack of high-level connections. |
| Approach: | They propose a query-based approach to construct instance-level representations for relational triples . they use query embeddings and token embeddables to extract all types of triples in one step . |
| Outcome: | The proposed method achieves state-of-the-art on five widely used benchmarks. |
AutoAlign: Get Your LLM Aligned with Minimal Annotations (2025.acl-demo)
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Xinyu Lu, Dong Xu, Chunkang Zhang, Xinyan Guan, Junxiang Wang, Qingyu Zhang, Pengbo Wang, Yingzhi Mao, Hao Xiang, Xueru Wen, Zichao Li, Yaojie Lu, Hongyu Lin, Le Sun, Xianpei Han
| Challenge: | Automated Alignment (ALM) is a set of algorithms designed to align Large Language Models (LLMs) with human intentions and values while minimizing manual intervention. |
| Approach: | They propose an open-source toolkit that integrates mainstream automated algorithms through a consistent interface and an accessible workflow supporting one-click execution for prompt synthesis and automatic alignment signal construction. |
| Outcome: | The proposed framework enables easy reproduction of existing results through extensive benchmarks and facilitates the development of novel approaches via modular components. |
Towards Context-Robust LLMs: A Gated Representation Fine-tuning Approach (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) enhanced with external contexts face challenges in handling imperfect evidence. |
| Approach: | They propose a framework that can balance internal knowledge with external contexts . they propose gating mechanisms and low-rank representation adapters to adjust hidden representations based on a lightweight intervention function . |
| Outcome: | The proposed model can effectively balance internal knowledge with external context, similar to human cognitive processes. |
Towards Boosting the Open-Domain Chatbot with Human Feedback (2023.acl-long)
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| Challenge: | Existing frameworks for pre-training open-domain dialogue models with social media comments generate coherent replies but have difficulties producing engaging responses. |
| Approach: | They propose a framework to boost the open-domain chatbot by leveraging human feedback and annotating the model's candidate responses. |
| Outcome: | The proposed framework boosts the open-domain chatbot by leveraging human demonstrated responses and leveraging the implicit preference in the data collection process. |
CHAIRO: Contextual Hierarchical Analogical Induction and Reasoning Optimization for LLMs (2026.acl-long)
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| Challenge: | Recent advances in large language models (LLMs) have enabled more sophisticated content moderation, but these methods lack generalization, interpretability, and adaptability to unseen or ambiguous cases. |
| Approach: | They propose a new moderation framework that leverages analogical examples to enhance rule induction and decision reliability. |
| Outcome: | The proposed method outperforms rule-injected fine-tuning baselines and multi-stage static RAG pipelines in terms of moderation accuracy and rule quality. |
SCITAT: A Question Answering Benchmark for Scientific Tables and Text Covering Diverse Reasoning Types (2025.findings-acl)
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Xuanliang Zhang, Dingzirui Wang, Baoxin Wang, Longxu Dou, Xinyuan Lu, Keyan Xu, Dayong Wu, Qingfu Zhu
| Challenge: | Existing scientific question answering datasets lack diverse reasoning types and neglect relevance between tables and text. |
| Approach: | They propose a scientific question answering benchmark for scientific tables and text with diverse reasoning types (SCITAT) to address these challenges, they propose QA benchmark which incorporates tables and texts to ensure that the questions encompass both tables and textes. |
| Outcome: | The proposed benchmark improves by 4.1% over baselines on SCITAT. |
Large Dual Encoders Are Generalizable Retrievers (2022.emnlp-main)
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Jianmo Ni, Chen Qu, Jing Lu, Zhuyun Dai, Gustavo Hernandez Abrego, Ji Ma, Vincent Zhao, Yi Luan, Keith Hall, Ming-Wei Chang, Yinfei Yang
| Challenge: | Experimental results show that dual encoders outperform sparse and dense retrievers on the BEIR dataset significantly. |
| Approach: | They challenge belief that bottleneck layer is too limited for out-of-domain generalization . they scale up the model while keeping bottleneck as a single dot-product with a fixed size . |
| Outcome: | The proposed model outperforms sparse and dense retrievers on the BEIR dataset significantly. |
X-Boundary: Establishing Exact Safety Boundary to Shield LLMs from Jailbreak Attacks without Compromising Usability (2025.findings-emnlp)
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| Challenge: | Existing methods for enhancing LLM security compromise usability, study finds . boundary-safe representations close to harmful representations are disrupted, resulting in usability decline . |
| Approach: | They propose a method to push harmful representations away from boundary-safe representations and obtain an exact distinction boundary. |
| Outcome: | The proposed method reduces over-refusal rate and maintains general capability . it pushes harmful representations away from boundary-safe representations, thereby reducing usability. |
TART: An Open-Source Tool-Augmented Framework for Explainable Table-based Reasoning (2025.findings-naacl)
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| Challenge: | Current Large Language Models lack ability to understand table structures and apply precise numerical reasoning. |
| Approach: | They propose a tool-augmented reasoning framework for table-based tasks that integrates LLMs with specialized tools. |
| Outcome: | The proposed framework improves on the TOOLTAB dataset, a benchmark for LLMs in table–tool integration. |
Are All Prompt Components Value-Neutral? Understanding the Heterogeneous Adversarial Robustness of Dissected Prompt in LLMs (2026.eacl-long)
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Yujia Zheng, Tianhao Li, Haotian Huang, Tianyu Zeng, Jingyu Lu, Chuangxin Chu, Yuekai Huang, Ziyou Jiang, Qian Xiong, Yuyao Ge, Mingyang Li
| Challenge: | Existing studies treat prompts as flat text, overlooking their internal structure, and different components within a prompt contribute unequally to robustness. |
| Approach: | They propose a framework that decomposes prompts into functional components and a method that selectively modifies components to expose component-wise vulnerabilities. |
| Outcome: | The proposed framework exposes component-wise vulnerabilities while ensuring linguistic plausibility through perplexity-based filtering. |
User Feedback Alignment for LLM-powered Exploration in Large-scale Recommendation Systems (2025.acl-industry)
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Jianling Wang, Yifan Liu, Yinghao Sun, Xuejian Ma, Yueqi Wang, He Ma, Zhengyang Su, Minmin Chen, Mingyan Gao, Onkar Dalal, Ed H. Chi, Lichan Hong, Ningren Han, Haokai Lu
| Challenge: | Large Language Models (LLMs) can be used to broaden user experiences beyond established preferences and reinforce feedback loops. |
| Approach: | They propose a hierarchical approach that combines hierarchic planning with LLM inference-time scaling to improve recommendation relevancy without compromising novelty. |
| Outcome: | The proposed approach shows significant gains in both user satisfaction and exploration diversity. |
Argument Generation with Retrieval, Planning, and Realization (P19-1)
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| Challenge: | a novel argument generation framework is used to generate counter-arguments . CANDELA uses a text planning decoder to retrieve arguments of different perspectives . |
| Approach: | They propose a powerful retrieval system and a novel two-step argument generation framework . they use a retrieval-based retrieval platform indexed with 12 million articles from Wikipedia . |
| Outcome: | The proposed framework yields higher BLEU, ROUGE, and METEOR scores than state-of-the-art models. |
TRUE-UIE: Two Universal Relations Unify Information Extraction Tasks (2024.naacl-long)
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| Challenge: | Information extraction (IE) tasks have a variety of schemas and objectives that differ across tasks. |
| Approach: | They propose a paradigm where all IE tasks are aligned to learn the same goals . they use two universal relations to extract mention spans and type recognition . |
| Outcome: | The proposed model achieves state-of-the-art on established benchmarks spanning 16 datasets, spanning 7 diverse IE tasks. |
AscendKernelGen: LLM-Driven Kernel Generation for NPUs (2026.findings-acl)
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Xinzi Cao, Jianyang Zhai, Pengfei Li, Zhiheng Hu, Cen Yan, null Mubingxu, Guanghuan Fang, Bin She, Jiayu Li, Yihan Su, Dongyang Tao, Feidiao Yang, Chang-Dong Wang, Yutong Lu, Weicheng Xue, Bin Zhou, Yonghong Tian
| Challenge: | Neural Processing Units (NPUs) are critical for AI infrastructure, but their development remains a bottleneck due to vendor-specific Domain-Specific Languages (DSLs). |
| Approach: | They propose a framework for NPU kernel development that bridges the gap in hardware-specific coding . compiler success on complex Level-2 kernels improves from 0% to 95.5%, they say . |
| Outcome: | The proposed framework bridges the gap in hardware-specific coding, showing a near-zero success rate on complex kernels. |
Retrieved Sequence Augmentation for Protein Representation Learning (2024.emnlp-main)
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Chang Ma, Haiteng Zhao, Lin Zheng, Jiayi Xin, Qintong Li, Lijun Wu, Zhihong Deng, Yang Lu, Qi Liu, Sheng Wang, Lingpeng Kong
| Challenge: | Using multiple sequence alignments (MSA) to extract evolutionary knowledge is limited. |
| Approach: | They propose to use multiple sequence alignments to augment protein representations . they propose to employ Retrieved Sequence Augmentation to enhance protein representation learning . |
| Outcome: | The proposed method surpasses MSA Transformer by 5% in structural and property prediction tasks while being 373 times faster. |
Neural Conversation Recommendation with Online Interaction Modeling (D19-1)
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| Challenge: | Existing models that only use lexical features and ignore past user interactions in online conversations are inadequate to identify and engage in online discussions. |
| Approach: | They propose a framework that automatically recommends conversations based on user's prior conversation behaviors by exploring deep semantic features that measure how a user’s preferences match an ongoing conversation’s context. |
| Outcome: | The proposed model outperforms state-of-the-art models on two large-scale datasets from Twitter and Reddit showing that it incorporates deep semantic features that measure how a user’s preferences match an ongoing conversation’s context. |
LLM-Based Explicit Models of Opponents for Multi-Agent Games (2025.naacl-long)
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| Challenge: | Existing approaches to model adversarial and cooperative interactions often focus on treating other agents as separate entities with their own intentions and strategies. |
| Approach: | They propose a model of opponents based on Large Language Models (LLMs) that constructs an individual model for each opponent and aligns these models working in synergy through a bi-level feedback-refinement framework. |
| Outcome: | The proposed model outperforms single-model approaches in multi-player deduction games, showing that it significantly enhances agents’ decision-making. |
An Empirical Analysis of Leveraging Knowledge for Low-Resource Task-Oriented Semantic Parsing (2023.findings-acl)
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Mayank Kulkarni, Aoxiao Zhong, Nicolas Guenon des mesnards, Sahar Movaghati, Mukund Sridhar, He Xie, Jianhua Lu
| Challenge: | Task-oriented semantic parsing is a new approach to represent the meaning of user requests with arbitrarily nested semantics. |
| Approach: | They propose to use knowledge-enhanced encoders to parse user requests with arbitrarily nested semantics. |
| Outcome: | The proposed model improves performance in low-resource and low-compute settings. |
STRICT: Stress-Test of Rendering Image Containing Text (2025.emnlp-main)
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| Challenge: | Despite the advances in diffusion models, the generation of coherent text remains a major bottleneck. |
| Approach: | They propose a benchmark to test the ability of diffusion models to render coherent text in images. |
| Outcome: | The proposed model fails to generate coherent and legible text in images despite its iterative nature . the model fails in both the maximum length of readable text and correctness and legibility of the generated text . |
STaR-SQL: Self-Taught Reasoner for Text-to-SQL (2025.acl-long)
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| Challenge: | Existing methods for generating step-by-step “chain-of-thought” rationales are limited to text-to-SQL. |
| Approach: | They propose a method that prompts SQL query generation to produce reasoning steps for SQL queries and fine-tunes it on rationales that lead to correct outcomes. |
| Outcome: | The proposed method outperforms agent-like prompting methods on the Spider benchmark. |
Enhancing Zero-Shot Chain-of-Thought Reasoning in Large Language Models through Logic (2024.lrec-main)
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| Challenge: | Experimental evaluations of large language models demonstrate the efficacy of enhanced reasoning by logic. |
| Approach: | They propose a framework that uses symbolic logic to verify and rectify reasoning steps by steps. |
| Outcome: | The proposed framework improves the zero-shot chain-of-thought reasoning ability of large language models by verifying and rectifying the reasoning steps step by step. |
MAST: A Multi-View Alignment Strategy for Optimal Transport-Based Contrastive Clustering of Short Text (2026.findings-acl)
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| Challenge: | Short text clustering has gained significant prominence due to its ubiquity in real-world applications. |
| Approach: | They propose a multi-view alignment strategy with transport-based clustering that integrates structural views to capture multi-granularity semantic features. |
| Outcome: | Experiments show that MAST outperforms state-of-the-art methods on benchmark datasets. |
LNN-EL: A Neuro-Symbolic Approach to Short-text Entity Linking (2021.acl-long)
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Hang Jiang, Sairam Gurajada, Qiuhao Lu, Sumit Neelam, Lucian Popa, Prithviraj Sen, Yunyao Li, Alexander Gray
| Challenge: | Existing work deals with EL in the context of longer text, such as a sentence. |
| Approach: | They propose a neuro-symbolic approach that uses interpretable rules based on first-order logic to achieve better performance with black-box neural approaches. |
| Outcome: | The proposed approach achieves better performance than heuristics-based approaches on short-text EL . it can easily blend existing rule templates with multiple types of features, and even with scores resulting from previous EL methods. |
MedCPI: A Construct–Personalize–Integrate Framework for KG-enhanced Clinical Prediction (2026.findings-acl)
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| Challenge: | Existing KG-enhanced approaches to clinical prediction are limited . existing approaches to personalize and integrate knowledge are weakly controlled . |
| Approach: | They propose a framework to integrate medical knowledge graphs into EHRs to support KG-enhanced clinical prediction. |
| Outcome: | The proposed framework improves on MIMIC-III and MIMIC IV tasks. |
UniMSE: Towards Unified Multimodal Sentiment Analysis and Emotion Recognition (2022.emnlp-main)
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| Challenge: | Existing studies study sentiment and emotion separately and do not fully exploit the complementary knowledge behind the two. |
| Approach: | They propose a multimodal sentiment knowledge-sharing framework that unifies MSA and ERC tasks from features, labels, and models. |
| Outcome: | The proposed framework achieves consistent improvements on four public benchmark datasets on MOSI, MOSEI, MELD, and IEMOCAP. |
Academics Can Contribute to Domain-Specialized Language Models (2024.emnlp-main)
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Mark Dredze, Genta Winata, Prabhanjan Kambadur, Shijie Wu, Ozan Irsoy, Steven Lu, Vadim Dabravolski, David Rosenberg, Sebastian Gehrmann
| Challenge: | Commercially available models dominate academic leaderboards, focusing on creating and adapting general-purpose models . however, general- purpose models often underperform in specialized domains, and domain-specific models yield superior results. |
| Approach: | They advocate for a renewed focus on developing and evaluating domain- and task-specific models . they advocate for an adapted or adapted model that can be used to improve academic leaderboard standings . |
| Outcome: | The proposed model can do well on professional and linguistic examinations, college-level knowledge questions, and collections of reasoning tasks. |
How Do LLMs and VLMs Understand Viewpoint Rotation Without Vision? An Interpretability Study (2026.acl-long)
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Zhen Yang, Ping Jian, Zhongbin Guo, Zuming Zhang, Chengzhi Li, Yonghong Deng, Xinyue Zhang, Wenpeng Lu
| Challenge: | Existing studies on spatial intelligence from the perspective of visual-spatial intelligence have not explored whether visual intelligence alone is sufficient to endow models with spatial intelligence. |
| Approach: | They propose to use a linguistic perspective to investigate spatial intelligence from a theoretical perspective. |
| Outcome: | The proposed model performs poorly on the proposed dataset while human can easily achieve 100% accuracy. |
Experience-Driven Reflective Co-Evolution of Prompts and Heuristics for Autonomous Algorithm Design (2026.findings-acl)
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| Challenge: | Combinatorial optimization has long been dominated by manually engineered heuristics, which require substantial expert intuition and implementation overhead. |
| Approach: | They propose a framework that couples an island migration model with elite selection to maintain population diversity. |
| Outcome: | The proposed framework achieves superior accuracy on the Traveling Salesman and Bin Packing Problems. |
One vs. Many QA Matching with both Word-level and Sentence-level Attention Network (C18-1)
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| Challenge: | Existing studies on question answer matching focus on formal text . however, there exists many scenarios where the QA text is informal . |
| Approach: | They propose a novel QA matching approach using informal text from a product review site. |
| Outcome: | The proposed approach improves word-level and sentence-level attentions for solving the noisy problem in the informal text. |
Don’t Go To Extremes: Revealing the Excessive Sensitivity and Calibration Limitations of LLMs in Implicit Hate Speech Detection (2024.acl-long)
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| Challenge: | Several studies have examined whether large language models exhibit bias or discrimination against individuals or groups in terms of protected attributes like race, gender, or religion. |
| Approach: | They evaluate LLMs' ability to detect implicit hate speech and express confidence in their responses by considering prompt patterns and mainstream uncertainty estimation methods. |
| Outcome: | The proposed models exhibit two extremes: (1) excessive sensitivity towards groups or topics that may cause fairness issues, resulting in misclassifying benign statements as hate speech; (2) confidence scores for each method excessively concentrate on a fixed range, remaining unchanged regardless of the dataset’s complexity. |
Predicting Rewards Alongside Tokens: Non-disruptive Parameter Insertion for Efficient Inference Intervention in Large Language Model (2024.emnlp-main)
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| Challenge: | Existing approaches to fine tune LLMs produce unsafe responses and unreliable reasoning, but this solution introduces substantial time and space overhead due to the separate models required. |
| Approach: | They propose to insert extra parameters into transformer architecture to predict calibration signals along with original LLM output. |
| Outcome: | The proposed model reduces time and space costs while enabling seamless online deployment. |
ToW: Thoughts of Words Improve Reasoning in Large Language Models (2025.naacl-long)
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Zhikun Xu, Ming Shen, Jacob Dineen, Zhaonan Li, Xiao Ye, Shijie Lu, Aswin Rrv, Chitta Baral, Ben Zhou
| Challenge: | Unlike other data augmentation methods, thoughts of words (TOW) views next-word prediction as a core reasoning task and injects fine-grained thoughts into pre-training texts. |
| Approach: | They propose a training-time data-augmentation method called thoughts of words (TOW) that injects fine-grained thoughts directly into a next-word prediction task and teaches the model to understand how the observed next word is related to previous contexts. |
| Outcome: | The proposed method reduces model hallucination by 10% and improves reasoning performance by 7% to 9% on average. |
Good Meta-tasks Make A Better Cross-lingual Meta-transfer Learning for Low-resource Languages (2023.findings-emnlp)
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| Challenge: | Model-agnostic meta-learning has garnered attention as a promising technique for enhancing few-shot cross-lingual transfer learning in low-resource scenarios. |
| Approach: | They propose a Meta-Task Collector-based Cross-lingual Meta-Transfer framework to adapt data selection strategies to construct cross-lingual meta-tasks to reduce language gaps. |
| Outcome: | The proposed framework significantly improves model performance in the target language with minimal annotation costs. |
Better Few-Shot Relation Extraction with Label Prompt Dropout (2022.emnlp-main)
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| Challenge: | Existing studies assume textual labels are always present during learning and prediction. |
| Approach: | They propose a method which randomly drops out textual labels in the learning process. |
| Outcome: | The proposed approach improves the few-shot relation extraction task by randomly dropping out labels in the learning process. |
Adapting LLM Agents with Universal Communication Feedback (2025.findings-naacl)
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| Challenge: | Recent advances in large language models (LLMs) have demonstrated potential for LLM agents. |
| Approach: | They propose a universal buffer and iterative pipeline to store feedback and itersative pipelines to enable LLM agents to explore and update their policy in an environment. |
| Outcome: | The proposed approach outperforms supervised instruction fine-tuning baselines on four datasets. |
Versatile Framework for Song Generation with Prompt-based Control (2025.findings-emnlp)
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Yu Zhang, Wenxiang Guo, Changhao Pan, Zhiyuan Zhu, Ruiqi Li, Jingyu Lu, Rongjie Huang, Ruiyuan Zhang, Zhiqing Hong, Ziyue Jiang, Zhou Zhao
| Challenge: | Existing methods for song generation fail to generate vocals with prompt-based control and proper alignment. |
| Approach: | VersBand is a multi-task song generation framework for synthesizing high-quality songs with prompt-based control. |
| Outcome: | Experimental results show that VersBand performs better than baseline models across multiple song generation tasks. |
MoQAE: Mixed-Precision Quantization for Long-Context LLM Inference via Mixture of Quantization-Aware Experts (2025.acl-long)
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| Challenge: | Existing approaches to optimize large language models for long-context inference are inefficient and consume memory. |
| Approach: | They propose a mixed-precision quantization method via mixture of experts that inputs tokens into router chunk by chunk to reduce inference overhead. |
| Outcome: | The proposed method outperforms state-of-the-art KV cache quantization methods on multiple benchmark datasets. |
Parsing Natural Language into Propositional and First-Order Logic with Dual Reinforcement Learning (2022.coling-1)
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Xuantao Lu, Jingping Liu, Zhouhong Gu, Hanwen Tong, Chenhao Xie, Junyang Huang, Yanghua Xiao, Wenguang Wang
| Challenge: | Existing methods to parse natural language into structured logical expressions have limitations due to paucity of labeled data. |
| Approach: | They propose a scoring model to automatically learn a model-based reward . they also propose introducing a Chinese-PL/FOL dataset to compensate for paucity of labeled data . |
| Outcome: | The proposed model outperforms competitors on several datasets. |
BrainLoc: Brain Signal-Based Object Detection with Multi-modal Alignment (2025.findings-emnlp)
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Jiaqi Duan, Xiaoda Yang, Kaixuan Luan, Hongshun Qiu, Weicai Yan, Xueyi Zhang, Youliang Zhang, Zhaoyang Li, Donglin Huang, JunYu Lu, Ziyue Jiang, Xifeng Yang
| Challenge: | BrainLoc is a lightweight object detection model guided by fMRI signals. |
| Approach: | They propose a brain-based object detection model guided by fMRI signals . they employ a multi-modal alignment strategy that enhances fmr feature extraction . |
| Outcome: | The proposed model improves fMRI-based object detection accuracy and convenience. |
Graph-based Multilingual Product Retrieval in E-Commerce Search (2021.naacl-industry)
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| Challenge: | Modern e-commerce search systems require product retrieval under multilingual scenarios. |
| Approach: | They propose a universal multilingual retrieval system that captures interactions between search queries and items in e-commerce search. |
| Outcome: | The proposed system outperforms state-of-the-art retrieval models on five countries and has been deployed in production for multiple countries. |
VoxMind: An End-to-End Agentic Spoken Dialogue System (2026.acl-long)
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Tianle Liang, Yifu Chen, Shengpeng Ji, Yijun Chen, Zhiyang Jia, Jingyu Lu, Fan Zhuo, Xueyi Pu, Yangzhuo Li, Zhou Zhao
| Challenge: | Existing research on end-to-end spoken dialogue models has focused on core perception and generation, with limited exploration of tool-augmented extensions. |
| Approach: | They propose a framework to equip end-to-end spoken dialogue models with comprehensive agentic abilities by leveraging a 470-hour AgentChat dataset. |
| Outcome: | The proposed framework outperforms Gemini-2.5-Pro on spoken agent tasks while maintaining general conversational quality. |
Libra-VLA: Achieving Learning Equilibrium via Asynchronous Coarse-to-Fine Dual-System (2026.acl-long)
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| Challenge: | Vision-Language-Action models ground high-level semantic instructions into executable physical actions. |
| Approach: | They propose a Coarse-to-Fine Dual-System VLA architecture that decouples learning complexity into a coarse-to fine hierarchy while leveraging structural modularity to implement an asynchronous execution strategy. |
| Outcome: | The proposed architecture decouples learning complexity into a coarse-to-fine hierarchy while leveraging structural modularity to implement an asynchronous execution strategy. |
Empowering Psychotherapy with Large Language Models: Cognitive Distortion Detection through Diagnosis of Thought Prompting (2023.findings-emnlp)
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| Challenge: | Existing systems for mental health support are shallow and heuristic, e.g., analyzing emotions and generating comforting responses. |
| Approach: | They propose to use cognitive distortion detection to perform diagnosis on the patient’s speech via three stages: subjectivity assessment to separate the facts and the thoughts; contrastive reasoning to elicit the reasoning processes supporting and contradicting the thoughts and schema analysis to summarize the cognition schemas. |
| Outcome: | The proposed system improves on ChatGPT for cognitive distortion detection while generating high-quality rationales approved by human experts. |
Tab-CoT: Zero-shot Tabular Chain of Thought (2023.findings-acl)
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| Challenge: | Recent efforts to encourage more structured reasoning procedures to be captured have shown that chain-of-though (CoT) prompting methods can be effective in NLP tasks. |
| Approach: | They propose a tabular-format CoT prompting method that allows the complex reasoning process to be explicitly modeled in a highly structured manner. |
| Outcome: | The proposed method shows impressive performance improvements on a range of reasoning tasks. |
TernaryBERT: Distillation-aware Ultra-low Bit BERT (2020.emnlp-main)
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| Challenge: | Transformer-based pre-training models like BERT are computationally expensive and limited to resource-constrained devices. |
| Approach: | They propose a method which ternarizes the weights in a fine-tuned BERT model. |
| Outcome: | The proposed method outperforms the other methods on the GLUE and SQUAD benchmarks while being 14.9x smaller. |
Narrative Order Aware Story Generation via Bidirectional Pretraining Model with Optimal Transport Reward (2023.findings-emnlp)
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| Challenge: | Existing storytelling systems suffer from insufficient understanding of event correlations and inadequate awareness of event temporal order. |
| Approach: | They propose a narrative order aware framework to generate coherent stories with flashbacks . they propose 'bidirectional pretraining model with Optimal Transport Reward' to improve quality . |
| Outcome: | The proposed framework generates coherent stories with flashbacks with a novel optimal transport reward. |
PINEAPPLE: Personifying INanimate Entities by Acquiring Parallel Personification Data for Learning Enhanced Generation (2022.coling-1)
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Sedrick Scott Keh, Kevin Lu, Varun Gangal, Steven Y. Feng, Harsh Jhamtani, Malihe Alikhani, Eduard Hovy
| Challenge: | Personifications are figures of speech that endow inanimate entities with properties and actions typically seen as requiring animacy. |
| Approach: | They propose to use personification data to train a parallel corpus of personifications . they propose to combine personification-related literalizations with automatic ones . |
| Outcome: | The proposed personification system can generate diverse and creative personifications . it can generate personification-related qualities such as interestingness and animacy . |
TreeRL: LLM Reinforcement Learning with On-Policy Tree Search (2025.acl-long)
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| Challenge: | Existing methods for On-Policy LLM RL typically train a separate process reward model, which suffers from distribution mismatch and reward hacking. |
| Approach: | They propose a reinforcement learning framework that directly incorporates on-policy tree search for RL training. |
| Outcome: | Experiments on math and code reasoning benchmarks show that tree search achieves superior performance compared to traditional ChainRL. |
DiffusionNER: Boundary Diffusion for Named Entity Recognition (2023.acl-long)
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| Challenge: | Named Entity Recognition (NER) tasks are fundamental to many structured information extraction tasks. |
| Approach: | They propose a named entity recognition task that uses a boundary-denoising diffusion process to denoise noisy spans. |
| Outcome: | The proposed method achieves comparable or even better performance than previous state-of-the-art models on flat and nested datasets. |
More Samples or More Prompts? Exploring Effective Few-Shot In-Context Learning for LLMs with In-Context Sampling (2024.findings-naacl)
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Bingsheng Yao, Guiming Chen, Ruishi Zou, Yuxuan Lu, Jiachen Li, Shao Zhang, Yisi Sang, Sijia Liu, James Hendler, Dakuo Wang
| Challenge: | Existing studies on LLM prompting focus on selecting a better set of data samples inside one single prompt input, but why not design and leverage multiple ICL prompts together to further improve the LLM’s performance? |
| Approach: | They propose a low-resource LLM prompting technique to optimize the construction of multiple ICL prompt inputs to produce confident predictions. |
| Outcome: | The proposed technique can produce confident predictions by optimizing the construction of multiple ICL prompt inputs on four NLI datasets and one QA dataset. |
Learning Explicit and Implicit Structures for Targeted Sentiment Analysis (D19-1)
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| Challenge: | Existing research efforts focus on targeting sentiment analysis as a sequence labeling problem, building models that can capture explicit structures in the output space. |
| Approach: | They argue that both implicit and explicit structural information are crucial for building a successful targeted sentiment analysis model. |
| Outcome: | The proposed model outperforms existing models by capturing implicit and explicit structural information. |
MarkLLM: An Open-Source Toolkit for LLM Watermarking (2024.emnlp-demo)
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Leyi Pan, Aiwei Liu, Zhiwei He, Zitian Gao, Xuandong Zhao, Yijian Lu, Binglin Zhou, Shuliang Liu, Xuming Hu, Lijie Wen, Irwin King, Philip Yu
| Challenge: | Large Language Models (LLMs) embed imperceptible yet algorithmically detectable signals in outputs to identify LLM-generated text. |
| Approach: | They propose to develop an open-source toolkit for LLM watermarking that embeds imperceptible yet algorithmically detectable signals in model outputs to identify LLM-generated text. |
| Outcome: | MarkLLM provides a unified framework for implementing LLM watermarking algorithms, while providing user-friendly interfaces to ensure ease of access. |
BACO: A Background Knowledge- and Content-Based Framework for Citing Sentence Generation (2021.acl-long)
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| Challenge: | citing sentences capture salient information in cited papers and the connection between citing and citing papers. |
| Approach: | They propose a BAckground knowledge- and COntent-based framework for citing sentence generation that integrates two types of information: background knowledge and content. |
| Outcome: | The proposed framework outperforms baselines in the citation sentence generation task. |
CrossWeigh: Training Named Entity Tagger from Imperfect Annotations (D19-1)
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| Challenge: | Named entity recognition (NER) models can identify labels in 5.38% of test sentences . a framework to handle label mistakes during NER model training is proposed . |
| Approach: | They propose a framework to manually correct label mistakes in named entity recognition (NER) they aim to improve the accuracy of models by re-evaluating popular models on corrected test sets . |
| Outcome: | The proposed framework can detect label mistakes in 5.38% of test sentences . the proposed framework improves on three datasets with a high-performance model . |
PAIR: Planning and Iterative Refinement in Pre-trained Transformers for Long Text Generation (2020.emnlp-main)
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| Challenge: | We present a content-controlled text generation framework for pre-trained Transformers . large pre-train models are the cornerstone of many state-of-the-art models in natural language understanding and generation tasks. |
| Approach: | They propose a content-controlled text generation framework that adds content planning to large pre-trained Transformers without modifying model architecture. |
| Outcome: | The proposed framework improves the quality of the outputs on three domains. |
MMLU-ProX: A Multilingual Benchmark for Advanced Large Language Model Evaluation (2025.emnlp-main)
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Weihao Xuan, Rui Yang, Heli Qi, Qingcheng Zeng, Yunze Xiao, Aosong Feng, Dairui Liu, Yun Xing, Junjue Wang, Fan Gao, Jinghui Lu, Yuang Jiang, Huitao Li, Xin Li, Kunyu Yu, Ruihai Dong, Shangding Gu, Yuekang Li, Xiaofei Xie, Felix Juefei-Xu, Foutse Khomh, Osamu Yoshie, Qingyu Chen, Douglas Teodoro, Nan Liu, Randy Goebel, Lei Ma, Edison Marrese-Taylor, Shijian Lu, Yusuke Iwasawa, Yutaka Matsuo, Irene Li
| Challenge: | Existing large language model evaluation benchmarks focus on English, while current multilingual tasks lack parallel questions that specifically assess cross-lingual reasoning abilities. |
| Approach: | They propose a comprehensive benchmark covering 29 languages, built on an English benchmark. |
| Outcome: | The MMLU-ProX is a comprehensive benchmark covering 29 languages, built on an English benchmark. |
Combining Spans into Entities: A Neural Two-Stage Approach for Recognizing Discontiguous Entities (D19-1)
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| Challenge: | Named entity recognition (NER) aims at identifying shallow semantic elements in text. |
| Approach: | They propose a neural two-stage approach to recognizing discontiguous and overlapping entities by decomposing the problem into two subtasks. |
| Outcome: | The proposed model achieves state-of-the-art in a standard dataset even without external features. |
The Music Maestro or The Musically Challenged, A Massive Music Evaluation Benchmark for Large Language Models (2024.findings-acl)
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| Challenge: | Existing benchmarks to evaluate LLMs' capabilities are inadequate for assessing their musical capabilities. |
| Approach: | They propose to use a large-scale music benchmark specifically designed to evaluate the music-related capabilities of large language models (LLMs). |
| Outcome: | The proposed framework evaluates 16 large language models in the domain of music. |
On-the-Fly Attention Modulation for Neural Generation (2021.findings-acl)
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Yue Dong, Chandra Bhagavatula, Ximing Lu, Jena D. Hwang, Antoine Bosselut, Jackie Chi Kit Cheung, Yejin Choi
| Challenge: | Degeneration of neural text is associated with insufficient learning of task-specific characteristics by the attention mechanism. |
| Approach: | They propose to use attention modulation to inject priors into inference to improve fluency, creativity, and commonsense reasoning in neural text generation models. |
| Outcome: | The proposed method improves fluency, creativity, and commonsense reasoning, and significantly reduces sentence-level repetition. |
NovelHopQA: Diagnosing Multi-Hop Reasoning Failures in Long Narrative Contexts (2025.emnlp-main)
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| Challenge: | Current large language models struggle to answer questions that span tens of thousands of tokens. |
| Approach: | They evaluate 1–4 hop QA over 64k–128k-token excerpts from 83 novels . they find consistent accuracy drops with increased hops and context length . |
| Outcome: | The novelhopqa benchmark evaluates 1–4 hop QA over 64k–128k-token excerpts from 83 public-domain novels. |
ProxyQA: An Alternative Framework for Evaluating Long-Form Text Generation with Large Language Models (2024.acl-long)
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Haochen Tan, Zhijiang Guo, Zhan Shi, Lu Xu, Zhili Liu, Yunlong Feng, Xiaoguang Li, Yasheng Wang, Lifeng Shang, Qun Liu, Linqi Song
| Challenge: | Existing evaluation methods for large language models are labor-intensive and lack efficiency. |
| Approach: | They propose a framework dedicated to assessing long-text generation that includes in-depth human-curated meta-questions spanning various domains . they use a set of proxy-quests with pre-annotated answers to assess the content's quality by incorporating the generated texts as contextual background. |
| Outcome: | The proposed framework assesses the quality of long-text content by matching it with references through human evaluation or automated metrics. |
Multilingual Language Model Pretraining using Machine-translated Data (2025.emnlp-main)
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Jiayi Wang, Yao Lu, Maurice Weber, Max Ryabinin, David Ifeoluwa Adelani, Yihong Chen, Raphael Tang, Pontus Stenetorp
| Challenge: | Existing methods for collecting and filtering multilingual web data lead to most languages lagging behind English performance due to the Internet's English-centric nature. |
| Approach: | They propose to translate a high-quality English web corpus into nine languages and pretrain a 1.3B-parameter model on it. |
| Outcome: | The proposed model matches or outperforms multilingual LLMs of similar size across Non-English understanding and reasoning tasks despite being trained on an order of magnitude less data. |
Expanding the Boundaries of Vision Prior Knowledge in Multi-modal Large Language Models (2026.eacl-long)
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Qiao Liang, Yanjiang Liu, Weixiang Zhou, Ben He, Yaojie Lu, Hongyu Lin, Jia Zheng, Xianpei Han, Le Sun, Yingfei Sun
| Challenge: | Existing research treats MLLMs as unified systems optimized through end-to-end training, but the impact of vision encoder’s prior knowledge is seldom investigated. |
| Approach: | They propose a metric to quantify the effect of prior knowledge on MLLM performance by integrating prior knowledge at the vision encoder level into a training framework. |
| Outcome: | The proposed training framework incorporates prior knowledge at the vision encoder level, and significantly boosts visual understanding capabilities of MLLMs. |
Learning with Structured Representations for Negation Scope Extraction (P18-2)
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| Challenge: | Existing approaches to negation scope detection have been criticized for capturing information related to negations, long-distance dependencies and structural information. |
| Approach: | They propose to use conditional random fields, semi-Markov CRF and latent-variable CRF models to capture useful information such as long-distance dependencies and some latent structural information. |
| Outcome: | The proposed approaches can capture useful information such as features related to negation cue, long-distance dependencies and some latent structural information. |
Large Language Models are Superpositions of All Characters: Attaining Arbitrary Role-play via Self-Alignment (2024.acl-long)
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| Challenge: | Existing work cheaply emulates LLMs, allowing users to create profiles for their preferred characters. |
| Approach: | They propose a self-alignment method that encourages an instruction-following LLM to simulate role-play dialogues as a variant of reading comprehension. |
| Outcome: | The proposed model outperforms open-source role-play benchmarks and the roleplay subset of MT-Bench in multiple parameters. |
Collaborative Generative AI: Integrating GPT-k for Efficient Editing in Text-to-Image Generation (2023.emnlp-main)
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| Challenge: | Experimental results show that GPT-k models focus more on inserting modifiers than predicting spontaneous changes in the primary subject matter. |
| Approach: | They compare the common edits made by humans and GPT-k models to examine their performance in prompting T2I. |
| Outcome: | The proposed models improve the prompt editing process by 20-30%, the authors show . they show that humans tend to replace words and phrases with modifiers . |
Anaphor Assisted Document-Level Relation Extraction (2023.emnlp-main)
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| Challenge: | Existing methods for document-level relation extraction are incomplete and lack anaphor for identifying relations between entities. |
| Approach: | They propose an Anaphor-Assisted (AA) framework for document-level relation extraction . they use a document or sentences as intermediate nodes to model cross-sentence entity interactions . |
| Outcome: | The proposed framework achieves state-of-the-art on the widely-used datasets. |
UniEX: An Effective and Efficient Framework for Unified Information Extraction via a Span-extractive Perspective (2023.acl-long)
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| Challenge: | Existing approaches for information extraction (IE) are limited by the number of subtasks and the isolation of the subtask. |
| Approach: | They propose a new paradigm for universal information extraction that is compatible with any schema format and applicable to a list of IE tasks. |
| Outcome: | The proposed framework outperforms generative universal IE models on 14 benchmarks with the supervised setting and the state-of-the-art performance in low-resource scenarios. |
x1: Learning to Think Adaptively Across Languages and Cultures (2026.findings-acl)
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Yangfan Ye, Xiaocheng Feng, Xiachong Feng, Yichong Huang, Zekun Yuan, Lei Huang, Weitao Ma, Qichen Hong, Yunfei Lu, Dandan Tu, Bing Qin
| Challenge: | Existing large language models (LLMs) ignore this diversity by reasoning in a single dominant language. |
| Approach: | They propose a family of reasoning models that can adaptively reason in an advantageous language on a per-instance basis. |
| Outcome: | The proposed model can reason in a single dominant language on a per-instance basis. |
EnAnchored-X2X: English-Anchored Optimization for Many-to-Many Translation (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) have demonstrated strong machine translation capabilities for English-centric language pairs but underperform in direct non-English (x2x) translation. |
| Approach: | They propose a synthetic data generation framework that leverages models’ established English-to-x (en2x) capabilities by extending English parallel corpora into omnidirectional datasets and developing an English-referenced quality evaluation proxy. |
| Outcome: | The proposed framework achieves significant improvement across 72 x2x directions while generalizing to enhance en2x performance. |
Tell2Design: A Dataset for Language-Guided Floor Plan Generation (2023.acl-long)
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| Challenge: | Recent studies have demonstrated impressive results in generating high-fidelity artistic images. |
| Approach: | They propose a Sequence-to-Sequence model that can serve as a strong baseline for future research. |
| Outcome: | The proposed model can be used as a baseline for future research and human evaluations are conducted on the generated samples and provided an analysis of human performance. |
Nonparametric Decoding for Generative Retrieval (2023.findings-acl)
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Hyunji Lee, JaeYoung Kim, Hoyeon Chang, Hanseok Oh, Sohee Yang, Vladimir Karpukhin, Yi Lu, Minjoon Seo
| Challenge: | Existing text retrieval models depend on the information encoded in its parameters without external memory, its information capacity is limited and fixed. |
| Approach: | They propose a nonparametric decoding approach which uses external memory instead of vanilla vocab embeddings as decoder voka embedds. |
| Outcome: | The proposed model can utilize parametric and nonparametric space. |
Improving Autoformalization Using Direct Dependency Retrieval (2026.acl-long)
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| Challenge: | Existing methods for hallucinate formal dependencies lack scalability and precision to leverage ever-growing public datasets. |
| Approach: | They propose a retrieval-augmented framework based on Direct Dependency Retrieval to generate formal dependencies from natural-language mathematical descriptions and verify their existence via an efficient Suffix Array Check (SAC). |
| Outcome: | The proposed framework outperforms state-of-the-art methods in retrieval precision and recall and can be used to validate formal representations in a public dataset. |
LongSafety: Evaluating Long-Context Safety of Large Language Models (2025.acl-long)
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Yida Lu, Jiale Cheng, Zhexin Zhang, Shiyao Cui, Cunxiang Wang, Xiaotao Gu, Yuxiao Dong, Jie Tang, Hongning Wang, Minlie Huang
| Challenge: | Large Language Models (LLMs) have demonstrated remarkable capabilities in understanding and generating long sequences. |
| Approach: | They propose a benchmark to evaluate LLM safety in open-ended long-context tasks . they find that relevant context and extended input sequences can exacerbate safety risks . |
| Outcome: | The proposed benchmark identifies significant safety vulnerabilities in 16 LLMs . strong safety performance in short-context scenarios does not correlate with safety in long-contact tasks . |
CogDual: Enhancing Dual Cognition of LLMs via Reinforcement Learning with Implicit Rule-Based Rewards (2025.emnlp-main)
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| Challenge: | Existing approaches to role-playing language models rely on prompt engineering or supervised fine-tuning to emulate character behaviors but neglect the underlying cognitive mechanisms driving these behaviors. |
| Approach: | They propose a novel RPLA adopting a cognize-then-respond reasoning paradigm that leverages dual cognition for more contextually grounded and psychologically coherent responses. |
| Outcome: | The proposed RPLA outperforms baselines and generalizes effectively across diverse role-playing tasks. |
DemoSG: Demonstration-enhanced Schema-guided Generation for Low-resource Event Extraction (2023.findings-emnlp)
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| Challenge: | Current Event Extraction methods focus on high-resource scenarios, which requires large amount of annotated data. |
| Approach: | They propose a demonstration-based learning paradigm for EE to fully use annotated data . they propose EE as a natural language generation task guided by schema-based prompts . |
| Outcome: | The proposed model outperforms current methods in low-resource scenarios. |
Token-level Proximal Policy Optimization for Query Generation (2025.emnlp-main)
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Yichen Ouyang, Lu Wang, Fangkai Yang, Pu Zhao, Chenghua Huang, Jianfeng Liu, Bochen Pang, Yaming Yang, Yuefeng Zhan, Hao Sun, Qingwei Lin, Saravan Rajmohan, Weiwei Deng, Dongmei Zhang, Feng Sun
| Challenge: | Large Language Models (LLMs) have improved search engines and recommendation systems through their text understanding capabilities. |
| Approach: | They propose a token-level proximal policy optimization approach to empower LLMs to perform better in query generation through fine-tuning. |
| Outcome: | The proposed approach outperforms existing LLMs on an open-source and industrial dataset. |
RATIONALYST: Pre-training Process-Supervision for Improving Reasoning (2025.acl-long)
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Dongwei Jiang, Guoxuan Wang, Yining Lu, Andrew Wang, Jingyu Zhang, Chuyu Liu, Benjamin Van Durme, Daniel Khashabi
| Challenge: | RATIONALYST is a model for process-supervision of reasoning based on pretraining on rationale annotations extracted from unlabeled data. |
| Approach: | They propose a model for process-supervision of reasoning based on pre-training on rationale annotations extracted from unlabeled data. |
| Outcome: | RATIONALYST improves reasoning accuracy by 3.9% on representative reasoning benchmarks. |
AutoFigure-Edit: Generating Editable Scientific Illustrations via Reference-Guided Styling (2026.acl-demo)
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Zhen Lin, Qiujie Xie, Minjun Zhu, Shichen Li, QiYao Sun, Enhao Gu, Yiran Ding, Ke Sun, Fang Guo, Panzhong Lu, Zhiyuan Ning, Yixuan Weng, Yue Zhang
| Challenge: | Existing automated systems for scientific illustrations are limited in editability, stylistic controllability, and efficiency. |
| Approach: | They propose an end-to-end system that generates fully editable scientific illustrations from long-form scientific text while enabling flexible style adaptation through user-provided reference images. |
| Outcome: | The proposed system generates fully editable scientific illustrations from long-form scientific texts while enabling flexible style adaptation through user-provided reference images. |
Efficient Inference for Large Language Models –Algorithm, Model, and System (2025.emnlp-tutorials)
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| Challenge: | Inference of LLMs incurs high computational costs, memory access overhead, and memory usage, leading to inefficiencies in terms of latency, throughput, power consumption, and storage. |
| Approach: | This tutorial introduces the basics of efficient inference for LLMs and explains how to diagnose efficiency bottlenecks for a given workload on specific hardware. |
| Outcome: | The tutorial introduces the basic concepts of modern LLMs, software and hardware. |
Model Editing Harms General Abilities of Large Language Models: Regularization to the Rescue (2024.emnlp-main)
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| Challenge: | Existing methods that edit large language models with updated knowledge can cause side effects on the general abilities of LLMs such as reasoning, natural language inference, and question answering. |
| Approach: | They propose to regularize the edit update weights by imposing constraints on their complexity based on the RElative Change in weighT. |
| Outcome: | The proposed method can significantly mitigate the side effects while maintaining over 94% editing performance. |
Structured Moral Reasoning in Language Models: A Value-Grounded Evaluation Framework (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) are increasingly deployed in domains requiring moral understanding, yet their reasoning often remains shallow and misaligned with human reasoning. |
| Approach: | They propose a value-grounded framework for evaluating and distilling structured moral reasoning in large language models. |
| Outcome: | The proposed framework evaluates 12 open-source models across four moral datasets. |
Towards Generalizable Neuro-Symbolic Systems for Commonsense Question Answering (D19-60)
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| Challenge: | Recent approaches on non-extractive commonsense QA show increased performance . attention-based injection seems to be preferable for knowledge integration . |
| Approach: | They propose to use attention-based injection to integrate knowledge into commonsense QA models. |
| Outcome: | The proposed methods show that attention-based injection is preferable for knowledge integration, and that the degree of domain overlap plays a crucial role in determining model success. |
Evolving Agentic Workflow Driven by Human-Agent Collaboration (2026.findings-acl)
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Yuxin Liu, Jinxuan Zhang, Yuezhang Peng, Hefeng Zhou, Xiangfeng Wang, Jiong Lou, Chentao Wu, Jie LI, Jingjing Qu, Chaochao Lu
| Challenge: | Existing approaches to generate agentic workflows using large language models are limited by high manual design costs, inefficient agentic search, and poor dynamic adaptability to new tasks and human preferences. |
| Approach: | They propose an evolutionary framework for generating agentic workflows through human-agent collaboration using evolutionary algorithms that mutate and cross over their structures, prompts, and LLM backbones. |
| Outcome: | The proposed framework surpasses other automated baselines by 27.34% while achieving comparable performance to o1-preview at only one-fourth of the cost. |
PromptNER: Prompt Locating and Typing for Named Entity Recognition (2023.acl-long)
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Yongliang Shen, Zeqi Tan, Shuhui Wu, Wenqi Zhang, Rongsheng Zhang, Yadong Xi, Weiming Lu, Yueting Zhuang
| Challenge: | Existing methods for prompt learning require a multi-round prompting manner and require elaborate templates. |
| Approach: | They propose to unify entity locating and entity typing into prompt learning by enumerating spans to predict their entity types or constructing type-specific prompts to locate entities. |
| Outcome: | The proposed model outperforms the state-of-the-art model in a few-shot setting . it uses a template filled with multiple prompts and a bipartite graph matching mechanism . |
Mitigating Data Scarcity in Semantic Parsing across Languages with the Multilingual Semantic Layer and its Dataset (2024.findings-acl)
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Abelardo Carlos Martinez Lorenzo, Pere-Lluís Huguet Cabot, Karim Ghonim, Lu Xu, Hee-Soo Choi, Alberte Fernández-Castro, Roberto Navigli
| Challenge: | Large Language Models (LLMs) have advanced significantly in understanding human text, but semantic representations remain crucial for various applications. |
| Approach: | They introduce a multilingual semantic layer which decouples from disambiguation and external inventories and simplifies the task. |
| Outcome: | The proposed model reduces performance gap between languages and annotators by enabling them to understand semantic relations between concepts in any language. |
Intervention-Based Alignment of Code Search with Execution Feedback (2023.findings-emnlp)
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| Challenge: | Existing code search training datasets approximate text-code co-occurrences as positive execution feedback, but this approximation may misalign models’ retrieval decisions from ground-truth correctness. |
| Approach: | They propose a code intervention-based reinforcement learning approach that perturbs training code to result in misalignment, then tests models’ decisions and corrects them with the execution feedback by reinforcement learning. |
| Outcome: | The proposed method induces the execution feedback from perturbation, without actual execution, and then tests models’ decisions and corrects them with the execution input by reinforcement learning. |
OkraLong: A Flexible Retrieval-Augmented Framework for Long-Text Question Answering (2025.findings-emnlp)
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| Challenge: | a new framework for large language models addresses long-text questions . context compression and dynamic retrieval loops sacrifice critical details or incur iterative costs . |
| Approach: | a new framework is proposed to optimize the entire processing workflow . it uses synergistic components to analyzer, organizer and executor to optimize workflow a . |
| Outcome: | OkraLong improves answer accuracy by 5.7%-41.2% and saves 1.3x-4.7x . |
CROSSAGENTIE: Cross-Type and Cross-Task Multi-Agent LLM Collaboration for Zero-Shot Information Extraction (2025.findings-acl)
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| Challenge: | Large language models struggle with producing structured output while maintaining accuracy in zero-shot information extraction (IE) |
| Approach: | They propose a multi-agent framework that enhances zero-shot IE through multi-task collaboration. |
| Outcome: | CROSSAGENTIE outperforms state-of-the-art models in structured prediction . the framework significantly reduces inference cost while preserving accuracy . |
Rethinking the Reversal Curse of LLMs: a Prescription from Human Knowledge Reversal (2024.emnlp-main)
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Zhicong Lu, Li Jin, Peiguang Li, Yu Tian, Linhao Zhang, Sirui Wang, Guangluan Xu, Changyuan Tian, Xunliang Cai
| Challenge: | Existing methods for large language models (LLMs) are limited by their aggressive sample permutation and lack a detailed understanding of the underlying reasons for the reversal curse. |
| Approach: | They propose a method which enhances bidirectional entity correlation modeling and pairwise relationship reasoning to overcome the reversal curse. |
| Outcome: | The proposed method overcomes the reversal curse by augmenting the samples with entity order-reversals and semantically preserved question-answer pairs. |
Re-examining the Role of Schema Linking in Text-to-SQL (2020.emnlp-main)
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| Challenge: | Existing text-to-SQL models treat schema linking as a minor component . Existing solutions treat schema as merely a string component based on string matching . |
| Approach: | They build a schema linking corpus based on a Spider text-to-SQL dataset . they find schema linking is the crux for the current text- to-Sql task . |
| Outcome: | The proposed model performs well on the Spider text-to-SQL dataset despite its simplicity. |
A Simple yet Effective Learnable Positional Encoding Method for Improving Document Transformer Model (2022.findings-aacl)
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| Challenge: | Existing document Transformers lack a robust positional encoding mechanism to indicate and embed sequential order information in documents. |
| Approach: | They propose a positional encoding method that can be pre-trained on document datasets to improve document understanding. |
| Outcome: | The proposed method outperforms baselines on document understanding tasks in form, receipt, and invoice domains and is robust and stable on noisy data with incorrect order information. |
Unsupervised Bitext Mining and Translation via Self-Trained Contextual Embeddings (2020.tacl-1)
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| Challenge: | Existing methods to extract parallel sentences from unaligned text yield surprisingly good results. |
| Approach: | They propose an unsupervised method to create pseudo-parallel corpora for machine translation (MT) from unaligned text using multilingual BERT to create source and target sentence embeddings for nearest-neighbor search and adapt the model via self-training. |
| Outcome: | The proposed method outperforms existing methods and outperformed previous state-of-the-art methods by boosting translation performance by up to 3.5 BLEU on the WMT’14 French-English and WMT'16 German-English tasks. |
Verifiable LLM-Generated Text Detection via Projected Semantic-Structural Distributions (2026.acl-long)
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Ruochong Xiong, Qien Li, Wangwang Lian, Yulong Wan, Hanlin Xue, Zhouxing Tan, Han Yang, Fengyu Lu, Junfei Liu
| Challenge: | Existing methods for detecting LLM-Generated text suffer from distribution misalignment and limited interpretability. |
| Approach: | They propose a statistical framework utilizing supervised subspace learning to extract compact features and construct conditional semantic distributions based on syntactic structures. |
| Outcome: | The proposed framework is superior in cross-domain, cross-model, and adversarial scenarios. |
Protein Large Language Models: A Comprehensive Survey (2025.findings-emnlp)
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Yijia Xiao, Wanjia Zhao, Junkai Zhang, Yiqiao Jin, Han Zhang, Zhicheng Ren, Renliang Sun, Haixin Wang, Guancheng Wan, Pan Lu, Xiao Luo, Yu Zhang, James Zou, Yizhou Sun, Wei Wang
| Challenge: | Existing studies focus on specific aspects or applications, but this study provides a comprehensive overview of Protein-specific large language models. |
| Approach: | This paper proposes a structured taxonomy of state-of-the-art ProteinLLMs . they analyze how they leverage large-scale protein sequence data for improved accuracy . |
| Outcome: | The proposed model covers their architectures, training datasets, evaluation metrics, and diverse applications. |
Look-up and Adapt: A One-shot Semantic Parser (D19-1)
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| Challenge: | Current conversational agents such as Siri, Alexa or Google Assistant do not cater to the specific phrasing of a user or the specific action. |
| Approach: | They propose a semantic parser that generalizes to out-of-domain examples by adapting the logical forms of seen utterances to fit an unseen utterant. |
| Outcome: | The proposed parser improves on one-shot parsing by 68.8% compared to baselines . it adapts the logical forms of seen utterances to fit the unseen utterant . |
Rephrasing Invokes Better Generations for Large Language Models (2024.naacl-srw)
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| Challenge: | Existing methods for prompt tuning and input pre-processing are under-studied . e.g., ReLLM replaces low-frequency words with their high-frequency counterparts . |
| Approach: | They propose a method that automatically paraphrases input content for better output generation. |
| Outcome: | The proposed method is user-friendly and requires no additional training. |
Merging Generated and Retrieved Knowledge for Open-Domain QA (2023.emnlp-main)
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| Challenge: | Open-domain question answering systems often have retrieval modules but retrieving passages from external knowledge sources is known to suffer from insufficient knowledge coverage. |
| Approach: | They propose a Compatibility-Oriented knowledge Merging framework to leverage both sources of information by matching LLM-generated passages with retrieved counterparts into compatible pairs. |
| Outcome: | The proposed framework outperforms baselines on three out of four tested open-domain QA benchmarks. |
CURA: Clinical Uncertainty Risk Alignment for Language Model–Based Risk Prediction (2026.acl-long)
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| Challenge: | Clinical language models (LMs) are increasingly applied to support clinical risk prediction from free-text notes, yet their uncertainty estimates are poorly calibrated and clinically unreliable. |
| Approach: | They propose a framework that aligns clinical LM-based risk estimates and uncertainty with individual error likelihoods and cohort-level ambiguities. |
| Outcome: | The proposed framework improves accuracy on clinical risk prediction tasks without compromising discrimination. |
On the Representation Geometry of LoRA Model Merging (2026.findings-acl)
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| Challenge: | Existing methods for low-rank Adaptation (LoRA) fine-tuning focus on globally shared structure . combining SVD with CUR improves performance of LoRA model merging . |
| Approach: | They propose a training-free method that combines SVD and CUR decomposition to improve LoRA merging performance. |
| Outcome: | The proposed procedure improves on vision and language benchmarks. |
Zipage: Maintain High Request Concurrency for LLM Reasoning through Compressed PagedAttention (2026.findings-acl)
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Mengqi Liao, Lu Wang, Chaoyun Zhang, Bo Qiao, Si Qin, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang, Huaiyu Wan
| Challenge: | Existing methods to evict KV cache during inference phase are impractical for industrial-grade applications. |
| Approach: | They propose a method that combines token-wise KV cache eviction with PagedAttention and propose 'zipage' it achieves 95% of the performance of Full KV inference engines while delivering over 2.1 speedup . |
| Outcome: | The proposed method achieves 95% of the performance of Full KV inference engines while delivering over 2.1 speedup on large-scale mathematical reasoning tasks. |
TurboRAG: Accelerating Retrieval-Augmented Generation with Precomputed KV Caches for Chunked Text (2025.emnlp-main)
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| Challenge: | Current RAG systems concatenate and process numerous retrieved document chunks for prefill . this leads to significant latency in time-to-first-token (TTFT) Experimental results demonstrate that TurboRAG reduces TTFT by up to 9.4x compared to the conventional RAG system. |
| Approach: | They propose a hybrid offline-online paradigm that precomputes chunk-level key-value caches and stitches them together at inference time using independent–attention and reorderedRoPE techniques. |
| Outcome: | Experimental results show that TurboRAG reduces TTFT by 9.4x compared to the conventional RAG systems . long concatenated contexts consume disproportionate GPU memory, limiting throughput . |
Language Model Adaption for Reinforcement Learning with Natural Language Action Space (2024.acl-long)
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| Challenge: | Previous research has focused on reducing the size of the natural language action space due to the combinatorial nature of the language. |
| Approach: | They propose mutual-information regularized policy optimization to reduce the action space by dynamically adjusting the prior provided by the pretrained model. |
| Outcome: | The proposed method improves monotonically on the mutual-information regularized RL objective. |
Knowing When to Quit: Diagnosing and Training LLMs to Abort Futile Reasoning (2026.findings-acl)
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| Challenge: | Large language models generate costly yet semantically void reasoning on beyond-capability tasks . the dominant failure mode is specious reasoning, superficially valid outputs with subtle hallucinations . |
| Approach: | They propose a capability-aligned reinforcement learning approach that aligns model behavior with capability boundaries. |
| Outcome: | The proposed model reduces futile reasoning while maintaining performance across tasks. |
Controlled Low-Rank Adaptation with Subspace Regularization for Continued Training on Large Language Models (2025.acl-long)
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| Challenge: | Existing approaches to mitigate catastrophic forgetting can be broadly categorized into data-based, architecture-based and learning-based methods. |
| Approach: | They propose a subspace regularization method on LoRA structure that imposes constraints on direction of updating matrix’s null space. |
| Outcome: | The proposed method reduces scale of output change while introducing minimal constraint on model capacity. |
CCHall: A Novel Benchmark for Joint Cross-Lingual and Cross-Modal Hallucinations Detection in Large Language Models (2025.acl-long)
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| Challenge: | Existing studies on hallucinations in large language models are limited to a single scenario, either cross-lingual or cross-modal. |
| Approach: | They propose a joint Cross-lingual and Cross-modal hallucinations benchmark to fill this gap . they incorporate cross-lingual, cross-modal scenarios to assess hallucinic capabilities . |
| Outcome: | The proposed benchmark incorporates both cross-lingual and cross-modal hallucination scenarios to assess the cross-linguistic and crossmodal capabilities of LLMs. |
WASA: WAtermark-based Source Attribution for Large Language Model-Generated Data (2025.findings-acl)
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Xinyang Lu, Jingtan Wang, Zitong Zhao, Zhongxiang Dai, Chuan-Sheng Foo, See-Kiong Ng, Bryan Kian Hsiang Low
| Challenge: | Large language models (LLMs) have impressive performance but intellectual property concerns are looming . a framework that can be used to perform source attribution for LLMs can be developed. |
| Approach: | They propose a framework that enables an LLM to generate synthetic texts with embedded watermarks that contain information about their source. |
| Outcome: | The proposed framework achieves source attribution accuracy and robustness against adversaries. |
How to Allocate, How to Learn? Dynamic Rollout Allocation and Advantage Modulation for Policy Optimization (2026.findings-acl)
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Yangyi Fang, Jiaye Lin, Xiaoliang Fu, Cong Qin, Haolin Shi, Chaowen Hu, Lu Pan, Ke Zeng, Xunliang Cai
| Challenge: | Existing methods for reinforcement learning with verifiable rewards are limited by the complexity of the problem and the complexity. |
| Approach: | They propose a theoretically-grounded dual-pronged optimization framework for reinforcement learning with verifiable rewards that compensates for gradient attenuation of high-confidence correct actions while utilizing entropy changes as computable indicators to stabilize excessive update magnitudes. |
| Outcome: | The proposed framework compensates for gradient attenuation of high-confidence correct actions while utilizing entropy changes as computable indicators to stabilize excessive update magnitudes. |
HyperFM: Fact-Centric Multimodal Fusion for Link Prediction over Hyper-Relational Knowledge Graphs (2025.acl-long)
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| Challenge: | Existing link prediction techniques focus on learning the complex relationships between entities and relations while ignoring the multimodal information. |
| Approach: | They propose a fact-centric fusion technique that captures complex interactions between different data modalities while accommodating the hyper-relational structure of the KG in a facts-centric manner. |
| Outcome: | The proposed technique improves on two real-world KG datasets by 6.0-6.8% over baselines. |
Lightweight Contenders: Navigating Semi-Supervised Text Mining through Peer Collaboration and Self Transcendence (2025.findings-naacl)
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| Challenge: | Existing frameworks for semi-supervised text mining with lightweight models are limited by label data scarcity. |
| Approach: | They propose a framework for semi-supervised text mining with lightweight models . it incorporates online distillation to train lightweight student models by imitating the Teacher model . |
| Outcome: | The proposed framework exhibits notable performance enhancements over existing frameworks. |
Causal Distillation for Language Models (2022.naacl-main)
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Zhengxuan Wu, Atticus Geiger, Joshua Rozner, Elisa Kreiss, Hanson Lu, Thomas Icard, Christopher Potts, Noah Goodman
| Challenge: | Distillation efforts have led to language models that are more compact and efficient without serious drops in performance. |
| Approach: | They propose to augment distillation with a third objective that encourages the student model to imitate the causal dynamics of the teacher through a distillation interchange intervention training objective (DIITO). |
| Outcome: | The proposed method lowers perplexity on the WikiText-103M corpus and improves on the GLUE benchmark, SQuAD, and CoNLL-2003. |
Beyond the Context Window: Scaling Agentic RL via End-to-end Optimized Context Compression (2026.acl-long)
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| Challenge: | Existing reinforcement learning pipelines suffer from degraded instruction following, excessive rollout costs, and strict context limits. |
| Approach: | They propose a reinforcement learning (RL) fine-tuning of large language model (LLM) agents for long-horizon multi-turn tool use where context length quickly becomes a bottleneck. |
| Outcome: | The proposed framework improves the success rate while maintaining the same or even lower working context length compared to baselines. |
Goal-Oriented End-to-End Conversational Models with Profile Features in a Real-World Setting (N19-2)
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| Challenge: | a recent study has focused on how algorithmic improvements help model performance on fabricated datasets. |
| Approach: | They propose two approaches to train conversational neural models for goal-oriented conversational systems . they train models on historical chat transcripts and test on live contacts . |
| Outcome: | The proposed model is able to generate top-four responses on live contacts . the model is also able for customer profile features to assess their impact on performance . |
Analyzing Occupational Distribution Representation in Japanese Language Models (2024.lrec-main)
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| Challenge: | Recent advances in large language models have enabled users to generate fluent and seemingly convincing text, but they have uneven performance in different languages, which is associated with undesirable societal biases toward marginalized populations. |
| Approach: | They develop three Japanese language prompts to probe LLMs’ understanding of Japanese names and their association between gender and occupations. |
| Outcome: | The proposed models can associate Japanese names with correct gendered occupations when using constrained decoding, but with sampling or greedy decoding they prefer a small set of stereotypically genderes. |
Open Grounded Planning: Challenges and Benchmark Construction (2024.acl-long)
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| Challenge: | Existing work on LLM-based planning uses language generation to produce free-style plans . however, these plans are not grounded in an executable set of actions . |
| Approach: | They propose a new task for open grounded planning that asks the model to generate an executable plan based on a variable action set. |
| Outcome: | The proposed task is open grounded planning, which is based on a set of variables. |
Routing to the Expert: Efficient Reward-guided Ensemble of Large Language Models (2024.naacl-long)
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| Challenge: | Existing ensemble methods for Large Language Models focus on reward model ranking of outputs, leading to significant computation overhead. |
| Approach: | They propose a reward-guided routing method distilling rewards on training queries to train a routing function. |
| Outcome: | The proposed method outperforms the best single model and ranks first on 44% of tasks. |
MuVER: Improving First-Stage Entity Retrieval with Multi-View Entity Representations (2021.emnlp-main)
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| Challenge: | Recent advances in entity retrieval ignore the property that meanings of entity mentions diverge in different contexts and are related to various portions of descriptions. |
| Approach: | They propose a novel approach that constructs multi-view representations for entity descriptions and approximates the optimal view for mentions via a heuristic searching method. |
| Outcome: | The proposed approach achieves state-of-the-art performance on ZESHEL and improves quality of candidates on three standard Entity Linking datasets. |
MemTR: Enhancing Tool-Calling Reliability via Uncertainty-Triggered FFN-Space Retracing (2026.findings-acl)
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| Challenge: | Existing tool-calling methods rely on costly tool-use training data or only constrain syntax, leaving tool selection and argument value errors largely unsolved. |
| Approach: | They propose a method that decodes tool evidence from the tool library and mixes it into the output at the uncertain layer. |
| Outcome: | The proposed method reduces tool calling failures by 2%–9% with only 1%–2% runtime overhead. |
MIDGARD: Self-Consistency Using Minimum Description Length for Structured Commonsense Reasoning (2024.acl-long)
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| Challenge: | Existing approaches to generate a reasoning graph from natural language input suffer from error propagation due to autoregressive nature and single-pass-based decoding. |
| Approach: | They propose a method that uses minimum description length to identify consistent properties among different graph samples generated by large language models. |
| Outcome: | The proposed method outperforms previous approaches for generating reasoning graphs from natural language input using large language models. |
Enabling Multimodal Generation on CLIP via Vision-Language Knowledge Distillation (2022.findings-acl)
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| Challenge: | Recent large-scale vision-language pre-training models are powerful in multimodal classification and retrieval tasks. |
| Approach: | They propose to augment a vision-language pre-training model with a textual pre-trained language model . the model achieves 44.5% zero-shot accuracy on multimodal generation tasks . |
| Outcome: | The proposed model achieves 44.5% zero-shot accuracy on open-ended visual question answering and image captioning tasks. |
Phi: Preference Hijacking in Multi-modal Large Language Models at Inference Time (2025.emnlp-main)
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| Challenge: | Recent advances in Multimodal Large Language Models have raised serious safety concerns. |
| Approach: | They propose a method for manipulating the output preference of MLLMs using a preference hijacked image. |
| Outcome: | The proposed method works at inference time and requires no model modifications. |
LLMs Deceive Unintentionally: Emergent Misalignment in Dishonesty from Misaligned Samples to Biased Human-AI Interactions (2026.findings-acl)
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| Challenge: | Existing studies have shown that LLMs finetuned on incorrect completions can exhibit harmful behaviors, which is called emergent misalignment. |
| Approach: | They investigate whether LLMs finetuned on incorrect completions can exhibit harmful behaviors . they find that 1% of misalignment data is sufficient to decrease honest behavior . |
| Outcome: | The proposed model can be misaligned on errors within narrow domains to exhibit harmful behaviors . the proposed model is able to exhibit dishonest behavior with only 10% biased user population . |
Optimizing Native Sparse Attention with Latent Attention and Local Global Alternating Strategies (2026.findings-acl)
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| Challenge: | Existing research has proposed a variety of training-free and post-training methods for selecting critical key-value pairs at each generation step. |
| Approach: | They propose to use local (sliding-window) and global (compression/selective) attention across layers to enlarge long-context modeling. |
| Outcome: | Experiments on models from 340M to 1.3B parameters show that the proposed method matches or exceeds full attention and native sparse attention in both common-sense reasoning and long-context understanding tasks. |
Grammar-Based Code Representation: Is It a Worthy Pursuit for LLMs? (2025.findings-acl)
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Qingyuan Liang, Zhao Zhang, Zeyu Sun, Zheng Lin, Qi Luo, Xiao Yueyi, Yizhou Chen, Yuqun Zhang, Haotian Zhang, Lu Zhang, Chenbin Chenbin, Yingfei Xiong
| Challenge: | Existing research demonstrates the effectiveness of grammar-based code representations in small-scale models, showing their ability to reduce syntax errors and enhance performance. |
| Approach: | They develop a series of billion-scale grammar-based code representations that incorporate grammar rules into the code generation process. |
| Outcome: | Experiments on HumanEval and MBPP show that grammar-based representations reduce syntax errors and improve performance even in billion-scale models. |
SEOE: A Scalable and Reliable Semantic Evaluation Framework for Open Domain Event Detection (2025.acl-long)
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| Challenge: | Existing evaluation methods for Open Domain Event Detection (ODED) lack representative representations of the real world, making it difficult to accurately reflect performance of various ODED methods in real-world scenarios. |
| Approach: | They propose a scalable and reliable Semantic-level Evaluation framework for Open domain event detection by constructing a more representative evaluation benchmark and introducing a semantic evaluation metric. |
| Outcome: | The proposed framework first constructs a more representative evaluation benchmark that currently includes 564 event types covering 7 major domains, with a cost-effective supplementary annotation strategy to ensure the benchmark’s representativeness. |
Do Vision-Language Models Have Internal World Models? Towards an Atomic Evaluation (2025.findings-acl)
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Qiyue Gao, Xinyu Pi, Kevin Liu, Junrong Chen, Ruolan Yang, Xinqi Huang, Xinyu Fang, Lu Sun, Gautham Kishore, Bo Ai, Stone Tao, Mengyang Liu, Jiaxi Yang, Chao-Jung Lai, Chuanyang Jin, Jiannan Xiang, Benhao Huang, Zeming Chen, David Danks, Hao Su, Tianmin Shu, Ziqiao Ma, Lianhui Qin, Zhiting Hu
| Challenge: | Recent studies have evaluated and shown limitations in specific capabilities such as visual understanding, but a systematic evaluation of VLMs’ fundamental WM abilities remains absent. |
| Approach: | They propose a framework that assesses perception and prediction to provide an atomic evaluation of VLMs as WMs. |
| Outcome: | The proposed framework assesses perception and prediction abilities on 15 latest VLMs and compares them to human-level models. |
Exploring Methods for Building Dialects-Mandarin Code-Mixing Corpora: A Case Study in Taiwanese Hokkien (2022.findings-emnlp)
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| Challenge: | CM is a challenging task when mixed languages include dialects. |
| Approach: | They propose to construct a Hokkien-Mandarin CM dataset to overcome the limitation . they propose to use a linguistics-based toolkit to train the model for translation tasks . |
| Outcome: | The proposed model achieves good results on CM data translation while maintaining monolingual translation quality. |
Chain-of-Quizzes: Pedagogy-inspired Example Selection in In-Context-Learning (2024.findings-acl)
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| Challenge: | In-context learning (ICL) is a powerful tool for enhancing large language models (LLMs) by mimicking the human learning process. |
| Approach: | They propose a Chain-of-Quizzes framework that uses LLMs to answer a quiz to sift 'good' examples, combine them iteratively with the increasing complexity, and utilize a final exam to gauge the combined example chains. |
| Outcome: | The proposed framework outperforms baseline models on diverse reasoning datasets and shows that it is scalable and can be used in future research. |
Towards Compositional Generalization in Code Search (2022.emnlp-main)
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| Challenge: | Existing code search models that focus on code as an unstructured sequence fail to generalize when the lexical perturbation without changing structures and labels is applied in test codes. |
| Approach: | They propose a compositional generalization model that extracts structural elements and a code template that targets compositional genericization. |
| Outcome: | The proposed model is complementary to flow graphs in GraphCodeBERT, by enhancing structural context around variables. |
Xinference: Making Large Model Serving Easy (2024.emnlp-demo)
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| Challenge: | Open-source large models are rapidly catching up with the closed-source models . however, many current inference tools are not as simple and convenient to use. |
| Approach: | They develop an open-source library to simplify the deployment and management of large models. |
| Outcome: | The proposed library outperforms open-source models and offers high throughput and low latency. |
PPTAgent: Generating and Evaluating Presentations Beyond Text-to-Slides (2025.emnlp-main)
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Hao Zheng, Xinyan Guan, Hao Kong, Wenkai Zhang, Jia Zheng, Weixiang Zhou, Hongyu Lin, Yaojie Lu, Xianpei Han, Le Sun
| Challenge: | Existing methods for generating presentations from documents focus on improving and evaluating content quality in isolation, overlooking visual appeal and structural coherence. |
| Approach: | They propose an edit-based presentation generation system that analyzes and iterates on slides to create new slides. |
| Outcome: | The proposed presentation generation tool outperforms existing methods in three dimensions . it analyzes slides, iterates and generates edit actions based on selected slides . |
Finch: Benchmarking Finance & Accounting across Spreadsheet-Centric Enterprise Workflows (2026.findings-acl)
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Haoyu Dong, Pengkun Zhang, Yan Gao, Xuanyu Dong, Yilin Cheng, Mingzhe Lu, Adina Yakefu, Shuxin Zheng
| Challenge: | FinWorkBench evaluates real-world enterprise-grade finance and accounting workflows . a human evaluation of GPT 5.1 Pro passes only 38.4% of workflows, a study finds . |
| Approach: | They propose a workflow construction process that combines LLM-assisted mining and expert annotation to build 172 composite workflows. |
| Outcome: | The proposed process combines expert annotation with LLM-assisted mining of workflows from authentic enterprise environments. |
StyleRemix: Interpretable Authorship Obfuscation via Distillation and Perturbation of Style Elements (2024.emnlp-main)
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| Challenge: | Authorship obfuscation methods that ignore author-specific stylistic features are often too rigid and lead to degradation of fluency and grammaticality. |
| Approach: | They propose an adaptive obfuscation method that perturbs stylistic elements of text . authors release a large set of 30K high-quality, long-form texts from a diverse set of 14 authors . |
| Outcome: | The proposed method outperforms state-of-the-art methods on an array of domains on automatic and human evaluation. |
Joint Detection and Location of English Puns (N19-1)
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| Challenge: | Existing research on puns has focused on understanding the meanings of words and phrases. |
| Approach: | They propose a model that addresses pun detection and pun location jointly from a sequence labeling perspective. |
| Outcome: | Empirical results show that the proposed model can handle both homographic and heterographic puns. |
Paired by the Teacher: Turning Unpaired Data into High-Fidelity Pairs for Low-Resource Text Generation (2025.emnlp-main)
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| Challenge: | a low-resource natural language generation task requires a large number of examples to generate outputs and outputs. |
| Approach: | They propose a teacher-student pipeline that synthesizes accurate input–output pairs without human labels or parallel data. |
| Outcome: | The proposed pipeline synthesizes accurate input–output pairs without human labels or parallel data. |