Papers by Lin Xu

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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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.

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

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

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

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

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Ting-En Lin, Hua Xu

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Visual Data in NLP

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

FPE2M2: Approaching Lossless and Efficient Quantization with Native Floating Point (2025.findings-acl)

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

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Ke Yi, Jianwei Zhang, Zhiying Xu, Xinlong Yang, Yang Zhou, Minmin Sun, Zengke Liu, Tong Zhang, Junyang Lin, Jingren Zhou

More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction (2020.aacl-main)

Challenge:	Auto-regressive decoding is a memory-bound job, meaning decoding performance is limited by the bandwidth rather than the computational capabilities of the GPU.
Approach:	They propose a framework that supports lossless weight-only quantization inference and validate it on Qwen and LLaMA Models.
Outcome:	The proposed framework achieves the highest efficiency with lossless accuracy on Qwen and LLaMA Models across various modalities.

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Xu Han, Tianyu Gao, Yankai Lin, Hao Peng, Yaoliang Yang, Chaojun Xiao, Zhiyuan Liu, Peng Li, Jie Zhou, Maosong Sun

PreP-OCR: A Complete Pipeline for Document Image Restoration and Enhanced OCR Accuracy (2025.acl-long)

Challenge:	Existing methods for extracting relational facts from text have been successful . but with explosion of Web text, human knowledge is increasing drastically .
Approach:	They propose to improve relation extraction methods to extract relational facts from text . they analyze existing methods and show promising directions towards more powerful RE .
Outcome:	The proposed methods can extract relational facts from text, but they are still lacking in the current field.

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Shuhao Guan, Moule Lin, Cheng Xu, Xinyi Liu, Jinman Zhao, Jiexin Fan, Qi Xu, Derek Greene

MMedAgent: Learning to Use Medical Tools with Multi-modal Agent (2024.findings-emnlp)

Challenge:	Existing pipelines that combine document image restoration with semantic-aware post-OCR correction can improve text extraction from degraded images.
Approach:	They propose a two-stage pipeline that combines document image restoration with semantic-aware post-OCR correction to enhance both visual clarity and textual consistency.
Outcome:	The proposed pipeline reduces character error rates by 63.9-70.3% on 13,831 pages of real historical documents in English, French, and Spanish compared to OCR on raw images.

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Binxu Li, Tiankai Yan, Yuanting Pan, Jie Luo, Ruiyang Ji, Jiayuan Ding, Zhe Xu, Shilong Liu, Haoyu Dong, Zihao Lin, Yixin Wang

MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing (2026.acl-industry)

Challenge:	Multi-modal Large Language Models (MLLMs) exhibit limited generality and often fall short when compared to specialized models.
Approach:	They propose a multi-modal medical agent that picks the most suitable medical tools based on user inputs.
Outcome:	The proposed agent performs better than open-source models and the closed-source model, GPT-4o.

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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

CorefDiffs: Co-referential and Differential Knowledge Flow in Document Grounded Conversations (2022.coling-1)

Challenge:	Document images are characterized by higher resolutions, denser content, and more complex structural layouts.
Approach:	They propose a 1.2B-parameter document parsing vision-language model that decouples layout analysis from local content recognition.
Outcome:	The proposed model surpasses general-purpose and domain-specific models on multiple benchmarks while maintaining significantly lower computational overhead.

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Lin Xu, Qixian Zhou, Jinlan Fu, Min-Yen Kan, See-Kiong Ng

Linguistic Neuron Overlap Patterns to Facilitate Cross-lingual Transfer on Low-resource Languages (2025.emnlp-main)

Challenge:	Document-grounded dialogs need smooth transitions between knowledge selected for generating responses.
Approach:	They propose a multi-document co-referential graph to capture inter- and intra-document relationships . they propose 'Coref-MDG' method to linearize static Coref-mDG into conversational sequence logic.
Outcome:	The proposed method outperforms the state-of-the-art by 9.5%, 7.4% and 8.2% on three public benchmarks.

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Yuemei Xu, Kexin Xu, Jian Zhou, Ling Hu, Lin Gui

Just Like a Human Would, Direct Access to Sarcasm Augmented with Potential Result and Reaction (2023.acl-long)

Challenge:	Recent studies have focused on data-efficient methods, particularly Cross-lingual In-Context Learning (X-ICL)
Approach:	They propose a method to improve cross-lingual in-context learning for low-resource languages by using language-specific neurons.
Outcome:	The proposed method improves cross-lingual performance on low-resource languages by ensuring full activation of language overlap neurons.

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Changrong Min, Ximing Li, Liang Yang, Zhilin Wang, Bo Xu, Hongfei Lin

Sparse Activation Editing for Reliable Instruction Following in Narratives (2025.emnlp-main)

Challenge:	sarcasm is a form of irony conveying mockery and contempt . social media has become increasingly popular for identifying sarcasm .
Approach:	They develop a method to detect sarcasm from social media using augmented potentials.
Outcome:	The proposed method outperforms baselines on benchmark datasets.

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Runcong Zhao, Chengyu Cao, Qinglin Zhu, Xiucheng Ly, Shun Shao, Lin Gui, Ruifeng Xu, Yulan He

Challenge:	Existing benchmarks fail to capture the challenges of instruction following in complex narrative contexts.
Approach:	They propose a training-free framework that identifies and edits instruction-relevant neurons using only natural language instructions without requiring labelled data.
Outcome:	The proposed framework improves instruction following by identifying and editing instruction-relevant neurons using only natural language instructions, without requiring labelled data.

SciAssess: Benchmarking LLM Proficiency in Scientific Literature Analysis (2025.findings-naacl)

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Hengxing Cai, Xiaochen Cai, Junhan Chang, Sihang Li, Lin Yao, Wang Changxin, Zhifeng Gao, Hongshuai Wang, Li Yongge, Mujie Lin, Shuwen Yang, Jiankun Wang, Mingjun Xu, Jin Huang, Xi Fang, Jiaxi Zhuang, Yuqi Yin, Yaqi Li, Changhong Chen, Zheng Cheng, Zifeng Zhao, Linfeng Zhang, Guolin Ke

Challenge:	Existing benchmarks fail to adequately evaluate the proficiency of Large Language Models (LLMs) Existing standards do not cover the skills needed to evaluate LLMs in scientific literature analysis.
Approach:	They propose a benchmark to evaluate the proficiency of large language models in scientific literature analysis.
Outcome:	SciAssess evaluates 11 LLMs on multiple tasks across scientific fields.

Meta-Information Guided Meta-Learning for Few-Shot Relation Classification (2020.coling-main)

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Bowen Dong, Yuan Yao, Ruobing Xie, Tianyu Gao, Xu Han, Zhiyuan Liu, Fen Lin, Leyu Lin, Maosong Sun

CTCC: A Robust and Stealthy Fingerprinting Framework for Large Language Models via Cross-Turn Contextual Correlation Backdoor (2025.emnlp-main)

Challenge:	Existing meta-learning models rely on implicit instance statistics and are unreliability and weak interpretability.
Approach:	They propose a meta-information guided meta-learning framework that uses semantics to guide meta- learning . experimental results demonstrate the effectiveness of the proposed framework .
Outcome:	The proposed framework can establish connections between instance-based information and semantic-based data, enabling faster initialization and adaptation.

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Zhenhua Xu, Xixiang Zhao, Xubin Yue, Shengwei Tian, Changting Lin, Meng Han

Temporal Precision Matters: Brain-Tuning Speech Language Models with Millisecond-Resolution Neural Signals (2026.acl-long)

Challenge:	Existing methods for fingerprinting model ownership traces are vulnerable to illegal plagiarism and are not reliable.
Approach:	They propose a rule-driven fingerprinting framework that encodes contextual correlations across multiple dialogue turns.
Outcome:	The proposed framework achieves stronger stealth and robustness than previous work.

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Zhejun Zhang, Wenqing Zhou, Haozhe Xu, Lin Zhang, Lei Li

Transition-based Directed Graph Construction for Emotion-Cause Pair Extraction (2020.acl-main)

Challenge:	Language models have emerged as powerful tools for predicting human brain activity during language comprehension.
Approach:	They propose a technique that leverages electrocorticography’s millisecond precision to train speech language models.
Outcome:	The proposed technique improves brain alignment over pretrained and distillation models and produces higher gains in higher-order language regions.

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Chuang Fan, Chaofa Yuan, Jiachen Du, Lin Gui, Min Yang, Ruifeng Xu

MoEL: Mixture of Empathetic Listeners (D19-1)

Challenge:	Existing methods to extract emotions and causes from unannotated text are pipelined, causing error propagation.
Approach:	They propose to transform a task into a procedure of parsing-like directed graph construction . they propose to generate a directed graph with labeled edges based on a sequence of actions .
Outcome:	The proposed method outperforms the state-of-the-art methods by 6.71% (p0.01) in F1 measure.

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Zhaojiang Lin, Andrea Madotto, Jamin Shin, Peng Xu, Pascale Fung

Call Me When Necessary: LLMs can Efficiently and Faithfully Reason over Structured Environments (2024.findings-acl)

Challenge:	Neural network approaches for conversation models have shown to be successful in generating fluent and relevant responses.
Approach:	They propose a novel end-to-end approach for modeling empathy in dialogue systems by using Mixture of Empathetic Listeners (MoEL).
Outcome:	The proposed model outperforms multitask training baseline in terms of empathy, relevance, and fluency.

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Sitao Cheng, Ziyuan Zhuang, Yong Xu, Fangkai Yang, Chaoyun Zhang, Xiaoting Qin, Xiang Huang, Ling Chen, Qingwei Lin, Dongmei Zhang, Saravan Rajmohan, Qi Zhang

An Auto-Encoder Matching Model for Learning Utterance-Level Semantic Dependency in Dialogue Generation (D18-1)

Challenge:	Large Language Models (LLMs) have shown potential in reasoning over structured environments, e.g., knowledge graphs and tables.
Approach:	They propose a framework that allows LLMs to efficiently and faithfully reason over structured environments.
Outcome:	The proposed framework surpasses state-of-the-art fine-tuned methods on three KGQA and two TableQA datasets and surpasse CWQ and WTQ methods.

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Liangchen Luo, Jingjing Xu, Junyang Lin, Qi Zeng, Xu Sun

Large Language Models Fall Short: Understanding Complex Relationships in Detective Narratives (2024.findings-acl)

Challenge:	Experimental results show that our model can generate semantically coherent responses compared to baseline models.
Approach:	They propose an Auto-Encoder Matching model to learn utterance-level semantic dependency . their model contains two auto-encoders and one mapping module .
Outcome:	Experimental results show that the proposed model can generate high coherence and fluency compared to baseline models.

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Runcong Zhao, Qinglin Zhu, Hainiu Xu, Jiazheng Li, Yuxiang Zhou, Yulan He, Lin Gui

s3: You Don’t Need That Much Data to Train a Search Agent via RL (2025.emnlp-main)

Challenge:	Existing datasets for narrative understanding fail to represent complexity and uncertainty of relationships in real-life social scenarios.
Approach:	They propose a benchmark for extracting and analysing intricate character relation graphs from detective narratives using large-scale large-language models.
Outcome:	The proposed dataset extracts and analyses character relation graphs from detective narratives using advanced Large Language Models like GPT-3.5, GPT-4, and Llama2 .

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Pengcheng Jiang, Xueqiang Xu, Jiacheng Lin, Jinfeng Xiao, Zifeng Wang, Jimeng Sun, Jiawei Han

LLM Critics Help Catch Bugs in Mathematics: Towards a Better Mathematical Verifier with Natural Language Feedback (2025.findings-acl)

Challenge:	Existing approaches to optimize retrieval using search-only metrics ignore downstream utility and fine-tune entire LLM to jointly reason and retrieve limit retrieval utility and compatibility with frozen or proprietary models.
Approach:	They propose a lightweight, model-agnostic framework that decouples the searcher from the generator and trains the search user using a Gain Beyond RAG reward.
Outcome:	The proposed framework outperforms baselines trained on over 70 more data with 2.4k training samples.

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Bofei Gao, Zefan Cai, Runxin Xu, Peiyi Wang, Ce Zheng, Runji Lin, Keming Lu, Dayiheng Liu, Chang Zhou, Wen Xiao, Tianyu Liu, Baobao Chang

PsyDT: Using LLMs to Construct the Digital Twin of Psychological Counselor with Personalized Counseling Style for Psychological Counseling (2025.acl-long)

Challenge:	Existing mathematical verifiers are trained with binary classification labels, which are not informative enough for the model to accurately assess the solutions.
Approach:	They propose a natural language feedback-enhanced verifier that can validate the correctness of response generated by policy models by constructing automatically generated training data and a two-stage training paradigm.
Outcome:	The proposed verifier significantly improves in verification and reinforcement learning and alleviates data-demanding problems of the reward model.

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Haojie Xie, Yirong Chen, Xiaofen Xing, Jingkai Lin, Xiangmin Xu

Memorizing is Not Enough: Deep Knowledge Injection Through Reasoning (2025.acl-long)

Challenge:	Existing mental health LLMs do not consider the fact that different psychological counselors exhibit different personal styles.
Approach:	They propose a framework that uses LLMs to construct the digital twin of psychological counselor with personalized counseling style.
Outcome:	The proposed framework can synthesize multi-turn dialogues that closely resemble real-world counseling cases and demonstrate better performance compared to baselines.

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Ruoxi Xu, Yunjie Ji, Boxi Cao, Yaojie Lu, Hongyu Lin, Xianpei Han, Ben He, Yingfei Sun, Xiangang Li, Le Sun

AgentCPM-GUI: Building Mobile-Use Agents with Reinforcement Fine-Tuning (2025.emnlp-demos)

Challenge:	Existing knowledge injection frameworks focus on knowledge memorization and retrieval, but static nature of large language models leads to outdated information as the real world evolves or when adapting to domain-specific knowledge.
Approach:	They propose a four-tier knowledge injection framework that defines the levels of knowledge injection: memorization, retrieval, reasoning, and association.
Outcome:	The proposed framework defines the levels of knowledge injection: memorization, retrieval, reasoning, and association.

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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

OctoBench: Benchmarking Scaffold-Aware Instruction Following in Repository-Grounded Agentic Coding (2026.acl-long)

Challenge:	Large language model agents have enabled GUI-based automation, but their deployment is limited by noisy data, poor generalization, and lack of support for non-English GUIs.
Approach:	They propose an 8B-parameter GUI agent built for robust and efficient on-device GUI interaction.
Outcome:	The proposed GUI agent achieves promising performance on five public benchmarks and proposed Chinese benchmark CAGUI.

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Deming Ding, Shichun Liu, Enhui Yang, Jiahang Lin, Ziying Chen, Shihan Dou, Honglin Guo, Weiyu Cheng, Pengyu Zhao, Chengjun Xiao, Qunhong Zeng, Qi Zhang, Xuanjing Huang, Qidi Xu, Tao Gui

Programming Languages in NLP

Challenge:	coding scaffolds that follow heterogeneous instructions remain under-examined in software engineering . coding models are capable software agents, but their ability to follow constraints remains under-explored .
Approach:	They introduce OctoBench, which benchmarks scaffold-aware instruction following in agentic coding.
Outcome:	The proposed benchmark aims to accelerate the development of more scaffold-aware agents.

Continual Dialogue State Tracking via Example-Guided Question Answering (2023.emnlp-main)

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Hyundong Cho, Andrea Madotto, Zhaojiang Lin, Khyathi Chandu, Satwik Kottur, Jing Xu, Jonathan May, Chinnadhurai Sankar

Continual Training of Language Models for Few-Shot Learning (2022.emnlp-main)

Challenge:	Dialogue systems are frequently updated to accommodate new services, but naively updating them by continually training with data for new services causes catastrophic forgetting.
Approach:	They propose to reformulate dialogue state tracking (DST) as a bundle of example-guided question answering tasks to minimize the task shift between services.
Outcome:	The proposed model achieves state-of-the-art performance on DST continual learning metrics without relying on any complex regularization or parameter expansion methods.

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Zixuan Ke, Haowei Lin, Yijia Shao, Hu Xu, Lei Shu, Bing Liu

An Empirical Study of Instruction-tuning Large Language Models in Chinese (2023.findings-emnlp)

Challenge:	Recent work on applying large language models (LMs) achieves impressive performance in many NLP applications.
Approach:	They propose to continuously post-train an LM with unlabeled domains to expand its knowledge without forgetting previous skills.
Outcome:	The proposed system improves few-shot end-task learning in these domains.

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Qingyi Si, Tong Wang, Zheng Lin, Xu Zhang, Yanan Cao, Weiping Wang

Mitigating Hallucinations of Large Language Models in Medical Information Extraction via Contrastive Decoding (2024.findings-emnlp)

Challenge:	emergence of ChatGPT validates the potential of large language models (LLMs) in artificial general intelligence (AGI) however, the closed source of LLMs coupled with the requirement for massive computing resources has deterred researchers from reaching the LLM training stage.
Approach:	They propose to use Chinese instruction-tuning LLMs as a cookbook for customizing LLM models that can better respond to Chinese instructions.
Outcome:	The proposed LLM can be used to customize Chinese LLMs that can better respond to Chinese instructions.

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Derong Xu, Ziheng Zhang, Zhihong Zhu, Zhenxi Lin, Qidong Liu, Xian Wu, Tong Xu, Xiangyu Zhao, Yefeng Zheng, Enhong Chen

Jointly Learning Aspect-Focused and Inter-Aspect Relations with Graph Convolutional Networks for Aspect Sentiment Analysis (2020.coling-main)

Challenge:	Medical Information Extraction (MIE) tasks are a fundamental component of medical NLP.
Approach:	They propose an alternative adaptive constraint strategy to adjust the scale and scope of contrastive tokens.
Outcome:	The proposed approach selectively enhances the identification and classification capabilities while minimizing the influence of other inherent abilities in LLMs.

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Bin Liang, Rongdi Yin, Lin Gui, Jiachen Du, Ruifeng Xu

Diagnosing Failures in Large Language Models’ Answers: Integrating Error Attribution into Evaluation Framework (2025.findings-acl)

Challenge:	Existing methods for aspect sentiment analysis do not include explicit sentiment expressions.
Approach:	They propose to construct a heterogeneous graph by leveraging aspect-focused and inter-aspect contextual dependencies for the specific aspect.
Outcome:	The proposed model outperforms state-of-the-art methods on four benchmark datasets and significantly boosts performance in comparison with BERT.

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Zishan Xu, Shuyi Xie, Qingsong Lv, Shupei Xiao, Linlin Song, Sui Wenjuan, Fan Lin

Why Do More Experts Fail? A Theoretical Analysis of Model Merging (2026.acl-long)

Challenge:	Existing evaluation models lack error attribution capability due to their proprietary nature.
Approach:	They propose a misattribution framework with 6 primary and 15 secondary categories to facilitate in-depth analysis.
Outcome:	The proposed framework is based on a dataset specifically designed for error attribution, along with the corresponding scores and feedback.

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Zijing Wang, Xingle Xu, YongKang Liu, Yiqun Zhang, Peiqin Lin, Shi Feng, Daling Wang, Xiaocui Yang, Hinrich Schuetze

Challenge:	Existing methods for model merging struggle to maintain performance gains as the number of merged models increases.
Approach:	They propose a Reparameterized Heavy-Tailed method to extend the merged model’s coverage and enhance performance.
Outcome:	The proposed method extends the merged model’s coverage and enhances performance on 19 benchmarks, including knowledge-intensive and general-purpose tasks.

VIGIL: Defending LLM Agents Against Tool-Stream Injection via Verify-Before-Commit (2026.acl-long)

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Junda Lin, Zhaomeng Zhou, Zhi Zheng, Shuochen Liu, Tong Xu, Yong Chen, Enhong Chen

Evaluating Readability and Faithfulness of Concept-based Explanations (2024.emnlp-main)

Challenge:	Existing defenses for indirect prompt injection are limited by static protection mechanisms . existing models prioritize injected rules due to strict alignment, whereas static protections sever the feedback loop required for adaptive reasoning.
Approach:	They propose a framework that shifts the paradigm from restrictive isolation to a verify-before-commit protocol.
Outcome:	The proposed framework outperforms state-of-the-art dynamic defenses by reducing the attack success rate by over 22% while more thandoubling utility under attack compared to static baselines.

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Meng Li, Haoran Jin, Ruixuan Huang, Zhihao Xu, Defu Lian, Zijia Lin, Di Zhang, Xiting Wang

Explainability & Interpretability in NLP

Challenge:	Existing methods for evaluating concepts from different perspectives lack a unified formalization.
Approach:	They propose a formal definition of concepts generalizing to diverse concept-based explanations’ settings and apply it to other types of explanations or tasks.
Outcome:	Extensive experimental analysis was carried out to determine the evaluation measures for explanation evaluation measures.

Deconvolution-Based Global Decoding for Neural Machine Translation (C18-1)

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Junyang Lin, Xu Sun, Xuancheng Ren, Shuming Ma, Jinsong Su, Qi Su

Is LLM an Overconfident Judge? Unveiling the Capabilities of LLMs in Detecting Offensive Language with Annotation Disagreement (2025.findings-acl)

Challenge:	Existing models for Neural Machine Translation (NMT) use Recurrent Neural Network (RNN) to generate translation word by word following a sequential order.
Approach:	They propose a Neural Machine Translation (NMT) model that decodes the sequence with the guidance of its structural prediction of the target-side context.
Outcome:	The proposed model is more competitive compared with the state-of-the-art methods and reduces repetition with the instruction from the target-side context for decoding.

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Junyu Lu, Kai Ma, Kaichun Wang, Kelaiti Xiao, Roy Ka-Wei Lee, Bo Xu, Liang Yang, Hongfei Lin

RepoGenesis: Benchmarking End-to-End Microservice Generation from Readme to Repository (2026.acl-long)

Challenge:	Existing studies focus on evaluating large language models' ability to handle disagreement cases.
Approach:	They evaluate the performance of large language models in detecting offensive language at varying levels of agreement.
Outcome:	The proposed model improves detection accuracy and model alignment with human judgment by using disagreement samples in training.

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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.

Large Language Model-based Human-Agent Collaboration for Complex Task Solving (2024.findings-emnlp)

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Xueyang Feng, Zhi-Yuan Chen, Yujia Qin, Yankai Lin, Xu Chen, Zhiyuan Liu, Ji-Rong Wen

Tree of Agents: Improving Long-Context Capabilities of Large Language Models through Multi-Perspective Reasoning (2025.findings-emnlp)

Challenge:	Recent advances in large language models have led to the development of LLM-based autonomous agents.
Approach:	They propose a Reinforcement Learning-based Human-Agent Collaboration method which trains a policy model to determine the most opportune stages for human intervention within the task-solving process.
Outcome:	The proposed method improves human-agent collaboration significantly through well-planned, limited human intervention.

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Song Yu, Xiaofei Xu, Ke Deng, Li Li, Lin Tian

Knowledgeable or Educated Guess? Revisiting Language Models as Knowledge Bases (2021.acl-long)

Challenge:	Large language models face persistent challenges when handling long-context tasks . existing methods that reduce input have the risk of discarding key information .
Approach:	To address this issue, we propose a multi-agent reasoning framework called Tree of Agents . the framework segments input into chunks processed by independent agents .
Outcome:	The proposed model outperforms baseline models on long-context tasks.

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Boxi Cao, Hongyu Lin, Xianpei Han, Le Sun, Lingyong Yan, Meng Liao, Tong Xue, Jin Xu

AgentAsk: Multi-Agent Systems Need to Ask (2026.acl-long)

Challenge:	Recent studies show that pre-trained masked language models can be factual knowledge bases.
Approach:	They conduct a rigorous study to explore the underlying predicting mechanisms of MLMs . they find that previous decent performance mainly owes to the biased prompts which overfit dataset artifacts a .
Outcome:	The proposed model improves on illustrative cases and external contexts . the results question the previous findings that MLMs can be reliable factual knowledge bases .

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Bohan Lin, Kuo Yang, Zelin Tan, Yingchuan Lai, Chen Zhang, Guibin Zhang, Xinlei Yu, Miao Yu, Xu Wang, Yudong Zhang, Yang Wang

Confidence v.s. Critique: A Decomposition of Self-Correction Capability for LLMs (2025.acl-long)

Challenge:	Multi-agent systems fail to consistently outperform strong single-a agent baselines due to error propagation at inter-aggent message handoffs.
Approach:	They propose an edge-level error taxonomy that identifies four main errors in multi-agent interactions as data gaps, signal corruption, referential drift and capacity gaps as primary sources of failure.
Outcome:	The proposed module outperforms existing systems on five benchmarks and is architecture-agnostic.

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Zhe Yang, Yichang Zhang, Yudong Wang, Ziyao Xu, Junyang Lin, Zhifang Sui

Challenge:	Existing approaches to improve self-correction performance of Large Language Models are based on intrinsic selfcorrectione, which allows the model to check and revise its selfgenerated answers without external feedback.
Approach:	They propose to decompose the self-correction capability into confidence and critique capabilities and a metric for overall self-corretion capability evaluation.
Outcome:	The proposed method outperforms vanilla SFT and achieves much higher accuracy after self-correction.

MMDialog: A Large-scale Multi-turn Dialogue Dataset Towards Multi-modal Open-domain Conversation (2023.acl-long)

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Jiazhan Feng, Qingfeng Sun, Can Xu, Pu Zhao, Yaming Yang, Chongyang Tao, Dongyan Zhao, Qingwei Lin

TrendSim: Simulating Trending Topics in Social Media Under Poisoning Attacks with LLM-based Multi-agent System (2025.findings-naacl)

Challenge:	MMDialog is a dataset of 1.08 million real-world dialogues with 1.53 million unique images across 4,184 topics.
Approach:	They propose to use a curated set of 1.08 million dialogues with 1.53 million unique images to generalize the open domain.
Outcome:	The proposed system can predict responses to multi-modal content with state-of-the-art techniques and measure their performance.

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Zeyu Zhang, Jianxun Lian, Chen Ma, Yaning Qu, Ye Luo, Lei Wang, Rui Li, Xu Chen, Yankai Lin, Le Wu, Xing Xie, Ji-Rong Wen

BC-Prover: Backward Chaining Prover for Formal Theorem Proving (2024.emnlp-main)

Challenge:	Trending topics bring in a new channel for poisoning attacks, resulting in negative impacts on society.
Approach:	They propose an LLM-based multi-agent system to simulate trending topics in social media . they propose a time-aware interaction mechanism, centralized message dissemination, and an interactive system .
Outcome:	The proposed system simulates trending topics under poisoning attacks on social media platforms.

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Yuhang He, Jihai Zhang, Jianzhu Bao, Fangquan Lin, Cheng Yang, Bing Qin, Ruifeng Xu, Wotao Yin

RAAMove: A Corpus for Analyzing Moves in Research Article Abstracts (2024.lrec-main)

Challenge:	Existing methods for interactive theorem proving in formal logic lack robustness and robustness.
Approach:	They propose a backward chaining framework guided by pseudo steps for proofstep generation that prioritizes pseudo steps.
Outcome:	The proposed framework improves on the miniF2F benchmark.

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Hongzheng Li, Ruojin Wang, Ge Shi, Xing Lv, Lei Lei, Chong Feng, Fang Liu, Jinkun Lin, Yangguang Mei, Linnan Xu

LongFaith: Enhancing Long-Context Reasoning in LLMs with Faithful Synthetic Data (2025.findings-acl)

Challenge:	RAAMove is a comprehensive multi-domain corpus dedicated to the annotation of move structures in Research Article (RA) abstracts.
Approach:	They propose a multi-domain corpus dedicated to the annotation of move structures in RA abstracts.
Outcome:	The proposed corpus is based on a human-annotated dataset and a BERT-based model to verify its effectiveness.

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Cehao Yang, Xueyuan Lin, Chengjin Xu, Xuhui Jiang, Shengjie Ma, Aofan Liu, Hui Xiong, Jian Guo

AdapTime: Enabling Adaptive Temporal Reasoning in Large Language Models (2026.findings-acl)

Challenge:	Long-context processing ability has emerged as a significant challenge for large language models.
Approach:	They propose a pipeline for synthesizing faithful long-context reasoning instruction datasets . they integrate ground truth and citation-based reasoning prompts integrating them .
Outcome:	The proposed pipeline eliminates distractions and improves reasoning chains.

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Yimin Deng, Yejing Wang, Zhenxi Lin, Zichuan Fu, Guoshuai Zhao, Derong Xu, Yefeng Zheng, Xiangyu Zhao, Xian Wu, Li Zhu, Xueming Qian

A Boundary Offset Prediction Network for Named Entity Recognition (2023.findings-emnlp)

Challenge:	Existing methods for temporal reasoning are limited and apply a fixed pipeline to all questions.
Approach:	They propose an adaptive temporal reasoning method that dynamically executes reasoning steps based on context and task requirements.
Outcome:	Experiments on two temporal QA benchmarks show the proposed method works.

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Minghao Tang, Yongquan He, Yongxiu Xu, Hongbo Xu, Wenyuan Zhang, Yang Lin

Mining Cross-Cultural Differences and Similarities in Social Media (P18-1)

Challenge:	Named entity recognition (NER) is a fundamental task in natural language processing . span-based methods assign entity types to text spans, resulting in imbalanced sample space .
Approach:	They propose a method that predicts boundary offsets between candidate and nearest spans . the method integrates entity type and span representations to generate type-aware boundary offset .
Outcome:	The proposed method outperforms existing methods on eight widely-used NER datasets.

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Bill Yuchen Lin, Frank F. Xu, Kenny Zhu, Seung-won Hwang

A Semantic Mention Graph Augmented Model for Document-Level Event Argument Extraction (2024.lrec-main)

Challenge:	a new paper examines the problem of computing cross-cultural differences and similarities in natural language understanding . cross-culture differences are important for cross-lingual research, especially in social media .
Approach:	They propose a framework for computing cross-cultural differences and similarities from social media . they propose to use a social media platform to find similar terms for slang across languages .
Outcome:	The proposed framework outperforms baseline methods on two novel tasks.

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Jian Zhang, Changlin Yang, Haiping Zhu, Qika Lin, Fangzhi Xu, Jun Liu

IntactKV: Improving Large Language Model Quantization by Keeping Pivot Tokens Intact (2024.findings-acl)

Challenge:	Document-level Event Argument Extraction (DEAE) aims to identify arguments and their specific roles from unstructured document.
Approach:	They propose a document-prompt-based method for document-level event argument extraction that uses a semantic mention graph to capture relations between documents and prompts.
Outcome:	The proposed method surpasses baseline methods and achieves state-of-the-art performance on RAMS and WikiEvents datasets.

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Ruikang Liu, Haoli Bai, Haokun Lin, Yuening Li, Han Gao, Zhengzhuo Xu, Lu Hou, Jun Yao, Chun Yuan

Stochastic Bridges as Effective Regularizers for Parameter-Efficient Tuning (2023.findings-acl)

Challenge:	Existing quantization methods are compromising performance of large language models (LLMs) despite their high computational intensity, LLMs are still demanding intensive computation.
Approach:	They propose to generate the KV cache of pivot tokens losslessly from the full-precision model.
Outcome:	The proposed method generates the KV cache of pivot tokens losslessly from the full-precision model with no extra inference overhead.

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Weize Chen, Xu Han, Yankai Lin, Zhiyuan Liu, Maosong Sun, Jie Zhou

A Diffusion Model for Event Skeleton Generation (2023.findings-acl)

Challenge:	Existing methods for tuning pre-trained language models ignore the running cost and only optimize the terminal cost.
Approach:	They propose to use stochastic bridges to regularize intermediate states and use regularization as running cost of PETs.
Outcome:	The proposed methods can be used to tune large pre-trained language models . they can be compared to full-parameter fine-tuning by tuning a small number of parameters .

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Fangqi Zhu, Lin Zhang, Jun Gao, Bing Qin, Ruifeng Xu, Haiqin Yang

Structured Data in NLP

Challenge:	Existing methods for event schema generation are noise-sensitive and error-accumulating, e.g., inability to correct errors while generating schema.
Approach:	They propose a novel diffusion event graph model that embeds and roundes event graphs into learnable latent representations and a denoising process to maintain the model's robustness.
Outcome:	The proposed model achieves better results than existing state-of-the-art models on three IED bombing datasets.

SnapNTell: Enhancing Entity-Centric Visual Question Answering with Retrieval Augmented Multimodal LLM (2024.findings-emnlp)

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Jielin Qiu, Andrea Madotto, Zhaojiang Lin, Paul Crook, Yifan Xu, Babak Damavandi, Xin Dong, Christos Faloutsos, Lei Li, Seungwhan Moon

RealMedDial: A Real Telemedical Dialogue Dataset Collected from Online Chinese Short-Video Clips (2022.coling-1)

Challenge:	Vision-extended LLMs have made significant strides in VQA, but they still encounter significant difficulties in handling queries involving long-tail entities.
Approach:	They propose a benchmark to test models' ability to identify entities and provide detailed, entity-specific knowledge by combining 10 images and 10 knowledge-intensive QA pairs.
Outcome:	The proposed model outperforms existing methods on the SnapNTell dataset, achieving a 66.5% improvement in the BELURT score.

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Bo Xu, Hongtong Zhang, Jian Wang, Xiaokun Zhang, Dezhi Hao, Linlin Zong, Hongfei Lin, Fenglong Ma

WarriorCoder: Learning from Expert Battles to Augment Code Large Language Models (2025.acl-long)

Challenge:	Existing medical dialogue systems are limited by the lack of corpora and data from real scenarios.
Approach:	They construct a Chinese medical dialogue dataset based on real medical consultations.
Outcome:	The proposed dataset is applicable to a wide range of NLP tasks with respect to medical dialogue.

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Huawen Feng, Pu Zhao, Qingfeng Sun, Can Xu, Fangkai Yang, Lu Wang, Qianli Ma, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang, Qi Zhang

PLAWBENCH: A Rubric-Based Benchmark for Evaluating LLMs in Real-World Legal Practice (2026.acl-long)

Challenge:	Recent code large language models have demonstrated impressive performance on code-related tasks.
Approach:	They propose a paradigm that learns from expert battles to address these limitations . they create an arena where leading LLMs challenge each other with evaluations .
Outcome:	The proposed model improves on existing models by leveraging expert battles . it achieves state-of-the-art performance even without relying on proprietary models .

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Yuzhen Shi, Huanghai Liu, Yiran HU, Song Gaojie, Xu Xinran, Yubo Ma, Tianyi Tang, Li Zhang, Qingjing Chen, Feng Di, Wenbo Lv, Weiheng Wu, Kexin Yang, Sen Yang, Wei Wang, Rongyao Shi, Qiu Yuanyang, Yuemeng Qi, Zhang Jingwen, Sui Xiaoyu, Yifan Chen, Zhang Yi, An Yang, Bowen Yu, Dayiheng Liu, Junyang Lin, Weixing Shen, Bing Zhao, Charles L. A. Clarke, HU Wei

CINO: A Chinese Minority Pre-trained Language Model (2022.coling-1)

Challenge:	Existing benchmarks for large language models (LLMs) are coarse, single-dimensional metrics and do not explicitly assess fine-grained legal reasoning.
Approach:	They propose a Practical Law Benchmark to evaluate large language models in real-world legal practice scenarios.
Outcome:	The proposed model is based on 850 questions and 13 scenarios with expert-designed evaluation rubrics.

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Ziqing Yang, Zihang Xu, Yiming Cui, Baoxin Wang, Min Lin, Dayong Wu, Zhigang Chen

Emergent Modularity in Pre-trained Transformers (2023.findings-acl)

Challenge:	Existing multilingual pre-trained language models do not perform well on some low-resource languages.
Approach:	They propose a multilingual pre-trained language model for Chinese minority languages . they collect documents from Wikipedia and construct two classification datasets .
Outcome:	The proposed model outperforms baseline models on various classification tasks.

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Zhengyan Zhang, Zhiyuan Zeng, Yankai Lin, Chaojun Xiao, Xiaozhi Wang, Xu Han, Zhiyuan Liu, Ruobing Xie, Maosong Sun, Jie Zhou

Prototype Tuning: A Meta-Learning Approach for Few-Shot Document-Level Relation Extraction with Large Language Models (2025.findings-naacl)

Challenge:	Existing studies on pre-trained Transformers show that they learn fine-grained neuron functions.
Approach:	They examine the presence of modularity in pre-trained Transformers . they focus on Mixture-of-Experts, a promising candidate for modularity .
Outcome:	The proposed structure stabilizes at the early stage, which is faster than neuron stabilization.

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Dinghao Pan, Yuanyuan Sun, Bo Xu, Jiru Li, Zhihao Yang, Ling Luo, Hongfei Lin, Jian Wang

FastCorrect 2: Fast Error Correction on Multiple Candidates for Automatic Speech Recognition (2021.findings-emnlp)

Challenge:	Few-Shot Document-Level Relation Extraction (FSDLRE) aims to develop models capable of generalizing to new categories with minimal support examples.
Approach:	They propose a meta-training approach to train Large Language Models to improve their ICL capabilities . they construct simulated episodes using relation types that do not overlap with test corpus .
Outcome:	Experimental results show that the proposed approach outperforms baseline models on few-shot tasks.

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Yichong Leng, Xu Tan, Rui Wang, Linchen Zhu, Jin Xu, Wenjie Liu, Linquan Liu, Xiang-Yang Li, Tao Qin, Edward Lin, Tie-Yan Liu

Enhancing Neural Topic Model with Multi-Level Supervisions from Seed Words (2023.findings-acl)

Challenge:	Error correction is widely used in automatic speech recognition (ASR) to post-process the generated sentence.
Approach:	They propose a fast correction model that takes multiple ASR candidates as input for better correction accuracy.
Outcome:	The proposed model can reduce the word error rate (WER) with multiple candidates by 3.2% and 2.6%.

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Yang Lin, Xin Gao, Xu Chu, Yasha Wang, Junfeng Zhao, Chao Chen

Second Language (Arabic) Acquisition of LLMs via Progressive Vocabulary Expansion (2025.acl-long)

Challenge:	Existing topic seed words are difficult to incorporate into topic models due to the semantic diversity of natural language.
Approach:	They propose a neural topic model enhanced with supervisions from seed words on word and document levels.
Outcome:	The proposed model outperforms the state-of-the-art seeded topic models in terms of topic quality and classification accuracy.

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Jianqing Zhu, Huang Huang, Zhihang Lin, Juhao Liang, Zhengyang Tang, Khalid Almubarak, Mosen Alharthi, Bang An, Juncai He, Xiangbo Wu, Fei Yu, Junying Chen, Ma Zhuoheng, Yuhao Du, He Zhang, Saied Alshahrani, Emad A. Alghamdi, Lian Zhang, Ruoyu Sun, Haizhou Li, Benyou Wang, Jinchao Xu

PLATO-XL: Exploring the Large-scale Pre-training of Dialogue Generation (2022.findings-aacl)

Challenge:	In the evolving landscape of large language models, the predominant focus has been on English and Chinese.
Approach:	They propose to utilize Arabic-specific vocabulary in the tokenizer to accelerate decoding.
Outcome:	The proposed model achieves decent performance comparable to the best Arabic LLMs across various Arabic benchmarks.

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Siqi Bao, Huang He, Fan Wang, Hua Wu, Haifeng Wang, Wenquan Wu, Zhihua Wu, Zhen Guo, Hua Lu, Xinxian Huang, Xin Tian, Xinchao Xu, Yingzhan Lin, Zheng-Yu Niu

MUR: Momentum Uncertainty guided Reasoning for Large Language Models (2026.acl-long)

Challenge:	Experimental results show PLATO-XL achieves state-of-the-art results across multiple conversational tasks.
Approach:	They propose to train PLATO-XL models with up to 11 billion parameters, trained on Chinese and English social media conversations.
Outcome:	The proposed model achieves state-of-the-art on multiple conversational tasks, verifying its potential as a foundation model of conversational AI.

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Hang Yan, Fangzhi Xu, Rongman Xu, Yifei Li, Jian Zhang, Haoran Luo, Xiaobao Wu, Anh Tuan Luu, Haiteng Zhao, Qika Lin, Jun Liu

GAM: Hierarchical Graph-based Agentic Memory for LLM Agents (2026.acl-long)

Challenge:	Existing methods for optimizing reasoning quality are limited by overthinking.
Approach:	They propose a method that allocates thinking budgets to critical reasoning steps by tracking and aggregating step-wise uncertainty over time.
Outcome:	The proposed method reduces computation by over 45% on average while improving accuracy by 0.33–3.46%.

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Zhaofen Wu, Hanrong Zhang, Fulin Lin, Wujiang Xu, Xinran Xu, Yankai Chen, Henry Peng Zou, Shaowen Chen, Weizhi Zhang, Xue Liu, Philip S. Yu, Hongwei Wang

Structured Data in NLP

Challenge:	Current unified stream-based memory systems facilitate context updates but remain vulnerable to interference from transient noise.
Approach:	They propose a hierarchical Graph-based Agentic Memory framework that explicitly decouples memory encoding from consolidation to resolve conflict between rapid context perception and stable knowledge retention.
Outcome:	The proposed framework outperforms state-of-the-art benchmarks on LoCoMo and LongDialQA.

PersLEARN: Research Training through the Lens of Perspective Cultivation (2023.acl-demo)

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Yu-Zhe Shi, Shiqian Li, Xinyi Niu, Qiao Xu, Jiawen Liu, Yifan Xu, Shiyu Gu, Bingru He, Xinyang Li, Xinyu Zhao, Zijian Zhao, Yidong Lyu, Zhen Li, Sijia Liu, Lin Qiu, Jinhao Ji, Lecheng Ruan, Yuxi Ma, Wenjuan Han, Yixin Zhu

Challenge:	PersLEARN is a tool designed to facilitate the cultivation of scientific perspectives . junior researchers struggle to identify the perspectives reflected in the literature and struggle to develop their own viewpoints.
Approach:	They propose a tool to facilitate the cultivation of scientific perspectives by interacting with a prompt-based model and allowing students to develop their own perspectives explicitly.
Outcome:	The proposed tool outperforms baseline approaches across multiple domains of literature from different perspectives.

Structured Preference Optimization for Vision-Language Long-Horizon Task Planning (2025.emnlp-main)

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Xiwen Liang, Min Lin, Weiqi Ruan, Rongtao Xu, Yuecheng Liu, Jiaqi Chen, Bingqian Lin, Yuzheng Zhuang, Xiaodan Liang

Verify Before You Commit: Towards Faithful Reasoning in LLM Agents via Self-Auditing (2026.acl-long)

Challenge:	Existing vision-language planning methods struggle with long-horizon reasoning in dynamic environments due to the difficulty of training models to generate high-quality reasoning processes.
Approach:	They propose a framework that enhances reasoning and action selection for long-horizon task planning through structured evaluation and optimized training.
Outcome:	The proposed framework outperforms existing methods on short-horizon tasks but struggles with long-horizon reasoning in dynamic environments.

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Wenhao Yuan, Chenchen Lin, Jian Chen, Jinfeng Xu, Xuehe Wang, Edith Cheuk-Han Ngai

UReader: Universal OCR-free Visually-situated Language Understanding with Multimodal Large Language Model (2023.findings-emnlp)

Challenge:	Existing approaches to reasoning faithfulness violate constraints, authors say . a science fantasy series and companion books are among the books .
Approach:	They propose a framework that enforces verification over internal belief states within the agent before action commitment, achieving faithful reasoning.
Outcome:	The proposed framework improves reasoning faithfulness while preserving competitive end-task performance.

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Jiabo Ye, Anwen Hu, Haiyang Xu, Qinghao Ye, Ming Yan, Guohai Xu, Chenliang Li, Junfeng Tian, Qi Qian, Ji Zhang, Qin Jin, Liang He, Xin Lin, Fei Huang

HyperHatePrompt: A Hypergraph-based Prompting Fusion Model for Multimodal Hate Detection (2025.coling-main)

Challenge:	Existing studies for visually-situated language understanding have shown shallow zero-shot visual text recognition ability when fed a low-resolution image with salient text information.
Approach:	They propose a model for universal OCR-free visually-situated language understanding based on the Multimodal Large Language Model (MLLM) their model is jointly finetuned on a wide range of visually situated language understanding tasks via a unified instruction format.
Outcome:	The proposed model achieves state-of-the-art ocr-free performance in 8 out of 10 visually-situated language understanding tasks across 5 domains: documents, tables, charts, natural images, and webpage screenshots.

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Bo Xu, Erchen Yu, Jiahui Zhou, Hongfei Lin, Linlin Zong

Towards IP Intelligence: Benchmarking Large Language Models on Intellectual Property Knowledge and Practice (2026.findings-acl)

Challenge:	Existing models for multimodal hate detection lack implicit hateful cues, cross-modal-induced hate, and diversity of hate target groups.
Approach:	They propose a hypergraph-based prompting fusion model that uses LLMs to generate hate cue prompts and hypergraph learning to merge multimodal hate features.
Outcome:	The proposed model outperforms state-of-the-art models on two benchmark datasets showing that it can detect hate content across multiple modalities.

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Qiyao Wang, Guhong Chen, Hongbo Wang, Huaren Liu, Minghui Zhu, Zhifei Qin, Li Linwei, Yilin Yue, Shiqiang Wang, Jiayan Li, Wu Yihang, Ziqiang Liu, Longze Chen, Run Luo, Liyang Fan, Jiaming Li, Lei Zhang, Kan Xu, Hamid Alinejad-Rokny, Chengming Li, Shiwen Ni, Yuan Lin, Min Yang

CLEVE: Contrastive Pre-training for Event Extraction (2021.acl-long)

Challenge:	Existing datasets and benchmarks focus only on patents or cover limited aspects of the IP field, lacking alignment with real-world scenarios.
Approach:	They propose a bilingual IP task taxonomy and a large-scale bilingual benchmark to evaluate LLMs in real-world IP practice.
Outcome:	The proposed model achieves only 75.8% accuracy, indicating room for improvement . open-source IP and law-oriented models lag behind closed-source general-purpose models .

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Ziqi Wang, Xiaozhi Wang, Xu Han, Yankai Lin, Lei Hou, Zhiyuan Liu, Peng Li, Juanzi Li, Jie Zhou

COPR: Continual Human Preference Learning via Optimal Policy Regularization (2025.findings-acl)

Challenge:	Existing EE methods do not model event characteristics from large unsupervised data.
Approach:	They propose a contrastive pre-training framework for event extraction to better learn event knowledge from large unsupervised data and their semantic structures.
Outcome:	The proposed framework improves on ACE 2005 and MAVEN datasets on event extraction tasks.

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Han Zhang, Lin Gui, Yu Lei, Yuanzhao Zhai, Yehong Zhang, Zhuo Zhang, Yulan He, Hui Wang, Yue Yu, Kam-Fai Wong, Bin Liang, Ruifeng Xu

Answer-driven Deep Question Generation based on Reinforcement Learning (2020.coling-main)

Challenge:	Reinforcement Learning from Human Feedback (RLHF) is effective for aligning Large Language Models with human preferences, but its complex process limits its ability to continually learn human feedback.
Approach:	They propose a non-RL offline method to convert historical optimal policies into optimization constraints when continually learning new preferences.
Outcome:	The proposed method outperforms strong CL baselines in terms of reward-based evaluations and human assessment.

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Liuyin Wang, Zihan Xu, Zibo Lin, Haitao Zheng, Ying Shen

TECHS: Temporal Logical Graph Networks for Explainable Extrapolation Reasoning (2023.acl-long)

Challenge:	Existing methods for deep question generation focus on enhancing document representations, but little attention is paid to the answer information.
Approach:	They propose a deep question generation model that makes better use of the target answer as a guidance to facilitate question generation.
Outcome:	The proposed model outperforms state-of-the-art models in automatic and human evaluations on the hotpotQA dataset.

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Qika Lin, Jun Liu, Rui Mao, Fangzhi Xu, Erik Cambria

Code Generation From Flowcharts with Texts: A Benchmark Dataset and An Approach (2022.findings-emnlp)

Challenge:	Existing frameworks for extrapolating knowledge graphs are incomplete and do not represent real-world knowledge.
Approach:	They propose an explainable extrapolation reasoning framework that integrates propositional reasoning and first-order reasoning by introducing a reasoning graph that iteratively expands to find the answer.
Outcome:	The proposed framework outperforms state-of-the-art baselines in explaining future facts based on past counterparts.

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Zejie Liu, Xiaoyu Hu, Deyu Zhou, Lin Li, Xu Zhang, Yanzheng Xiang

Programming Languages in NLP

Challenge:	Currently, researchers focus on generating codes from requirement documents.
Approach:	They propose to generate source code from flowcharts with texts instead of directly translating requirements into codes.
Outcome:	The proposed model improves on the baselines by transforming flowcharts into pseudo-code . the proposed model is based on 320 flowchartes with their corresponding source codes .

LLM-Based Multi-Agent Systems are Scalable Graph Generative Models (2025.findings-acl)

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Jiarui Ji, Runlin Lei, Jialing Bi, Zhewei Wei, Xu Chen, Yankai Lin, Xuchen Pan, Yaliang Li, Bolin Ding

Adversarial Multi-lingual Neural Relation Extraction (C18-1)

Challenge:	Social graphs are mathematical structures stem from pairwise interactions between entities through nodes and edges.
Approach:	They propose a framework for dynamic, text-attributed social graph generation that simulates the temporal node and edge generation processes for zero-shot social graphs.
Outcome:	The proposed framework improves macroscopic graph structure metrics by 11% . the proposed model can generate graphs with up to 100,000 nodes or 10 million edges .

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Xiaozhi Wang, Xu Han, Yankai Lin, Zhiyuan Liu, Maosong Sun

Exploring the Capability Boundaries of LLMs in Mastering of Chinese Chouxiang Language (2026.findings-acl)

Challenge:	Existing models cannot capture consistency and diversity of relation patterns in different languages.
Approach:	They propose an adversarial multi-lingual neural relation extraction model which considers consistency and diversity among languages.
Outcome:	The proposed model outperforms the state-of-the-art models on real-world datasets.

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Dianqing Lin, Tian Lan, Jiali Zhu, Jiang Li, Wei Chen, Xu Liu, null Aruukhan, Xiangdong Su, Hongxu Hou, Guanglai Gao

Getting To Know You: User Attribute Extraction from Dialogues (2020.lrec-1)

Challenge:	Current state-of-the-art LLMs exhibit clear limitations on multiple tasks, while performing well on tasks that involve contextual semantic understanding.
Approach:	They propose a mouse-based benchmark to evaluate LLMs' performance on NLP tasks involving Chouxiang Language.
Outcome:	The proposed benchmark evaluates the performance of LLMs on six NLP tasks involving Chouxiang Language.

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

ImpRIF: Stronger Implicit Reasoning Leads to Better Complex Instruction Following (2026.acl-long)

Challenge:	a new method to extract user attributes from dialogues is needed to improve user understanding.
Approach:	They propose to leverage dialogues with conversational agents to automatically extract user attributes from dialogues.
Outcome:	The proposed model surpasses retrieval and generation baselines on human evaluation.

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Yuancheng Yang, Lin Yang, Xu Wang, Chao Tong, Haihua Yang

Ensemble Privacy Defense for Knowledge-Intensive LLMs against Membership Inference Attacks (2026.findings-eacl)

Challenge:	Experiments show that enhancing implicit reasoning capabilities can significantly improve complex instruction following in large language models.
Approach:	They propose a method to enhance LLMs’ understanding of implicit reasoning instructions by formalizing such instructions as verifiable reasoning graphs and fine-tuning with graph reasoning.
Outcome:	The proposed method outperforms existing models on five complex instruction following benchmarks and will be open-sourced in the near future.

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Haowei Fu, Bo Ni, Han Xu, Kunpeng Liu, Dan Lin, Tyler Derr

AMA: Adaptive Memory via Multi-Agent Collaboration (2026.findings-acl)

Challenge:	Large language models (LLMs) are the foundation of modern natural language processing, powering applications across diverse domains.
Approach:	They propose a model-agnostic defense framework which aggregates and evaluates the outputs of a knowledge-injected LLM, a base LLM and a dedicated judge model to enhance resistance against membership inference attacks.
Outcome:	The proposed framework reduces MIA success by up to 27.8% for SFT and 526.3% for RAG compared to inference-time baseline while maintaining answer quality.

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Weiquan Huang, Zixuan Wang, Hehai Lin, Sudong Wang, Bo Xu, Qian Li, Beier Zhu, Linyi Yang, Chengwei Qin

Breaking the Boundaries: A Unified Framework for Chinese Named Entity Recognition Across Text and Speech (2024.findings-emnlp)

Challenge:	Existing approaches to longterm memory rely on rigid retrieval granularity, accumulation-heavy maintenance strategies, and coarse-grained update mechanisms.
Approach:	They propose a framework that leverages coordinated agents to manage memory across multiple granularities.
Outcome:	The proposed framework outperforms state-of-the-art benchmarks while reducing token consumption by approximately 80%.

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Jinzhong Ning, Yuanyuan Sun, Bo Xu, Zhihao Yang, Ling Luo, Hongfei Lin

Analyzing and Mitigating Inconsistency in Discrete Speech Tokens for Neural Codec Language Models (2025.acl-long)

Challenge:	Existing approaches to Named Entity Recognition (NER) tasks are limited by the complexity of the data and the potential connections between tasks.
Approach:	They propose a task to break the boundaries between different modal NER tasks by using a unified data format for inputs from different modalités.
Outcome:	The proposed task breaks the boundaries between different modal NER tasks and is a unified implementation of them.

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Wenrui Liu, Zhifang Guo, Jin Xu, Yuanjun Lv, Yunfei Chu, Zemin Liu, Junyang Lin

Mitigating Hallucinations in Multi-modal Large Language Models via Image Token Attention-Guided Decoding (2025.naacl-long)

Challenge:	Recent advances in Large Language Models (LLMs) have demonstrated significant strides in generating high-quality speech . discretizing speech by neural audio codecs often results in sequences that differ from text sequences .
Approach:	They quantitatively analyze the Discrete Representation Inconsistency phenomenon within popular audio tokenizers such as EnCodec.
Outcome:	The proposed method mitigates the DRI phenomenon within popular audio tokenizers such as EnCodec.

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Xinhao Xu, Hui Chen, Mengyao Lyu, Sicheng Zhao, Yizhe Xiong, Zijia Lin, Jungong Han, Guiguang Ding

Unsupervised Graph-Text Mutual Conversion with a Unified Pretrained Language Model (2023.acl-long)

Challenge:	Multi-modal large language models (MLLMs) generate plausible but incorrect content, resulting in hallucinations . recent advances in MLLM technology have demonstrated their outstanding performance in a variety of visual tasks, such as object detection.
Approach:	They propose a plug-and-play method which leverages MLLMs’ internal representations to mitigate hallucinations by analyzing input and output tokens.
Outcome:	The proposed method exploits MLLMs’ internal representations to mitigate hallucinations.

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Yi Xu, Shuqian Sheng, Jiexing Qi, Luoyi Fu, Zhouhan Lin, Xinbing Wang, Chenghu Zhou

Low-Resource NLP

Challenge:	Existing unsupervised approaches for learning knowledge graphs require multiple modules and require entity information or relation type for training.
Approach:	They propose a method that uses a unified pretrained language model to achieve fully unsupervised graph-text mutual conversion for the first time.
Outcome:	The proposed method outperforms state-of-the-art methods for G2T and T2G tasks by fine-tuning only one pretrained model.

Reduce Redundancy Then Rerank: Enhancing Code Summarization with a Novel Pipeline Framework (2024.lrec-main)

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Xiaoyu Hu, Xu Zhang, Zexu Lin, Deyu Zhou

HyCoRec: Hypergraph-Enhanced Multi-Preference Learning for Alleviating Matthew Effect in Conversational Recommendation (2024.acl-long)

Challenge:	Existing code summarization models lack redundant tokens and are plagued by exposure bias.
Approach:	They propose a pipeline framework to reduce redundancy then rerank that eliminates redundant information in code representation space and a re-ranking model to select more suitable summary candidates.
Outcome:	The proposed framework overrides state-of-the-art approaches on six datasets from the CodeSearchNet benchmark.

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Yongsen Zheng, Ruilin Xu, Ziliang Chen, Guohua Wang, Mingjie Qian, Jinghui Qin, Liang Lin

Words Worth a Thousand Pictures: Measuring and Understanding Perceptual Variability in Text-to-Image Generation (2024.emnlp-main)

Challenge:	Existing methods to study the Matthew effect in Recommender Systems (RSs) however, it is amplified when the user interacts with the system over time.
Approach:	They propose a paradigm to alleviate the Matthew effect in conversational recommendation by learning multi-aspect preferences.
Outcome:	The proposed paradigm achieves state-of-the-art performance and superior of alleviating Matthew effect in conversational recommendation tasks.

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Raphael Tang, Crystina Zhang, Lixinyu Xu, Yao Lu, Wenyan Li, Pontus Stenetorp, Jimmy Lin, Ferhan Ture

Open Hierarchical Relation Extraction (2021.naacl-main)

Challenge:	Current diffusion models do not cover recent models, thus we curate three test sets for evaluation.
Approach:	They propose a human-calibrated measure of variability in a set of images bootstrapped from existing image-pair perceptual distances.
Outcome:	The proposed model outperforms nine baselines by 18 points in accuracy and matches graded human judgements 78% of the time.

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Kai Zhang, Yuan Yao, Ruobing Xie, Xu Han, Zhiyuan Liu, Fen Lin, Leyu Lin, Maosong Sun

Select2Reason: Efficient Instruction-Tuning Data Selection for Long-CoT Reasoning (2026.findings-acl)

Challenge:	Existing OpenRE methods cast different relation types in isolation without considering their hierarchical dependency.
Approach:	They propose a framework to establish bidirectional connections between OpenRE and relation hierarchies by integrating hierarchy information into relation representations.
Outcome:	The proposed framework outperforms state-of-the-art models on relation clustering and hierarchy expansion.

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Cehao Yang, Xueyuan Lin, Xiaojun Wu, Chengjin Xu, Xuhui Jiang, Honghao Liu, Hui Xiong, Jian Guo

How to Determine the Most Powerful Pre-trained Language Model without Brute Force Fine-tuning? An Empirical Survey (2023.findings-emnlp)

Challenge:	Large reasoning models exhibit human-like behaviors such as exploration, verification, reflection, and correction.
Approach:	They propose a supervised fine-tuning framework for long chain-of-thoughts reasoning . they leverage a difficulty-aware reward model to estimate the learning value of questions .
Outcome:	The proposed framework performs fine-tuning on large reasoning models on 10% of the data selected.

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Jun Bai, Xiaofeng Zhang, Chen Li, Hanhua Hong, Xi Xu, Chenghua Lin, Wenge Rong

PiKGL: Leveraging Pruned Knowledge Graphs for Explainable Stance Detection (2026.tacl-1)

Challenge:	Transferability estimation has been a topic of great interest in computer vision fields . a lack of a comprehensive comparison between these estimation methods is a problem .
Approach:	They conduct a thorough survey of existing methods to find the most suitable model . they also outline difficulties of consideration of training details and applicability to text generation .
Outcome:	The proposed methods perform well with superiorities in effectiveness and efficiency.

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Bingbing Wang, Jingjie Lin, Zhixin Bai, Xintong Song, Qianlong Wang, Min Yang, Xi Zeng, Jing Li, Ruifeng Xu

EduBench: A Comprehensive Benchmarking Dataset for Evaluating Large Language Models in Diverse Educational Scenarios (2026.acl-long)

Challenge:	Experimental results demonstrate that a Pruned interpretable knowledge Graph Learning framework for explainable stance detection is state-of-the-art for social media stance prediction.
Approach:	They propose a Pruned interpretable knowledge Graph Learning framework for explainable stance detection that incorporates commonsense knowledge and prunes redundant information to ensure precision and minimize noise.
Outcome:	The proposed framework achieves state-of-the-art on three public datasets.

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Bin Xu, Yu Bai, Huashan Sun, Yiguan Lin, Siming Liu, Xinyue Liang, Yaolin Li, Zhuangzhi Dong, Jingren Zhang, Yufan Deng, Xinyu Zou, Yang Gao, Heyan Huang

Introducing Semantics into Speech Encoders (2023.acl-long)

Challenge:	Existing benchmarks that focus on knowledge-intensive tasks do not reflect diverse educational scenarios.
Approach:	They propose a benchmark that incorporates 9 major scenarios and 4,000 educational contexts.
Outcome:	The proposed model performs comparable to state-of-the-art large models on the test set.

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Derek Xu, Shuyan Dong, Changhan Wang, Suyoun Kim, Zhaojiang Lin, Bing Liu, Akshat Shrivastava, Shang-Wen Li, Liang-Hsuan Tseng, Guan-Ting Lin, Alexei Baevski, Hung-yi Lee, Yizhou Sun, Wei Wang

MapGPT: Map-Guided Prompting with Adaptive Path Planning for Vision-and-Language Navigation (2024.acl-long)

Challenge:	Existing self-supervised speech encoders contain primarily acoustic rather than semantic information.
Approach:	They propose a task-agnostic unsupervised way to incorporate semantic information from large language model (LLM) systems into self-supervised speech encoders without labeled audio transcriptions.
Outcome:	The proposed approach improves spoken language understanding (SLU) performance by over 5% on intent classification (IC), with modest gains in named entity resolution (NER) and slot filling (SF), and spoken question answering (SQA) score by over 22%.

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Jiaqi Chen, Bingqian Lin, Ran Xu, Zhenhua Chai, Xiaodan Liang, Kwan-Yee Wong

Construction of a Chinese Corpus for the Analysis of the Emotionality of Metaphorical Expressions (P18-2)

Challenge:	Embodied agents equipped with GPT as their brains have extraordinary decision-making and generalization abilities across various tasks.
Approach:	They propose a map-based agent that introduces an online linguistic-formed map to encourage global exploration.
Outcome:	The proposed agent achieves state-of-the-art zero-shot performance on R2R and REVERIE simultaneously.

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Dongyu Zhang, Hongfei Lin, Liang Yang, Shaowu Zhang, Bo Xu

DimABSA: Building Multilingual and Multidomain Datasets for Dimensional Aspect-Based Sentiment Analysis (2026.acl-long)

Challenge:	a corpus of 5,605 manually annotated sentences in Chinese is described . emotion is an abstract and vague conception, which is often described by metaphor .
Approach:	They propose to construct a corpus of metaphors annotated with emotion in Chinese . they use an annotation scheme to include linguistic metaphors, emotional categories and intensity .
Outcome:	The proposed corpus contains 5,605 manually annotated sentences in Chinese . the authors show that the corpus is large enough to analyze emotions .

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Lung-Hao Lee, Liang-Chih Yu, Natalia V Loukachevitch, Ilseyar Alimova, Alexander Panchenko, Tzu-Mi Lin, Zhe-Yu Xu, Jian-Yu Zhou, Guangmin Zheng, Jin Wang, Sharanya Awasthi, Jonas Becker, Jan Philip Wahle, Terry Ruas, Shamsuddeen Hassan Muhammad, Saif M. Mohammad

MAVEN-ERE: A Unified Large-scale Dataset for Event Coreference, Temporal, Causal, and Subevent Relation Extraction (2022.emnlp-main)

Challenge:	Existing ABSA research relies on coarse-grained categorical labels, which limits its ability to capture nuanced affective states.
Approach:	They propose a dimensional approach that represents sentiment with continuous valence–arousal (VA) scores, enabling fine-grained analysis at both the aspect and sentiment levels.
Outcome:	The proposed approach represents sentiment with continuous valence–arousal (VA) scores, enabling fine-grained analysis at both the aspect and sentiment levels.

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Xiaozhi Wang, Yulin Chen, Ning Ding, Hao Peng, Zimu Wang, Yankai Lin, Xu Han, Lei Hou, Juanzi Li, Zhiyuan Liu, Peng Li, Jie Zhou

ICL-Bandit: Relevance Labeling in Advertisement Recommendation Systems via LLM (2025.findings-emnlp)

Challenge:	Existing datasets only cover limited relation types at once, which prevents models from taking full advantage of relation interactions.
Approach:	They construct a large-scale human-annotated ERE dataset with improved annotation schemes to address these drawbacks.
Outcome:	The proposed dataset is larger than existing datasets of all the ERE tasks by at least an order of magnitude.

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Lu Wang, Chiming Duan, Pu Zhao, Fangkai Yang, Yong Shi, Xuefeng Luo, Bingjing Xu, Weiwei Deng, Qingwei Lin, Dongmei Zhang

Global Encoding for Abstractive Summarization (P18-2)

Challenge:	In-context learning (ICL) is a common practice to enhance LLM performance on domain-specific tasks.
Approach:	They propose a method that leverages large language models to enhance query-ad relevance labeling . they identify and provide superior demonstrations for ICL, thereby improving labeling performance .
Outcome:	The proposed method improves query-ad relevance labeling performance by providing demonstrations.

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Junyang Lin, Xu Sun, Shuming Ma, Qi Su

Concept rather than Document: Context Compression via AMR-based Conceptual Entropy (2026.findings-acl)

Challenge:	Existing models for abstractive summarization suffer from repetition and semantic irrelevance.
Approach:	They propose a global encoding framework which controls the information flow from the encoder to the decoder based on the global information of the source context.
Outcome:	The proposed model outperforms baseline models on the LCSTS and English Gigaword and can generate summary of higher quality and reduce repetition.

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Kaize Shi, Xueyao Sun, Xiaohui Tao, Lin Li, Qika Lin, Guandong Xu

CATCH: A Novel Data Synthesis Framework for High Therapy Fidelity and Memory-Driven Planning Chain of Thought in AI Counseling (2025.findings-emnlp)

Challenge:	Existing methods rely on lexical or token-level features that fragment semantic units and fail to capture conceptually essential content.
Approach:	They propose an unsupervised framework leveraging Abstract Meaning Representation to preserve essential information while filtering irrelevant text.
Outcome:	The proposed framework outperforms RAG and existing baselines while preserving essential information while filtering irrelevant text.

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Mingyu Chen, Jingkai Lin, Zhaojie Chu, Xiaofen Xing, Yirong Chen, Xiangmin Xu

Training Language Models to Critique With Multi-agent Feedback (2025.findings-emnlp)

Challenge:	Existing studies employ a one-time generation approach to synthesize multi-turn dialogue samples, resulting in low therapy fidelity and failing to capture decision-making rationale behind each response.
Approach:	They propose a data synthesis framework that synthesizes multi-turn dialogue samples and incrementally generates stage-aligned counseling dialogues.
Outcome:	The proposed framework significantly improves therapy fidelity and logical coherence in AI counseling.

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Tian Lan, Wenwei Zhang, Chengqi Lyu, Shuaibin Li, Chen Xu, Heyan Huang, Dahua Lin, Xian-Ling Mao, Kai Chen

Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction (2021.acl-long)

Challenge:	utilizing human annotations can enhance critique ability, but model-generated critiques suffer from inherent flaws due to complexity of critique . a new framework that leverages multi-agent feedback improves critique ability .
Approach:	They propose a framework that leverages multi-agent feedback to improve critique ability . they propose to use supervised fine-tuning and reinforcement learning to improve this capability .
Outcome:	The proposed framework improves critique ability in both supervised fine-tuning and reinforcement learning stages.

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Yaojie Lu, Hongyu Lin, Jin Xu, Xianpei Han, Jialong Tang, Annan Li, Le Sun, Meng Liao, Shaoyi Chen

Open Relation Extraction: Relational Knowledge Transfer from Supervised Data to Unsupervised Data (D19-1)

Challenge:	Existing methods to extract event records from text decompose complex structure prediction task into multiple subtasks.
Approach:	They propose a sequence-to-structure generation paradigm that can extract events from text . they propose unified event extraction, constrained decoding algorithm and curriculum learning algorithm .
Outcome:	The proposed method can achieve competitive performance using record-level annotations in both supervised learning and transfer learning settings.

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Ruidong Wu, Yuan Yao, Xu Han, Ruobing Xie, Zhiyuan Liu, Fen Lin, Leyu Lin, Maosong Sun

Premise-based Multimodal Reasoning: Conditional Inference on Joint Textual and Visual Clues (2022.acl-long)

Challenge:	Existing methods to extract relational facts from open domain corpora are time-consuming and human-intensive.
Approach:	They propose a framework to learn similarity metrics of relations from labeled data . they propose to transfer relational knowledge to identify novel relations in unlabeled data.
Outcome:	Experiments on two real-world datasets show that the proposed framework improves compared with state-of-the-art methods.

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Qingxiu Dong, Ziwei Qin, Heming Xia, Tian Feng, Shoujie Tong, Haoran Meng, Lin Xu, Zhongyu Wei, Weidong Zhan, Baobao Chang, Sujian Li, Tianyu Liu, Zhifang Sui

AntCritic: Argument Mining for Free-Form and Visually-Rich Financial Comments (2024.lrec-main)

Challenge:	Existing work in vision language cross-modal reasoning uses binary or multi-choice classification based on source image and textual query.
Approach:	They propose a task where a textual premise is the background presumption on each source image.
Outcome:	The proposed task is based on a dataset of 15,360 movie screenshots and human-curated premise templates from 6 pre-defined categories.

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Huadai Liu, Xu Wenqiang, Xuan Lin, Jingjing Huo, Hong Chen, Zhou Zhao

Visual Data in NLP

Challenge:	Argument mining is a thriving task in natural language processing, but its generalization is limited by existing datasets.
Approach:	They propose to use a dataset to help model argument mining . the dataset AntCritic supports both argument component detection and argument relation prediction tasks.
Outcome:	The proposed model can detect arguments and identify their relationships automatically.

Extending LLM Context Window with Adaptive Grouped Positional Encoding: A Training-Free Method (2025.acl-long)

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Xinhao Xu, Jiaxin Li, Hui Chen, Zijia Lin, Jungong Han, Guiguang Ding

Knowledge-Centric Hallucination Detection (2024.emnlp-main)

Challenge:	Existing long-context training data is scarce and requires substantial GPU resources for training.
Approach:	They propose a training-free plug-and-play method to enhance long-context understanding in existing large language models.
Outcome:	The proposed method outperforms existing LLMs on various tasks and surpasses baseline methods.

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Xiangkun Hu, Dongyu Ru, Lin Qiu, Qipeng Guo, Tianhang Zhang, Yang Xu, Yun Luo, Pengfei Liu, Yue Zhang, Zheng Zhang

Not All Directions Matter: Towards Structured and Task-Aware Low-Rank Model Adaptation (2026.acl-long)

Challenge:	Large Language Models (LLMs) have shown impressive capabilities but a tendency to hallucinate.
Approach:	They propose a framework that introduces claim-triplets to represent claims in LLM responses and evaluates them against a reference.
Outcome:	The proposed framework outperforms prior methods by 18.2 to 27.2 points on a benchmark spanning various NLP tasks and annotated 11k claim-triplets from 2.1k responses by seven LLMs.

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Xi Xiao, Chenrui Ma, Yunbei Zhang, Chen Liu, Zhuxuanzi Wang, Yanshu Li, Lin Zhao, Guosheng Hu, Tianyang Wang, Hao Xu

DIAG-NRE: A Neural Pattern Diagnosis Framework for Distantly Supervised Neural Relation Extraction (P19-1)

Challenge:	Low-Rank Adaptation (LoRA) is a key parameter-efficient fine-tuning method . however, its effectiveness is hampered by semantic drift and structural incoherence .
Approach:	They propose a low-rank Adaptation framework that tackles semantic drift and structural incoherence by pruning task-irrelevant directions.
Outcome:	Experiments on large language models, vision models, and vision models show that the proposed framework outperforms LoRA and advanced dynamic rank allocation and sparsity-based methods.

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Shun Zheng, Xu Han, Yankai Lin, Peilin Yu, Lu Chen, Ling Huang, Zhiyuan Liu, Wei Xu

Breakthrough from Nuance and Inconsistency: Enhancing Multimodal Sarcasm Detection with Context-Aware Self-Attention Fusion and Word Weight Calculation. (2024.lrec-main)

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.

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Hongfei Xue, Linyan Xu, Yu Tong, Rui Li, Jiali Lin, Dazhi Jiang

Does Acceleration Cause Hidden Instability in Vision Language Models? Uncovering Instance-Level Divergence Through a Large-Scale Empirical Study (2025.emnlp-main)

Challenge:	Existing methods for sarcasm detection rely on feature concatenation to fuse different modalities or model inconsistencies among modalités.
Approach:	They propose to use Context-Aware Self-Attention Fusion to integrate local and momentary multimodal information into specific words to illustrate the inconsistencies between connotation and denotation.
Outcome:	The proposed method achieves an accuracy of 76.9 and an F1 score of 76.1 on the MUStARD dataset, surpassing the current state-of-the-art IWAN model by 1.7 and 1.6 respectively.

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Yizheng Sun, Hao Li, Chang Xu, Hongpeng Zhou, Chenghua Lin, Riza Batista-Navarro, Jingyuan Sun

Plug-and-Play Knowledge Injection for Pre-trained Language Models (2023.acl-long)

Challenge:	Current acceleration evaluations focus on minimal overall performance degradation . however, accelerated models can exhibit significant changes in instance-level predictions .
Approach:	They investigate whether accelerated vision-Language Models can still give the same answers as before . they found that accelerated models changed original answers up to 20% of the time .
Outcome:	The results show that accelerated models changed their original answers up to 20% of the time.

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Zhengyan Zhang, Zhiyuan Zeng, Yankai Lin, Huadong Wang, Deming Ye, Chaojun Xiao, Xu Han, Zhiyuan Liu, Peng Li, Maosong Sun, Jie Zhou

PACE: Predictive Adaptive Context Extraction for Long-Horizon LLM Agents (2026.acl-long)

Challenge:	Existing knowledge injection methods are not suitable for enhancing pre-trained language models with external knowledge bases.
Approach:	They propose a plug-and-play knowledge injection method where knowledge bases are injected into frozen existing downstream models by a knowledge plugin.
Outcome:	The proposed method improves the performance of knowledge injection on knowledge-driven tasks while keeping model parameters frozen.

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Lei Wei, Xiao Peng, null Tt, Guannan Zhang, Chenhao Jiang, Hongyu Li, Lanbo Lin, Yuanwu Xu, Jiayao Liu, Kesu Wang, Bin Wang

Towards Tool Use Alignment of Large Language Models (2024.emnlp-main)

Challenge:	Large Language Model (LLM) agents struggle with ultra-long-horizon tasks requiring hundreds or thousands of interaction steps.
Approach:	They propose a framework that reconceptualizes context management as a Next Step Prediction problem.
Outcome:	The proposed framework improves task success rates and robust cross-lingual performance.

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Zhi-Yuan Chen, Shiqi Shen, Guangyao Shen, Gong Zhi, Xu Chen, Yankai Lin

Embedding Dynamic Attributed Networks by Modeling the Evolution Processes (2020.coling-main)

Challenge:	Existing studies on tool use with LLMs focus on enhancing tool-calling ability of LLM . e.g., LLM should not answer unsafe tool use relevant instructions or insecure tool responses to ensure reliability and harmlessness.
Approach:	They propose to use supervised fine-tuning and preference learning to align LLMs with H2A principle for tool use.
Outcome:	The proposed model demonstrates that LLMs can generate truthful and helpful responses while remaining harmless.

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Zenan Xu, Zijing Ou, Qinliang Su, Jianxing Yu, Xiaojun Quan, ZhenKun Lin

Representation Learning

Challenge:	Existing methods to embed nodes into low-dimensional vectors focus on static networks, but in practice, many networks are evolving over time and hence are dynamic, e.g., social networks.
Approach:	They propose to extract high-order neighborhood information at each given timestamp and then use an embedding prediction framework to capture the temporal correlations.
Outcome:	Extensive experiments on four real-world datasets show that the proposed method outperforms baseline methods for dynamic link prediction and node classification tasks.

Token-wise Influential Training Data Retrieval for Large Language Models (2024.acl-long)

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Huawei Lin, Jikai Long, Zhaozhuo Xu, Weijie Zhao

UNIKIE-BENCH: Benchmarking Large Multimodal Models for Key Information Extraction in Visual Documents (2026.acl-long)

Challenge:	Large language models (LLMs) have been widely used in various industries due to their unprecedented scale and impressive capabilities derived from the massive training dataset.
Approach:	They propose a framework that can estimate the influence of training data by caching and retrieval.
Outcome:	The proposed framework can estimate the influence of training data within minutes, achieving over a speedup of 6,326x.

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Yifan Ji, Zhipeng Xu, Zhenghao Liu, Zulong Chen, Qian Zhang, Zhibo Yang, Junyang Lin, Yu Gu, Ge Yu, Maosong Sun

Specificity-Driven Cascading Approach for Unsupervised Sentiment Modification (D19-1)

Challenge:	Recent Large Multimodal Models (LMMs) have shown promising potential for performing end-to-end KIE directly from document images.
Approach:	They propose a benchmark to evaluate the performance of Large Multimodal Models (LMMs) using a constrained-category KIE track and an open-categorical KIE Track.
Outcome:	Experiments on 15 state-of-the-art LMMs show performance degradation under diverse schema definitions, long-tail key fields, and complex layouts, along with pronounced performance disparities across different document types and scenarios.

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Pengcheng Yang, Junyang Lin, Jingjing Xu, Jun Xie, Qi Su, Xu Sun

RAP: Robustness-Aware Perturbations for Defending against Backdoor Attacks on NLP Models (2021.emnlp-main)

Challenge:	Existing methods for unsupervised sentiment modification lack specific information in text generated without parallel data . specificity-driven cascading approach can improve specificity of generated text and content preservation .
Approach:	They propose a specificity-driven cascading approach for unsupervised sentiment modification . the method performs target sentiment addition and content reconstruction independently .
Outcome:	The proposed method outperforms competitive systems by a large margin on Yelp and Amazon datasets.

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Wenkai Yang, Yankai Lin, Peng Li, Jie Zhou, Xu Sun

Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation (2024.findings-acl)

Challenge:	Backdoor attacks are a serious threat to the safety of reusing deep neural networks (DNNs).
Approach:	They propose an efficient online defense mechanism based on robustness-aware perturbations to distinguish poisoned and clean samples to defend against backdoor attacks on natural language processing models.
Outcome:	The proposed method achieves better defending performance and lower computational costs than existing defense methods.

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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.

Knowledge-Augmented Methods for Natural Language Processing (2022.acl-tutorials)

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Chenguang Zhu, Yichong Xu, Xiang Ren, Bill Yuchen Lin, Meng Jiang, Wenhao Yu

MTAVG-Bench: A Diagnostic Benchmark for Multi-Talker Dialogue-Centric Audio-Video Generation (2026.acl-long)

Challenge:	Knowledge in natural language processing (NLP) is a rising trend especially after the advent of large scale pre-trained models.
Approach:	This tutorial introduces the key steps in integrating knowledge into natural language processing (NLP) it introduces knowledge grounding from text, knowledge representation and fusing.
Outcome:	This tutorial introduces the key steps in integrating knowledge into natural language processing including knowledge grounding from text, knowledge representation and fusing.

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Yanghao Zhou, Haitian Li, Rexar Lin, Heyan Huang, Jinxing Zhou, Changsen Yuan, Tian Lan, Ziqin Zhou, Yudong Li, Jiajun Xu, Jingyun Liao, YiMing Cheng, Xuefeng Chen, Xian-Ling Mao, Yousheng Feng

Boosting Inference Efficiency: Unleashing the Power of Parameter-Shared Pre-trained Language Models (2023.findings-emnlp)

Challenge:	Existing evaluation benchmarks for text-to-audio-video (T2AV) generation are largely designed for human-recorded videos or single-speaker settings.
Approach:	They propose a failure-driven diagnostic benchmark for multi-talker dialogue-centric audio-video generation.
Outcome:	The benchmark evaluates multi-speaker dialogue generation at four levels: audio-visual signal fidelity, temporal attribute consistency, social interaction, and cinematic expression.

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Weize Chen, Xiaoyue Xu, Xu Han, Yankai Lin, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou

ShortGPT: Layers in Large Language Models are More Redundant Than You Expect (2025.findings-acl)

Challenge:	Parameter-shared pre-trained language models (PLMs) have emerged as a successful approach in resource-constrained environments.
Approach:	They propose a method to enhance the inference efficiency of parameter-shared PLMs by pre-training models that can achieve even greater acceleration.
Outcome:	The proposed method improves inference efficiency on autoregressive and autoencoding models.

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Xin Men, Mingyu Xu, Qingyu Zhang, Qianhao Yuan, Bingning Wang, Hongyu Lin, Yaojie Lu, Xianpei Han, Weipeng Chen

Towards Effective and Efficient Continual Pre-training of Large Language Models (2025.acl-long)

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.

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Jie Chen, Zhipeng Chen, Jiapeng Wang, Kun Zhou, Yutao Zhu, Jinhao Jiang, Yingqian Min, Xin Zhao, Zhicheng Dou, Jiaxin Mao, Yankai Lin, Ruihua Song, Jun Xu, Xu Chen, Rui Yan, Zhewei Wei, Di Hu, Wenbing Huang, Ji-Rong Wen

OpenResearcher: Unleashing AI for Accelerated Scientific Research (2024.emnlp-demo)

Challenge:	Continual pre-training (CPT) has been an important approach for adapting language models to specific domains or tasks.
Approach:	They propose a Continual pre-training method that can greatly improve Chinese language ability and scientific reasoning ability of LLMs.
Outcome:	The proposed method can greatly improve Chinese language ability and scientific reasoning ability of LLMs.

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Yuxiang Zheng, Shichao Sun, Lin Qiu, Dongyu Ru, Cheng Jiayang, Xuefeng Li, Jifan Lin, Binjie Wang, Yun Luo, Renjie Pan, Yang Xu, Qingkai Min, Zizhao Zhang, Yiwen Wang, Wenjie Li, Pengfei Liu

Symbol-LLM: Towards Foundational Symbol-centric Interface For Large Language Models (2024.acl-long)

Challenge:	Global scientific publications are growing annually by about 4%-5% (Pinedo et al., 2024).
Approach:	They introduce an AI-assisted platform that answers diverse questions from researchers using Retrieval-Augmented Generation (RAG) they develop various tools to understand queries, search from the scientific literature, filter retrieved information, provide accurate and comprehensive answers, and self-refine answers.
Outcome:	OpenResearcher is built on Retrieval-Augmented Generation (RAG) to integrate Large Language Models (LLMs) with up-to-date, domain-specific knowledge.

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Fangzhi Xu, Zhiyong Wu, Qiushi Sun, Siyu Ren, Fei Yuan, Shuai Yuan, Qika Lin, Yu Qiao, Jun Liu

Annotating Chinese Word Senses with English WordNet: A Practice on OntoNotes Chinese Sense Inventories (2024.lrec-main)

Challenge:	Large Language Models (LLMs) have limitations when it comes to comprehending and expressing world knowledge that extends beyond the boundaries of natural language.
Approach:	They propose a model that integrates symbolic data into LLM training without loss of generality ability.
Outcome:	The proposed model performs better on symbol- and NL-centric tasks.

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Hongzhi Xu, Jingxia Lin, Sameer Pradhan, Mitchell Marcus, Ming Liu

Challenge:	a recent study has shown that large language models can be useful for cross-lingual applications.
Approach:	They propose to annotate Chinese word senses using English WordNet synsets . they examine the relationship between two annotators and find patterns among synset .
Outcome:	The proposed method shows that the annotators agree on 38% of the synsets compared with the original synset . the results highlight similarities between the synnotated synset and the WordNet structure .

Beyond Code Pairs: Dialogue-Based Data Generation for LLM Code Translation (2026.acl-long)

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Le Chen, Nuo Xu, Winson Chen, Bin Lei, Pei-Hung Lin, Dunzhi Zhou, Rajeev Thakur, Caiwen Ding, Ali Jannesari, Chunhua Liao

RAVR: Reference-Answer-guided Variational Reasoning for Large Language Models (2026.findings-acl)

Challenge:	Large language models (LLMs) have shown remarkable capabilities in code translation, yet their performance deteriorates in low-resource programming domains such as Fortran and emerging frameworks like CUDA .
Approach:	They propose a dual-LLM Questioner–Solver pipeline that integrates external knowledge from compilers and runtime feedback to generate verified translations and multi-turn dialogues.
Outcome:	The proposed model outperforms proprietary models on key metrics like compilation success and accuracy.

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Tianqianjin Lin, Xi Zhao, Xingyao Zhang, Rujiao Long, Yi Xu, Zhuoren Jiang, Wenbo Su, Bo Zheng

V-VAE: A Variational Auto Encoding Framework Towards Fine-Grained Control over Human-Like Chat (2025.emnlp-main)

Challenge:	Experiments show that reinforcement learning (RL) can refine the reasoning abilities of large language models (LLMs) but requires a key prerequisite: the model must already be able to generate high-utility reasoning paths with non-negligible probability.
Approach:	They propose a framework that uses answer-conditioned reasoning as a variational surrogate for question-only reasoning.
Outcome:	Experiments on 11 benchmarks and 3 models show that RAVR reduces hesitation, strengthens conclusion consolidation, and promotes problem-specific strategies in reasoning.

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Qi Lin, Weikai Xu, Lisi Chen, Bin Dai

Think before Go: Hierarchical Reasoning for Image-goal Navigation (2026.acl-long)

Challenge:	Existing role-play and persona-based chat approaches rely on static role descriptions, coarse-grained signal space, and low-quality synthetic data.
Approach:	They propose a Verbal Variational Auto-Encoding framework which dynamically adapts dialogue behaviour based on latent variables across talking style, interaction patterns, and personal attributes.
Outcome:	The proposed framework outperforms baselines on HumanChatBench and DialogBench to address the scarcity of high-quality data in the human-like domain.

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Pengna Li, Kangyi Wu, Shaoqing Xu, Fang Li, Lin Zhao, Long Chen, Zhi-Xin Yang, Nanning Zheng

Interpretable User Satisfaction Estimation for Conversational Systems with Large Language Models (2024.acl-long)

Challenge:	Existing methods for image-goal navigation fail to extract informative visual cues, leading agents to wander around.
Approach:	They propose a framework that decomposes image-goal navigation into high-level planning and low-level execution.
Outcome:	The proposed method is superior to existing methods in both simulation and real-world environments.

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Ying-Chun Lin, Jennifer Neville, Jack Stokes, Longqi Yang, Tara Safavi, Mengting Wan, Scott Counts, Siddharth Suri, Reid Andersen, Xiaofeng Xu, Deepak Gupta, Sujay Kumar Jauhar, Xia Song, Georg Buscher, Saurabh Tiwary, Brent Hecht, Jaime Teevan

HAD: HAllucination Detection Language Models Based on a Comprehensive Hallucination Taxonomy (2026.acl-industry)

Challenge:	Existing approaches to user satisfaction estimation are hard to interpret and lack generalizable patterns.
Approach:	They propose to use supervised prompting to extract interpretable user satisfaction signals from natural language utterances to tailor an LLM to USE using labeled examples.
Outcome:	The proposed method extracts interpretable signals of user satisfaction from natural language utterances more effectively than embedding-based approaches.

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Fan Xu, Xinyu Hu, Zhenghan Yu, Li Lin, Xu Zhang, Yang Zhang, Wei Zhou, Jinjie Gu, Xiaojun Wan

Meta-Transfer Learning for Code-Switched Speech Recognition (2020.acl-main)

Challenge:	relying on large language models for information has raised concerns about reliability and accuracy of outputs.
Approach:	They propose a hallucination taxonomy with 11 categories for various NLG tasks and propose HAllucination Detection models which integrate hallucinism detection, span-level identification, and correction into a single inference process.
Outcome:	The proposed models outperform baselines on HaluEval, FactCHD, and FaithBench, confirming their robustness and versatility.

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Genta Indra Winata, Samuel Cahyawijaya, Zhaojiang Lin, Zihan Liu, Peng Xu, Pascale Fung

AutoAlign: Get Your LLM Aligned with Minimal Annotations (2025.acl-demo)

Challenge:	Increasing number of people in the world today speak a mixed-language as a result of being multilingual.
Approach:	They propose a method to transfer learn on a code-switched speech recognition system by extracting information from high-resource monolingual datasets.
Outcome:	The proposed model outperforms baselines on speech recognition and language modeling tasks and is faster to converge.

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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

Stronger Models are Not Always Stronger Teachers for Instruction Tuning (2025.naacl-long)

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.

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Zhangchen Xu, Fengqing Jiang, Luyao Niu, Bill Yuchen Lin, Radha Poovendran

Vulnerability of Text-to-Image Models to Prompt Template Stealing: A Differential Evolution Approach (2025.findings-acl)

Challenge:	Existing methods to optimize instruction-following capabilities of large language models (LLMs) assume that larger or stronger models are stronger teachers and therefore adopt smaller models as response generators.
Approach:	They propose to use large-scale instruction datasets to tune large language models to align with specific tasks and user intents.
Outcome:	The proposed metric outperforms most baselines in identifying the effectiveness of response generators.

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Yurong Wu, Fangwen Mu, Qiuhong Zhang, Jinjing Zhao, Xinrun Xu, Lingrui Mei, Yang Wu, Lin Shi, Junjie Wang, Zhiming Ding, Yiwei Wang

WebCPM: Interactive Web Search for Chinese Long-form Question Answering (2023.acl-long)

Challenge:	Prompt trading has emerged as a significant intellectual property concern in recent years, where vendors entice users by showcasing sample images before selling prompt templates that can generate similar images.
Approach:	They propose a prompt-stealing benchmark consisting of 50 templates and 450 images organized into Easy and Hard difficulty levels.
Outcome:	The proposed method outperforms baseline methods with an average improvement of over 10%.

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Yujia Qin, Zihan Cai, Dian Jin, Lan Yan, Shihao Liang, Kunlun Zhu, Yankai Lin, Xu Han, Ning Ding, Huadong Wang, Ruobing Xie, Fanchao Qi, Zhiyuan Liu, Maosong Sun, Jie Zhou

Fine-grained Artificial Neurons in Audio-transformers for Disentangling Neural Auditory Encoding (2023.findings-acl)

Challenge:	Long-form question answering requires two procedures: information retrieval and information synthesis.
Approach:	They propose a Chinese long-form question answering dataset called WebCPM . the dataset is based on a web search interface that engages with a search engine in real time .
Outcome:	The proposed dataset generates answers that are no worse than human-written ones . the dataset is the first Chinese LFQA dataset .

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Mengyue Zhou, Xu Liu, David Liu, Zihao Wu, Zhengliang Liu, Lin Zhao, Dajiang Zhu, Lei Guo, Junwei Han, Tianming Liu, Xintao Hu

FanChuan: A Multilingual and Graph-Structured Benchmark For Parody Detection and Analysis (2025.findings-acl)

Challenge:	Existing studies treat each transformer encoding layer as a single artificial neuron . layer-level embeddings aggregate multiple types of contextual attention captured by multiple head modules .
Approach:	They propose to embed each transformer encoding layer as a single artificial neuron . they propose to couple those ANs with their biological-neuron counterparts in the human brain .
Outcome:	The proposed models can be used to link representations to brain activity, the authors say . their results show that the proposed models carry meaningful neurolinguistic information .

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Yilun Zheng, Sha Li, Fangkun Wu, Yang Ziyi, Lin Hongchao, Zhichao Hu, Cai Xinjun, Ziming Wang, Jinxuan Chen, Sitao Luan, Jiahao Xu, Lihui Chen

SafeDecoding: Defending against Jailbreak Attacks via Safety-Aware Decoding (2024.acl-long)

Challenge:	Parody is an emerging phenomenon on social media, where individuals imitate a role or position opposite to their own . limited available data and deficient diversity in current datasets hinder study of parody .
Approach:	They build a dataset of parody users and annotated comments from both English and Chinese corpora to test parody detection and comment sentiment analysis.
Outcome:	The proposed datasets provide richer contextual information, which is lacking in existing datasets.

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Zhangchen Xu, Fengqing Jiang, Luyao Niu, Jinyuan Jia, Bill Yuchen Lin, Radha Poovendran

Targeted Distillation for Sentiment Analysis (2025.emnlp-main)

Challenge:	Despite advances in large language models, they face substantial challenges in terms of safety.
Approach:	They develop a safety-aware decoding strategy for large language models to defend against jailbreak attacks.
Outcome:	The proposed strategy outperforms six defense methods against jailbreak attacks on five LLMs.

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Yice Zhang, Guangyu Xie, Jingjie Lin, Jianzhu Bao, Qianlong Wang, Xi Zeng, Ruifeng Xu

Effective Synthetic Data and Test-Time Adaptation for OCR Correction (2024.emnlp-main)

Challenge:	Recent studies demonstrate that large language models exhibit remarkable capabilities and achieve state-of-the-art performance in diverse sentiment analysis tasks.
Approach:	They propose a distillation framework that decouples knowledge from alignment and introduces a sentiment analysis benchmark that covers a diverse set of tasks.
Outcome:	The proposed framework improves models' generalization to unseen tasks and their generalization is strong against existing small-scale models.

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Shuhao Guan, Cheng Xu, Moule Lin, Derek Greene

Towards Understanding Jailbreak Attacks in LLMs: A Representation Space Analysis (2024.emnlp-main)

Challenge:	Recent research has framed the post-OCR task as a Seq2Seq Neural Machine Translation (NMT) task.
Approach:	They propose a method for constructing post-OCR synthetic data with different noise levels using weak supervision.
Outcome:	The proposed method reduces CER by 68.67% without relying on manual annotations.

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Yuping Lin, Pengfei He, Han Xu, Yue Xing, Makoto Yamada, Hui Liu, Jiliang Tang

MAgIC: Investigation of Large Language Model Powered Multi-Agent in Cognition, Adaptability, Rationality and Collaboration (2024.emnlp-main)

Challenge:	Large language models (LLMs) are susceptible to a type of attack known as jailbreaking, which misleads LLMs to output harmful contents.
Approach:	They propose to leverage hidden representations into existing jailbreak targets to move the attacks along the acceptance direction.
Outcome:	The proposed methods are validated using the objective of existing jailbreak attacks.

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Lin Xu, Zhiyuan Hu, Daquan Zhou, Hongyu Ren, Zhen Dong, Kurt Keutzer, See-Kiong Ng, Jiashi Feng

ViT-TTS: Visual Text-to-Speech with Scalable Diffusion Transformer (2023.emnlp-main)

Challenge:	Large language models (LLMs) have advanced natural language processing, demonstrating exceptional reasoning, tool usage, and memory capabilities.
Approach:	They propose a competition-based benchmark framework specifically designed to assess LLMs within multi-agent environments.
Outcome:	The proposed framework enhances the LLMs’ abilities in navigating complex social and cognitive dimensions by over threefold between the strongest and weakest LLM models.

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Huadai Liu, Rongjie Huang, Xuan Lin, Wenqiang Xu, Maozong Zheng, Hong Chen, Jinzheng He, Zhou Zhao

Semantic-Unit-Based Dilated Convolution for Multi-Label Text Classification (D18-1)

Challenge:	Text-to-speech (TTS) performance has improved with the advent of denoising Diffusion Probabilistic Models . however, perceived quality of audio depends on content, pitch, rhythm, and energy .
Approach:	They propose a visual TTS model with scalable diffusion transformers that complement phoneme sequences with visual information to generate high-perceived audio.
Outcome:	The proposed model outperforms existing models regardless of visibility of the scene . it can generate high-perceived audio, opening up new avenues for AR and VR applications .

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Junyang Lin, Qi Su, Pengcheng Yang, Shuming Ma, Xu Sun

Manual Evaluation Matters: Reviewing Test Protocols of Distantly Supervised Relation Extraction (2021.findings-acl)

Challenge:	a novel model for multi-label text classification is proposed for the task of assigning multiple labels for a given text.
Approach:	They propose a novel model for multi-label text classification based on sequence-to-sequence learning and a hybrid attention mechanism that extracts both the word-level and the semantic unit.
Outcome:	The proposed model is competitive to the baseline models and more robust to classifying low-frequency labels.

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Tianyu Gao, Xu Han, Yuzhuo Bai, Keyue Qiu, Zhiyu Xie, Yankai Lin, Zhiyuan Liu, Peng Li, Maosong Sun, Jie Zhou

ExtRA: Extracting Prominent Review Aspects from Customer Feedback (D18-1)

Challenge:	Distantly supervised relation extraction (RE) has attracted much attention in the past few years . previous methods to evaluate models manually or directly on autolabeled data have produced inaccurate evaluations .
Approach:	They propose to use distant supervision to generate large-scale autolabeled data . they build manually-annotated test sets for two DS-RE datasets and evaluate models .
Outcome:	The proposed method produces 53% wrong labels at the entity pair level in the popular NYT10 dataset.

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Zhiyi Luo, Shanshan Huang, Frank F. Xu, Bill Yuchen Lin, Hanyuan Shi, Kenny Zhu

PathReasoner: Modeling Reasoning Path with Equivalent Extension for Logical Question Answering (2024.acl-long)

Challenge:	Existing methods for analyzing and summarizing customer reviews are based on a number of prominent review aspects.
Approach:	They propose a framework for extracting the most prominent aspects of a given product type from textual reviews.
Outcome:	The proposed framework extracts K most prominent aspect terms which do not overlap semantically without supervision.

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Fangzhi Xu, Qika Lin, Tianzhe Zhao, JiaweiHan JiaweiHan, Jun Liu

Bag-of-Words as Target for Neural Machine Translation (P18-2)

Challenge:	Existing logical reasoning tasks are challenging, especially for large language models.
Approach:	They propose a logic reasoning task model that transforms each logical sample into reasoning paths and propose an atom extension strategy supported by equivalent logical formulas to form new reasoning paths.
Outcome:	The proposed architecture achieves competitive performances on two logical reasoning benchmarks and great generalization abilities.

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Shuming Ma, Xu Sun, Yizhong Wang, Junyang Lin

Neural Topic Model with Reinforcement Learning (D19-1)

Challenge:	Existing neural machine translation models only use one correct sentence as the target, and the other correct sentences are punished as the incorrect ones.
Approach:	They propose an approach that uses both the sentences and the bag-of-words as targets in the training stage to encourage the model to generate the potentially correct sentences that are not appeared in the train set.
Outcome:	The proposed model outperforms baseline models on a Chinese-English translation dataset by the BLEU score of 4.55.

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Lin Gui, Jia Leng, Gabriele Pergola, Yu Zhou, Ruifeng Xu, Yulan He

Cultural Bias Matters: A Cross-Cultural Benchmark Dataset and Sentiment-Enriched Model for Understanding Multimodal Metaphors (2025.acl-long)

Challenge:	Experimental results show superior performance on perplexity and topic coherence measures compared to state-of-the-art topic models.
Approach:	They propose to incorporate topic coherence measures as reward signals to guide the learning of a VAE-based topic model.
Outcome:	The proposed model is able to separating background words dynamically from topic words eliminating the pre-processing step of filtering infrequent and/or top frequent words, typically required for learning traditional topic models.

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Senqi Yang, Dongyu Zhang, Jing Ren, Ziqi Xu, Xiuzhen Zhang, Yiliao Song, Hongfei Lin, Feng Xia

Web Fraud Attacks Against LLM-Driven Multi-Agent Systems (2026.findings-acl)

Challenge:	Metaphors are pervasive in communication, making them crucial for natural language processing.
Approach:	They propose a multicultural multimodal metaphor dataset designed for cross-cultural studies of metaphor in Chinese and English.
Outcome:	The proposed model improves metaphor comprehension across cultural backgrounds and cultural domains.

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Dezhang Kong, Hujin Peng, Yilun Zhang, Lele Zhao, Zhenhua Xu, Shi Lin, Changting Lin, Meng Han

To Code or not to Code? Adaptive Tool Integration for Math Language Models via Expectation-Maximization (2025.findings-acl)

Challenge:	Large Language Model (LLM)-driven multi-agent systems (MAS) are rapidly gaining popularity, and its inherent security risks are rapidly becoming a concern.
Approach:	They propose a novel attack manipulating unique structures of web links to deceive MAS by using homoglyph deception, sub-directory nesting, and parameter obfuscation.
Outcome:	The proposed attacks exploit unique structures of web links to deceive MAS . they exhibit significant destructive potential across different MAS architectures .

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Haozhe Wang, Long Li, Chao Qu, Weidi Xu, Fengming Zhu, Wei Chu, Fangzhen Lin

Unveiling Opinion Evolution via Prompting and Diffusion for Short Video Fake News Detection (2024.findings-acl)

Challenge:	Existing tools that integrate chain-of-thought reasoning and code execution lack metacognitive awareness to integrate tools.
Approach:	They propose a framework that synergizes structured exploration with off-policy RL optimization to create a cycle between metacognitive tool-use decisions and evolving capabilities.
Outcome:	The proposed framework improves over 11% on MATH500 and 9.4% on AIME without o1-like CoT.

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Linlin Zong, Jiahui Zhou, Wenmin Lin, Xinyue Liu, Xianchao Zhang, Bo Xu

A Law Reasoning Benchmark for LLM with Tree-Organized Structures including Factum Probandum, Evidence and Experiences (2025.findings-acl)

Challenge:	Existing methods for short video fake news detection ignore the implicit opinions and evolving nature of opinions across modalities.
Approach:	They propose a short video fake news model that mines implicit opinions within short videos and promotes the evolution of both explicit and implicit opinions across all modalities.
Outcome:	The proposed model outperforms existing methods on a publicly available dataset for short video fake news detection.

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Jiaxin Shen, Jinan Xu, Huiqi Hu, Luyi Lin, Guoyang Ma, Fei Zheng, Fandong Meng, Jie Zhou, Wenjuan Han

Holistic Evaluation for Interleaved Text-and-Image Generation (2024.emnlp-main)

Challenge:	a recent study focuses on generating impartial and interpretable judicial judgments based on established criminal fact.
Approach:	They propose a law reasoning schema enriched with hierarchical factum probandum, evidence, and implicit experience that enables public scrutiny and preventing bias.
Outcome:	The proposed schema enables public scrutiny and prevents bias in the "Intelligent Court" it employs a suite of legal analysis tools to address the challenge task.

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Minqian Liu, Zhiyang Xu, Zihao Lin, Trevor Ashby, Joy Rimchala, Jiaxin Zhang, Lifu Huang

Rethinking Stealthiness of Backdoor Attack against NLP Models (2021.acl-long)

Challenge:	Existing evaluation benchmarks do not support arbitrarily interleaved images and text for both inputs and outputs.
Approach:	They propose to use a benchmark to evaluate interleaved text-and-image generation . they define five evaluation aspects for InterleavatedEval, a reference-free metric .
Outcome:	The proposed benchmarks cover a limited number of domains and use cases and lack comparableity-based metrics.

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Wenkai Yang, Yankai Lin, Peng Li, Jie Zhou, Xu Sun

UniCorn: Towards Self-Improving Unified Multimodal Models through Self-Generated Supervision (2026.acl-long)

Challenge:	Existing backdoor attacks are not stealthy to system deployers or users.
Approach:	They propose a novel backdoor attack method based on negative data augmentation and modifying word embeddings that is much stealthier while maintaining pretty good attacking performance.
Outcome:	The proposed method is much stealthier while maintaining pretty good attacking performance.

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Zhen Fang, Ruiyan Han, XinYu Sun, Yuchen Ma, Ziheng Wang, Yu Zeng, Zehui Chen, Lin Chen, Wenxuan Huang, Wei-Jie Xu, Yi Cao, Feng Zhao

Fully Hyperbolic Neural Networks (2022.acl-long)

Challenge:	Unified Multimodal Models have achieved remarkable success in cross-modal comprehension, but a gap persists in their ability to translate internal knowledge into faithful and controllable synthesis.
Approach:	They propose a self-improvement framework that partitions a single UMM into three collaborative roles: Proposer, Solver, and Judge.
Outcome:	The proposed framework improves on TIIF, DPG, CompBench and UniCycle benchmarks.

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Weize Chen, Xu Han, Yankai Lin, Hexu Zhao, Zhiyuan Liu, Peng Li, Maosong Sun, Jie Zhou

Challenge:	Existing hyperbolic neural networks encode features in the hyperbolical space yet formalize most of their operations in the tangent space.
Approach:	They propose a fully hyperbolic framework to build hyperbolical networks based on the Lorentz model by adapting Lorentzer transformations to formalize essential operations of neural networks.
Outcome:	The proposed framework has better performance on four NLP tasks compared with existing hyperbolic models .

A Deep Reinforced Sequence-to-Set Model for Multi-Label Classification (P19-1)

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Pengcheng Yang, Fuli Luo, Shuming Ma, Junyang Lin, Xu Sun

Text Classification

Challenge:	Multi-label classification (MLC) aims to assign multiple labels to each sample.
Approach:	They propose a sequence-to-set model that is trained via reinforcement learning and rewards feedback independent of the label order.
Outcome:	The proposed model outperforms baseline models and reduces sensitivity to label order.

ALinFiK: Learning to Approximate Linearized Future Influence Kernel for Scalable Third-Parity LLM Data Valuation (2025.naacl-long)

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Yanzhou Pan, Huawei Lin, Yide Ran, Jiamin Chen, Xiaodong Yu, Weijie Zhao, Denghui Zhang, Zhaozhuo Xu

A Probabilistic Inference Scaling Theory for LLM Self-Correction (2025.emnlp-main)

Challenge:	Large Language Models (LLMs) heavily rely on high-quality training data, making data valuation crucial for optimizing model performance.
Approach:	They propose a third-party data valuation approach that assesses the value of individual data samples and proposes a learning strategy to approximate LinFiK.
Outcome:	The proposed approach surpasses baselines in effectiveness and efficiency, showing significant scalability advantages as LLM parameters increase.

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Zhe Yang, Yichang Zhang, Yudong Wang, Ziyao Xu, Junyang Lin, Zhifang Sui

Linguistic Theories

Challenge:	Large Language Models (LLMs) have demonstrated the capability to refine their generated answers through self-correction, enabling continuous performance improvement over multiple rounds.
Approach:	They propose a probabilistic theory to model the dynamics of accuracy change and explain performance improvements observed in multi-round self-correction.
Outcome:	The proposed model can predict accuracy curves and improve accuracy over multiple rounds.

Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability (2025.findings-acl)

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Chiwei Zhu, Benfeng Xu, An Yang, Junyang Lin, Quan Wang, Chang Zhou, Zhendong Mao

Ranking and Sampling in Open-Domain Question Answering (D19-1)

Challenge:	Existing studies have shown that training language models with rationales augmentation is beneficial, but this view does not hold consistently.
Approach:	They conduct comprehensive investigations to thoroughly inspect the impact of rationales on model performance and a novel perspective of model reliability.
Outcome:	The proposed method outperforms untrained models in several areas and provides informative regulations on the broad utilization of rationales.

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Yanfu Xu, Zheng Lin, Yuanxin Liu, Rui Liu, Weiping Wang, Dan Meng

Temporal Sampling for Forgotten Reasoning in LLMs (2026.acl-long)

Challenge:	Existing approaches focus on positive paragraphs which contain the answer during training, making it disturbed by similar but irrelevant paragraphs during testing.
Approach:	They propose a ranking model leveraging the paragraph-question and the paragraph relevance to compute a confidence score for each paragraph.
Outcome:	Experiments on three datasets show that the proposed model advances the state of the art.

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

Variator: Accelerating Pre-trained Models with Plug-and-Play Compression Modules (2023.findings-emnlp)

Challenge:	a new metric measures the percentage of questions that were answered incorrectly during fine-tuning .
Approach:	They propose a decoding strategy that draws outputs from multiple checkpoints along the training trajectory.
Outcome:	The proposed method improves reasoning performance and consistency across benchmarks.

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Chaojun Xiao, Yuqi Luo, Wenbin Zhang, Pengle Zhang, Xu Han, Yankai Lin, Zhengyan Zhang, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou

QaRL: Rollout-Aligned Quantization-Aware RL for Fast and Stable Training under Training–Inference Mismatch (2026.findings-acl)

Challenge:	Large language models (LLMs) have been successful on NLP tasks but require huge parameter sizes and computational resources.
Approach:	They propose a parameter-efficient acceleration method that enhances computational efficiency through plug-and-play compression plugins.
Outcome:	The proposed method saves 53% computational costs using only 0.9% additional parameters with a performance drop of less than 2%.

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Hao Gu, Hao Wang, Jiacheng Liu, Lujun Li, Qiyuan Zhu, Bei Liu, Binxing Xu, Lei Wang, Xintong Yang, Sida Lin, Sirui Han, Yike Guo

Challenge:	Recent work has shown that reinforcement learning with simple rule-based reward functions (RLVR) can induce emergent reasoning behaviors and yield gains in challenging domains such as math problem solving.
Approach:	They propose a rollout-alignment-quantization-aware RL which aligns training-side forward with the quantized rollout to minimize mismatch.
Outcome:	The proposed approach outperforms quantized-rollout training by +5.5 on Qwen3-30B-A3B MoE for math problems while maintaining low-bit throughput.

SafeChain: Safety of Language Models with Long Chain-of-Thought Reasoning Capabilities (2025.findings-acl)

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Fengqing Jiang, Zhangchen Xu, Yuetai Li, Luyao Niu, Zhen Xiang, Bo Li, Bill Yuchen Lin, Radha Poovendran

Denoising Relation Extraction from Document-level Distant Supervision (2020.emnlp-main)

Challenge:	Emerging large reasoning models (LRMs) leverage long chain-of-thought (CoT) reasoning to enhance their reasoning capabilities.
Approach:	They conduct a systematic study of LRM safety using human annotations to assess their safety.
Outcome:	The proposed safety measures are compared to state-of-the-art models on strong and wildjailbreak datasets.

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Chaojun Xiao, Yuan Yao, Ruobing Xie, Xu Han, Zhiyuan Liu, Maosong Sun, Fen Lin, Leyu Lin

Evaluating Character Understanding of Large Language Models via Character Profiling from Fictional Works (2024.emnlp-main)

Challenge:	Existing methods to generate auto-labeled sentences for relation extraction (RE) are difficult to extend to document-level relation extraction as noise from DS may be even multiplied in documents.
Approach:	They propose a pre-trained model which de-emphasizes noisy DS data via multiple pre-training tasks.
Outcome:	The proposed model can capture useful information from noisy data and achieve promising results on the large-scale DocRE benchmark.

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Xinfeng Yuan, Siyu Yuan, Yuhan Cui, Tianhe Lin, Xintao Wang, Rui Xu, Jiangjie Chen, Deqing Yang

COF: Adaptive Chain of Feedback for Comparative Opinion Quintuple Extraction (2025.coling-main)

Challenge:	Recent advances in large language models (LLMs) have catalyzed numerous AI applications, among which role-playing agents (RPAs) are particularly popular.
Approach:	They propose to evaluate LLMs' character understanding capability via the character profiling task, i.e., summarizing character profiles from corresponding materials, a widely adopted yet understudied practice for RPA development.
Outcome:	The proposed model outperforms existing models and literature summarization methods and proves its ability to understand fictional characters in downstream tasks.

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Qingting Xu, Kaisong Song, Chaoqun Liu, Yangyang Kang, Xiabing Zhou, Jun Lin, Yu Hong

Low-Resource NLP

Challenge:	Comparative Opinion Quintuple Extraction (COQE) aims to extract all comparative sentiment quintuples from product review text.
Approach:	They propose a model-unaware adaptive chain-of-feedback method to extract quintuples from product review text.
Outcome:	The proposed method improves performance on three benchmarks.

A Knowledge Regularized Hierarchical Approach for Emotion Cause Analysis (D19-1)

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Chuang Fan, Hongyu Yan, Jiachen Du, Lin Gui, Lidong Bing, Min Yang, Ruifeng Xu, Ruibin Mao

Semantic Contribution-Aware Adaptive Retrieval for Black-Box Models (2025.findings-emnlp)

Challenge:	Emotion cause analysis aims to identify the reasons behind emotions . previous models focus on learning architecture with local textual information .
Approach:	They propose a method to extract emotion cause with hierarchical neural model and knowledge-based regularizations by sentiment lexicon and common knowledge.
Outcome:	The proposed method outperforms baselines on two public datasets in different languages and outperformed competitive baselines by 2.08%.

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Qinhong Lin, Zhongliang Yang, Yuang Cai, Dingfu Yu, Xuan Xu, Yu Li, Linna Zhou

Diversity-Promoting GAN: A Cross-Entropy Based Generative Adversarial Network for Diversified Text Generation (D18-1)

Challenge:	Existing approaches to retrieval-agmented generation fail to generalize effectively in black-box scenarios.
Approach:	They propose a framework that leverages the semantic importance of words to dynamically adjust retrieval thresholds and filter information.
Outcome:	The proposed framework achieves the highest score on four long-form, knowledge-intensive generation datasets.

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Jingjing Xu, Xuancheng Ren, Junyang Lin, Xu Sun

MultiDx: A Multi-Source Knowledge Integration Framework towards Diagnostic Reasoning (2026.findings-acl)

Challenge:	Existing text generation methods tend to produce repeated and ”boring” expressions.
Approach:	They propose a model that assigns low reward for repeatedly generated text and high reward for ”novel” and fluent text, and a novel language-model based discriminator which can distinguish novel text from repeated text without the saturation problem.
Outcome:	The proposed model generates more diverse and informative text than existing baselines on review generation and dialogue generation tasks.

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Yimin Deng, Zhenxi Lin, Yejing Wang, Guoshuai Zhao, Pengyue Jia, Zichuan Fu, Derong Xu, Yefeng Zheng, Xiangyu Zhao, Li Zhu, Xian Wu, Xueming Qian

Data-to-Text Generation with Style Imitation (2020.findings-emnlp)

Challenge:	Existing approaches focus on diagnostic reasoning based on internal model knowledge or static knowledge bases.
Approach:	They propose a two-stage diagnostic reasoning framework that integrates multi-perspective evidence to generate a diagnostic prediction.
Outcome:	The proposed method generates suspected diagnoses and reasoning traces from web search, SOAP-formatted case, and clinical case database.

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Shuai Lin, Wentao Wang, Zichao Yang, Xiaodan Liang, Frank F. Xu, Eric Xing, Zhiting Hu

Combating Label Sparsity in Short Text Topic Modeling via Nearest Neighbor Augmentation (2024.findings-acl)

Challenge:	Recent approaches to data-to-text generation focus on improving content fidelity, but lack explicit control over writing styles.
Approach:	They propose a way to control writing styles by using existing sentences as "soft" templates . they conduct experiments in restaurants and sports domains to test their approach .
Outcome:	The proposed approach achieves stronger performance than a range of comparison methods.

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Yang Lin, Xinyu Ma, Xin Gao, Ruiqing Li, Yasha Wang, Xu Chu

AlpacaTag: An Active Learning-based Crowd Annotation Framework for Sequence Tagging (P19-3)

Challenge:	Existing topic models suffer from poor performance when applied to short text contents due to the limited length of a single topic.
Approach:	They propose a neural short text topic model that augments reconstruction labels with k-nearest documents to complement relevant but unobserved words.
Outcome:	The proposed model outperforms the state-of-the-art models on multiple public short-text datasets and can derive high-quality topics and document representations.

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Bill Yuchen Lin, Dong-Ho Lee, Frank F. Xu, Ouyu Lan, Xiang Ren

Tagging

Challenge:	Existing sequence annotation tools focus on improving user interfaces and user interface.
Approach:	They propose an open-source web-based data annotation framework for sequence tagging tasks . the framework is based on active learning and automatic crowd consolidation .
Outcome:	The proposed framework is a comprehensive solution for sequence labeling tasks . it can be deployed in downstream systems while new annotations are being made .

R2I-Bench: Benchmarking Reasoning-Driven Text-to-Image Generation (2025.emnlp-main)

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Kaijie Chen, Zihao Lin, Zhiyang Xu, Ying Shen, Yuguang Yao, Joy Rimchala, Jiaxin Zhang, Lifu Huang

Flexibly Utilize Memory for Long-Term Conversation via a Fragment-then-Compose Framework (2025.emnlp-main)

Challenge:	Reasoning is a fundamental capability underpinning text-to-image (T2I) generation.
Approach:	They propose a benchmark to rigorously assess reasoning-driven T2I generation.
Outcome:	Experiments with 16 representative T2I models show limited reasoning performance . a strong pipeline-based framework decouples reasoning and generation .

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Cai Ke, Yiming Du, Bin Liang, Yifan Xiang, Lin Gui, Zhongyang Li, Baojun Wang, Yue Yu, Hui Wang, Kam-Fai Wong, Ruifeng Xu

From Mimicking to Integrating: Knowledge Integration for Pre-Trained Language Models (2022.findings-emnlp)

Challenge:	Large language models extract useful information from conversation history to enhance the response in long-term conversations.
Approach:	They propose a Fragment-then-Compose framework to optimize memory utilization for long-term open-domain conversation.
Outcome:	The proposed framework can be used to extract useful information from conversation history . it can be adapted to different situations and improve response generation .

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Lei Li, Yankai Lin, Xuancheng Ren, Guangxiang Zhao, Peng Li, Jie Zhou, Xu Sun

MRE-MI: A Multi-image Dataset for Multimodal Relation Extraction in Social Media Posts (2025.findings-naacl)

Challenge:	Existing models for natural language processing (NLP) are fine-tuned and released for research and deployments.
Approach:	They propose a PLM reuse paradigm that merges teacher-PLM knowledge into a student model.
Outcome:	The proposed paradigm can reduce the computational cost and environmental side-effects of retraining the PLM from scratch.

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Shizhou Huang, Bo Xu, Changqun Li, Yang Yu, Xin Alex Lin

Locality Preserving Sentence Encoding (2021.findings-emnlp)

Challenge:	Existing approaches to Multimodal Relation Extraction focus on single image scenarios . current approaches focus on text paired with a single image, ignoring valuable insights provided by remaining images.
Approach:	They propose a human-annotated dataset that includes multi-image and single-image instances for relation extraction.
Outcome:	The proposed model significantly improves relation extraction in multi-image scenarios.

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Changrong Min, Yonghe Chu, Liang Yang, Bo Xu, Hongfei Lin

Mitigating Matthew Effect: Multi-Hypergraph Boosted Multi-Interest Self-Supervised Learning for Conversational Recommendation (2024.emnlp-main)

Challenge:	Existing word embeddings can be used to learn sentence embedds on the sentence level.
Approach:	They propose a sentence embedding method that uses the inner product to compute semantic similarity between sentences.
Outcome:	The proposed method encodes sentences better in the sense of semantic structures.

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Yongsen Zheng, Ruilin Xu, Guohua Wang, Liang Lin, Kwok-Yan Lam

From Discourse to Narrative: Knowledge Projection for Event Relation Extraction (2021.acl-long)

Challenge:	Existing methods to mitigate Matthew effect in offline recommendation systems are not effective . a number of studies have identified two root causes for the Matthew effect .
Approach:	They propose a framework to address the Matthew effect in conversational recommendation systems . they build hypergraphs to learn multi-level user interests to alleviate the Matthew effec .
Outcome:	The proposed framework achieves state-of-the-art performance on four CRS-based datasets . it improves on item-, entity-, word-oriented multiple-channel hypergraphs compared with existing methods .

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Jialong Tang, Hongyu Lin, Meng Liao, Yaojie Lu, Xianpei Han, Le Sun, Weijian Xie, Jin Xu

Mask the Correct Tokens: An Embarrassingly Simple Approach for Error Correction (2022.emnlp-main)

Challenge:	Existing event-centric knowledge graphs rely on explicit connectives to extract relations between events.
Approach:	They propose a knowledge projection paradigm for event relation extraction using commonalities between events.
Outcome:	The proposed method achieves state-of-the-art performance and extrinsic results verify the extracted event relations.

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Kai Shen, Yichong Leng, Xu Tan, Siliang Tang, Yuan Zhang, Wenjie Liu, Edward Lin

Decoding Scientific Experimental Images: The SPUR Benchmark for Perception, Understanding, and Reasoning (2026.acl-long)

Challenge:	Text error correction methods usually use the source (incorrect) sentence as encoder input and generate the target (correct) sentences through the decoder.
Approach:	They propose a method to correct errors in text sequences by randomly masking out the correct tokens in the source sentence.
Outcome:	The proposed method improves accuracy on Mandarin and English datasets with autoregressive and non-autoregressive generation models.

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Junpeng Ding, Zichen Tang, Haihong E, Mengyuan Ji, Yang Liu, Haolin Tian, Haiyang Sun, Pengqi Sun, Yang Xu, Yichen Liu, Haocheng Gao, Zijie Xi, Ruomeng Jiang, Peizhi Zhao, Rongjin Li, Yuanze Li, Jiacheng Liu, Zhongjun Yang, Jintong Chen, Siying Lin

IBSEN: Director-Actor Agent Collaboration for Controllable and Interactive Drama Script Generation (2024.acl-long)

Challenge:	Xu and Peng, 2025) . . SPUR is a comprehensive benchmark for scientific experimental image perception, understanding, and reasoning, comprising 4,264 question-answering (QA) pairs derived from 1,084 expert-curated images.
Approach:	They propose to use 4,264 question-answering (QA) pairs derived from 1,084 expert-curated images to evaluate the visual perception of multimodal large language models (MLLMs) . they also propose to utilize cross-panel relation understanding to evaluate MLLM’s ability to decipher intricate cross-panel relations.
Outcome:	The proposed model is based on 4,264 question-answering pairs derived from 1,084 expert-curated images.

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Senyu Han, Lu Chen, Li-Min Lin, Zhengshan Xu, Kai Yu

UEGP: Unified Expert-Guided Pre-training for Knowledge Rekindle (2024.findings-naacl)

Challenge:	Language models have demonstrated their capabilities in storyline creation and human-like character role-playing.
Approach:	They propose a director-actor coordinate agent framework that generates drama scripts . framework allows actors to role-play their characters while maintaining plot development .
Outcome:	The proposed framework generates drama scripts from a drama plot outline and human actors can play their characters.

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Yutao Mou, Kexiang Wang, Jianhe Lin, Dehong Ma, Jun Fan, Daiting Shi, Zhicong Cheng, Gu Simiu, Dawei Yin, Weiran Xu

Modeling Intra- and Inter-Modal Relations: Hierarchical Graph Contrastive Learning for Multimodal Sentiment Analysis (2022.coling-1)

Challenge:	Existing paradigms for pre-training and fine-tuning have limitations . knowledge rekindle aims to break through performance upper bounds of experts without introducing additional annotated data.
Approach:	They propose a new paradigm for pre-training and fine-tuning that aims to re-incorporate the fine- tuned expert model into the training cycle and break through performance upper bounds of experts.
Outcome:	The proposed model breaks through performance upper bounds of experts without additional annotated data.

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Zijie Lin, Bin Liang, Yunfei Long, Yixue Dang, Min Yang, Min Zhang, Ruifeng Xu

Take Its Essence, Discard Its Dross! Debiasing for Toxic Language Detection via Counterfactual Causal Effect (2024.lrec-main)

Challenge:	Existing studies in Multimodal Sentiment Analysis lack a mechanism to understand complex relations between different modalities.
Approach:	They propose a hierarchical graph contrastive learning framework for multimodal sentiment analysis that explores the relationships between modality representations.
Outcome:	The proposed framework outperforms the state-of-the-art in multimodal sentiment analysis on two benchmark datasets.

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Junyu Lu, Bo Xu, Xiaokun Zhang, Kaiyuan Liu, Dongyu Zhang, Liang Yang, Hongfei Lin

Cheems: A Practical Guidance for Building and Evaluating Chinese Reward Models from Scratch (2025.acl-long)

Challenge:	Existing methods to mitigate lexical bias in toxic language detection (TLD) do not exploit the “useful” and “misleading” impact of the bias.
Approach:	They propose a counterfactual Causal Debiasing Framework to mitigate lexical bias in toxic language detection (TLD) it preserves the “useful impact” of lexical bias and eliminates the "misleading impact" they propose to use the same framework to analyze the causal effect of a sentence and bias tokens .
Outcome:	The proposed framework preserves the “useful impact” of lexical bias and eliminates the ‘misleading impact’ Empirical evaluations show that the proposed model outperforms current debiased models for out-of-distribution data.

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Xueru Wen, Jie Lou, Zichao Li, Yaojie Lu, XingYu XingYu, Yuqiu Ji, Guohai Xu, Hongyu Lin, Ben He, Xianpei Han, Le Sun, Debing Zhang

Challenge:	Existing Chinese resources are small in scale and limited to specific domains, making them insufficient for LLM post-training.
Approach:	They propose a Chinese-annotated reward model and a preference dataset to address this gap . they evaluate Chinese RMs on CheemsBench and construct an RM that captures human preferences .
Outcome:	The proposed RM achieves state-of-the-art performance on CheemsBench and CheeMePreference.

SymbolicThought: Integrating Language Models and Symbolic Reasoning for Consistent and Interpretable Human Relationship Understanding (2026.acl-demo)

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Runcong Zhao, Qinglin Zhu, Hainiu Xu, Bin Liang, Lin Gui, Yulan He

Chain of Methodologies: Scaling Test Time Computation without Training (2025.findings-acl)

Challenge:	Existing methods for constructing character relationships from plain text are time-consuming and low in coverage.
Approach:	They propose a human-in-the-loop framework that combines LLM-based extraction with symbolic reasoning.
Outcome:	The proposed framework improves annotation accuracy and consistency while significantly reducing time cost.

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Cong Liu, Jie Wu, Weigang Wu, Xu Chen, Liang Lin, Wei-Shi Zheng

Low-Resource NLP

Challenge:	Existing prompts for complex reasoning tasks are limited to specific tasks with few-shot examples due to constraints like context length and information extraction accuracy.
Approach:	They propose a method to build structured reasoning processes by injecting human insights into LLMs' training data.
Outcome:	The proposed framework outperforms baselines in the analysis of large language models.

ProMedTS: A Self-Supervised, Prompt-Guided Multimodal Approach for Integrating Medical Text and Time Series (2025.findings-acl)

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Shuai Niu, Jing Ma, Hongzhan Lin, Liang Bai, Zhihua Wang, V. W., Richard Yi Da Xu, Guo Li, Xian Yang

Aligning Cross-Lingual Entities with Multi-Aspect Information (D19-1)

Challenge:	Large language models excel at processing unstructured data, but integrating time series data with text remains a challenge.
Approach:	They propose a self-supervised multimodal framework that uses prompt-guided learning to unify heterogeneous data types.
Outcome:	The proposed framework outperforms state-of-the-art approaches on disease diagnosis tasks using real-world datasets.

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Hsiu-Wei Yang, Yanyan Zou, Peng Shi, Wei Lu, Jimmy Lin, Xu Sun

Enhancing Unsupervised Semantic Parsing with Distributed Contextual Representations (2023.findings-acl)

Challenge:	Existing knowledge graphs that represent entities in different languages are not covered by existing systems.
Approach:	They propose two ways to embed entities from multilingual knowledge graphs into the same vector space, where equivalent entities are close to each other.
Outcome:	The proposed method significantly outperforms existing systems on two benchmark datasets.

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Zixuan Ling, Xiaoqing Zheng, Jianhan Xu, Jinshu Lin, Kai-Wei Chang, Cho-Jui Hsieh, Xuanjing Huang

MAPLE: Enhancing Review Generation with Multi-Aspect Prompt LEarning in Explainable Recommendation (2025.acl-long)

Challenge:	Existing methods to learn models on corpus of pairs of sentences require labor-intensive annotation.
Approach:	They propose to leverage distributed contextual word and phrase representations pre-trained on unlabelled texts to deal with homonymy and polysemy.
Outcome:	The proposed model achieves better accuracy on question-answering and relation extraction tasks.

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Ching-Wen Yang, Zhi-Quan Feng, Ying-Jia Lin, Che Wei Chen, Kun-da Wu, Hao Xu, Yao Jui-Feng, Hung-Yu Kao

NiuTrans.LMT: Toward Inclusive and Scalable Multilingual Machine Translation with LLMs (2026.acl-long)

Challenge:	Existing models that generate generic aspects do not provide personalized informative recommendations.
Approach:	They propose a model that integrates aspect category as another input dimension to facilitate memorizing fine-grained aspect terms.
Outcome:	The proposed model outperforms baseline model on restaurant review datasets in the restaurant domain.

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Yingfeng Luo, Ziqiang Xu, Yuxuan Ouyang, MuRun Yang, DingYang Lin, Kaiyan Chang, Tong Zheng, Bei Li, Peinan Feng, Quan Du, Tong Xiao, JingBo Zhu

Learning to Correct Noisy Labels for Fine-Grained Entity Typing via Co-Prediction Prompt Tuning (2023.findings-emnlp)

Challenge:	Large language models have significantly advanced Multilingual Machine Translation (MMT) yet scaling to many languages while maintaining robust performance across directions remains challenging.
Approach:	They propose a strategy to reduce the number of translations in one direction . they propose auxiliary parallel sentences to promote cross-lingual transfer .
Outcome:	The proposed model performs on par with or better than substantially larger baselines.

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Minghao Tang, Yongquan He, Yongxiu Xu, Hongbo Xu, Wenyuan Zhang, Yang Lin

Continual Relation Learning via Episodic Memory Activation and Reconsolidation (2020.acl-main)

Challenge:	Experimental results show that noise correction in fine-grained entity typing improves quality of training samples.
Approach:	They propose a method that leverages multiple prediction results to correct noisy labels . they integrate prediction results and utilize a differentiated margin to identify inaccurate labels a .
Outcome:	The proposed model improves quality of training samples annotated using distant supervision, ChatGPT, and crowdsourcing.

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Xu Han, Yi Dai, Tianyu Gao, Yankai Lin, Zhiyuan Liu, Peng Li, Maosong Sun, Jie Zhou

Challenge:	Existing methods to learn incessantly emerging novel relations are overfitting the few memorized examples of old relations, causing confusion among existing relations.
Approach:	They introduce episodic memory activation and reconsolidation (EMAR) to continual relation learning.
Outcome:	The proposed method outperforms state-of-the-art models in catastrophic forgetting old relations.

InSerter: Speech Instruction Following with Unsupervised Interleaved Pre-training (2025.acl-long)

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Dingdong Wang, Jin Xu, Ruihang Chu, Zhifang Guo, Xiong Wang, Jincenzi Wu, Dongchao Yang, Shengpeng Ji, Junyang Lin

Automatic Marketing Theme and Commodity Construction System for E-commerce (2023.emnlp-industry)

Challenge:	Recent advances in speech large language models exhibit suboptimal performance in adhering to speech instructions.
Approach:	They propose a method to pre-train large-scale unsupervised speech-text sequences . they use text-to-speech conversion to generate textual continuations corresponding to provided speech segments .
Outcome:	The proposed model achieves superior or competitive results across diverse speech processing tasks.

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Zhiping Wang, Peng Lin, Hainan Zhang, Hongshen Chen, Tianhao Li, Zhuoye Ding, Sulong Xu, Jinghe Hu

Matching Article Pairs with Graphical Decomposition and Convolutions (P19-1)

Challenge:	Existing recommendation system invites experts to write marketing themes and select relevant commodities, which suffer from difficulty in mass production, poor timeliness and low online indicators.
Approach:	They propose to use pretrained language model to generate marketing themes and commodity consistency module to select relevant commodities for the generative theme.
Outcome:	The proposed system can generate popular marketing themes and select relevant commodities automatically and improve theme online effectiveness compared with state-of-the-art methods.

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Bang Liu, Di Niu, Haojie Wei, Jinghong Lin, Yancheng He, Kunfeng Lai, Yu Xu

Structured Data in NLP

Challenge:	Existing methods for matching sentence pairs do not perform well in longer documents . Existing approaches for matching sentences do not work in longer document understanding tasks .
Approach:	They propose to model article pairs by comparing sentences that enclose same concept vertex . they propose to use a concept interaction graph to match articles by encoding sentences .
Outcome:	The proposed methods show significant improvements over existing methods . the proposed datasets consist of 30K pairs of breaking news articles .

MNER-MI: A Multi-image Dataset for Multimodal Named Entity Recognition in Social Media (2024.lrec-main)

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Shizhou Huang, Bo Xu, Changqun Li, Jiabo Ye, Xin Lin

Taming the Real-world Complexities in CPT E/M Coding with Large Language Models (2025.emnlp-industry)

Challenge:	Recent research has focused on multimodal named entity recognition (MNER) but current approaches focus on text and a single accompanying image, leaving a significant research gap in multi-image scenarios.
Approach:	They propose to construct a human-annotated MNER dataset with multiple images called MNER-MI and a temporal prompt model with multiple image to address the new challenges in multi-image scenarios.
Outcome:	The proposed method achieves state-of-the-art results on both MNER-MI and MNER -MI-Plus, demonstrating its effectiveness.

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Islam Nassar, Yang Lin, Yuan Jin, Rongxin Zhu, Chang Wei Tan, Zenan Zhai, Nitika Mathur, Thanh Tien Vu, Xu Zhong, Long Duong, Yuan-Fang Li

PAM: Enhancing General Alignment of Large Reasoning Models through Priority-Aware Metacognition (2026.acl-long)

Challenge:	Evaluation and Management (E/M) coding is performed by physicians and trained human coders who review clinical encounter notes and electronic health record data to assign appropriate codes.
Approach:	They propose a framework that automates evaluation and management coding tasks using the Current Procedural Terminology (CPT) taxonomy.
Outcome:	The proposed framework achieves an increase in coding accuracy of more than 36% over a commercial CPT E/M coding system and almost 5% over our strongest single-prompt baseline.

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Zhihao Xu, Fuzhen Yang, Liang Lin, Xiting Wang

LaoBench: A Large-Scale Multidimensional Lao Benchmark for Large Language Models (2026.acl-long)

Challenge:	Existing studies indicate that System-2 thinking alone does not transfer to the general alignment domain.
Approach:	They propose to use priority-aware metacognition to help LRMs understand human preferences and monitor and regulate their thinking process.
Outcome:	The proposed model improves general alignment performance by 10 points on helpfulness and harmless benchmarks.

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Jian Gao, Richeng Xuan, Zhaolu Kang, Dingshi Liao, Wenxin Huang, Zongmou Huang, Yangdi Xu, Bowen Qin, Zheqi He, Xi Yang, null Changjinli, Yonghua Lin

Towards Unified Task Embeddings Across Multiple Models: Bridging the Gap for Prompt-Based Large Language Models and Beyond (2024.findings-acl)

Challenge:	Existing SEA-focused benchmarks miss Lao-specific cultural grounding and linguistic properties.
Approach:	They propose a multi-dimensional benchmark for assessing large language models in Lao . they use open-source and held-out subsets to evaluate languages with a hybrid pipeline .
Outcome:	LaoBench is the first large-scale, high-quality, and multidimensional benchmark for assessing LLM language understanding and reasoning in Lao.

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Xinyu Wang, Hainiu Xu, Lin Gui, Yulan He

HSS-Synth: Humanities and Social Sciences Data Synthesis for LLMs (2026.findings-acl)

Challenge:	Existing task embedding methods rely on fine-tuned, task-specific language models, which hinders their adaptability to prompt-guided Large Language Models (LLMs).
Approach:	They propose a framework for unified task embedding that harmonizes task embeds from various models within a single vector space.
Outcome:	The proposed framework harmonizes task embeddings from various models within a single vector space.

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Ru Peng, Tianyu Zhao, Xijun Gu, Zhiting Fan, Haokai Xu, Jinyang Zhang, Yawen Zeng, Yihong Zhuang, Kexin Yang, Junyang Lin, Dayiheng Liu, Junbo Zhao

Challenge:	High-quality, diverse data are vital for large language models (LLMs) but remain scarce and costly.
Approach:	They define the first HSS domain system covering 14 mainstream fields and introduce HSS-Synth.
Outcome:	the proposed pipeline outperforms 14 leading baselines on 16 benchmarks.

OpenKE: An Open Toolkit for Knowledge Embedding (D18-2)

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Xu Han, Shulin Cao, Xin Lv, Yankai Lin, Zhiyuan Liu, Maosong Sun, Juanzi Li

GlotEval: A Test Suite for Massively Multilingual Evaluation of Large Language Models (2025.emnlp-demos)

Challenge:	Existing knowledge embedding tools are available for embeddable knowledge graphs.
Approach:	They propose a unified framework and various fundamental models to embed knowledge graphs into a continuous low-dimensional space.
Outcome:	The toolkit and pre-trained embeddings are available on http://openke.thunlp.org/.

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Hengyu Luo, Zihao Li, Joseph Attieh, Sawal Devkota, Ona de Gibert, Xu Huang, Shaoxiong Ji, Peiqin Lin, Bhavani Sai Praneeth Varma Mantina, Ananda Sreenidhi, Raúl Vázquez, Mengjie Wang, Samea Yusofi, Fei Yuan, Jörg Tiedemann

Autoencoder as Assistant Supervisor: Improving Text Representation for Chinese Social Media Text Summarization (P18-2)

Challenge:	Existing evaluation frameworks focus on English and a handful of high-resource languages, thereby overlooking the realistic performance of large language models in multilingual and lower-resourced scenarios.
Approach:	They propose a unified and lightweight framework that integrates 27 benchmarks under a standard ISO 639-3 language identifier system to enable seamless incorporation of new benchmarks.
Outcome:	The proposed framework integrates 27 benchmarks under a standard ISO 639-3 language identifier system, allowing for seamless incorporation of new benchmarks.

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Shuming Ma, Xu Sun, Junyang Lin, Houfeng Wang

Learning Relation Alignment for Calibrated Cross-modal Retrieval (2021.acl-long)

Challenge:	Existing abstractive text summarization models learn a semantic representation of the source text and the summaries from it.
Approach:	They evaluate the model on a popular Chinese social media dataset and compare it to other models.
Outcome:	The proposed model achieves state-of-the-art performance on a popular Chinese social media dataset.

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Shuhuai Ren, Junyang Lin, Guangxiang Zhao, Rui Men, An Yang, Jingren Zhou, Xu Sun, Hongxia Yang

STAND-Guard: A Small Task-Adaptive Content Moderation Model (2025.coling-industry)

Challenge:	despite advances in multimodal pre-training, cross-modal retrieval remains challenging . lack of relation consistency impairs contextualized representation of image-text pairs .
Approach:	They propose a new metric to quantify the relation consistency by measuring the semantic distance between linguistic and visual relations.
Outcome:	The proposed method boosts the performance of prevailing models on Flickr30k and MS COCO datasets by a considerable margin.

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Minjia Wang, Pingping Lin, Siqi Cai, Shengnan An, Shengjie Ma, Zeqi Lin, Congrui Huang, Bixiong Xu

Cross-lingual Spoken Language Understanding with Regularized Representation Alignment (2020.emnlp-main)

Challenge:	Content moderation is important for developing welcoming online platforms and responsible large language models.
Approach:	They propose a small task-adaptive coNtent moDeration model that can be easily adapted to new or customized content moderation tasks without extensive model tuning.
Outcome:	The proposed model is comparable to GPT-3.5-Turbo on unseen English binary classification tasks.

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Zihan Liu, Genta Indra Winata, Peng Xu, Zhaojiang Lin, Pascale Fung

Different Tunes Played with Equal Skill: Exploring a Unified Optimization Subspace for Parameter-Efficient Tuning (2022.findings-emnlp)

Challenge:	despite promising results, current cross-lingual models suffer from imperfect cross-linguistic representation alignments between the source and target languages, which makes the performance sub-optimal.
Approach:	They propose a regularization approach to align word-level and sentence-level representations across languages without external resources.
Outcome:	The proposed model outperforms state-of-the-art models in few-shot and zero-shot scenarios and achieves comparable performance to supervised training with all training data.

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

Adapting a Language Model While Preserving its General Knowledge (2022.emnlp-main)

Challenge:	Existing delta tuning algorithms freeze most of the parameters and only optimize minimal adaptive parameters.
Approach:	They propose to decompose DETs into a unified optimization subspace and conduct optimization within the subspace.
Outcome:	The proposed DETs achieve comparable performance to the original DET and can be transferred to another DET with non-trivial performance.

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Zixuan Ke, Yijia Shao, Haowei Lin, Hu Xu, Lei Shu, Bing Liu

Automatic Extraction of Commonsense LocatedNear Knowledge (P18-2)

Challenge:	Existing DA-training methods do not explicitly identify what knowledge should be preserved and what should be changed by the domain corpus.
Approach:	They propose to use an unlabeled corpus of aparticular domain to train a pre-trained general-purpose language model to adapt the LM so that end-tasks in the domain can give improved performances.
Outcome:	The proposed method improves the performance of pre-trained general-purpose language models by contrasting the representations of the general and the full (both general and domain knowledge) to learn an integrated representation with both general and specific knowledge.

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Frank F. Xu, Bill Yuchen Lin, Kenny Zhu

MedDCR: Learning to Design Agentic Workflows for Medical Coding (2026.findings-acl)

Challenge:	LocatedNear relation is a kind of commonsense knowledge describing two physical objects that are typically found near each other in real life.
Approach:	They propose to automatically extract LocatedNear relation from corpus by a sentence-level relation classifier and aggregating scores of entity pairs from a large corpus.
Outcome:	The proposed method can be used to extract the commonsense LOCATEDNEAR relation from a large corpus.

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Jiyang Zheng, Islam Nassar, Thanh Vu, Xu Zhong, Yang Lin, Tongliang Liu, Long Duong, Yuan-Fang Li

Challenge:	Medical coding is the process of translating unstructured clinical notes into standardized diagnostic and procedural codes.
Approach:	They propose a closed-loop framework that treats workflow design as a learning problem.
Outcome:	The proposed framework outperforms state-of-the-art workflows on benchmark datasets and produces interpretable, adaptable workflows that better reflect real coding practice.

ToolHop: A Query-Driven Benchmark for Evaluating Large Language Models in Multi-Hop Tool Use (2025.acl-long)

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Junjie Ye, Zhengyin Du, Xuesong Yao, Weijian Lin, Yufei Xu, Zehui Chen, Zaiyuan Wang, Sining Zhu, Zhiheng Xi, Siyu Yuan, Tao Gui, Qi Zhang, Xuanjing Huang, Jiecao Chen

𝜙-Decoding: Adaptive Foresight Sampling for Balanced Inference-Time Exploration and Exploitation (2025.acl-long)

Challenge:	Effective evaluation of multi-hop tool use is critical for analyzing the understanding, reasoning, and function-calling capabilities of large language models.
Approach:	They propose a dataset that provides rigorous evaluation of multi-hop tool use.
Outcome:	The proposed model achieves 49.04% accuracy across five model families.

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Fangzhi Xu, Hang Yan, Chang Ma, Haiteng Zhao, Jun Liu, Qika Lin, Zhiyong Wu

Golden Touchstone: A Comprehensive Bilingual Benchmark for Evaluating Financial Large Language Models (2025.findings-emnlp)

Challenge:	Existing inference-time optimization strategies address the shortsightedness of auto-regressive generation, but the vast search space leads to excessive exploration and insufficient exploitation.
Approach:	They propose a decoding strategy that approximates two distributions via foresight and clustering to provide an efficient estimation of step value.
Outcome:	The proposed decoding strategy outperforms strong baselines in performance and efficiency.

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Xiaojun Wu, Junxi Liu, Huan-Yi Su, Zhouchi Lin, Yiyan Qi, Chengjin Xu, Jiajun Su, Jiajie Zhong, Fuwei Wang, Saizhuo Wang, Fengrui Hua, Jia Li, Jian Guo

SoulChat: Improving LLMs’ Empathy, Listening, and Comfort Abilities through Fine-tuning with Multi-turn Empathy Conversations (2023.findings-emnlp)

Challenge:	Existing financial benchmarks suffer from limited language and task coverage, low-quality datasets, and inadequate adaptability for LLM evaluation.
Approach:	They propose a bilingual benchmark for financial LLMs that assesses models’ language understanding and generation capabilities.
Outcome:	The proposed bilingual benchmark assesses models’ language understanding and generation capabilities.

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Yirong Chen, Xiaofen Xing, Jingkai Lin, Huimin Zheng, Zhenyu Wang, Qi Liu, Xiangmin Xu

Learning from Context or Names? An Empirical Study on Neural Relation Extraction (2020.emnlp-main)

Challenge:	Large language models (LLMs) are used in psychological counseling to provide universal advice.
Approach:	They constructed a multi-turn empathetic conversation dataset with 2 million samples . they found that the model's empathy ability is enhanced when finetuning .
Outcome:	Experiments show that large language models can be finetuned to provide empathy . but, when applied to mental health or emotional support conversation, there are three main issues .

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Hao Peng, Tianyu Gao, Xu Han, Yankai Lin, Peng Li, Zhiyuan Liu, Maosong Sun, Jie Zhou

CRUXEVAL-X: A Benchmark for Multilingual Code Reasoning, Understanding and Execution (2025.acl-long)

Challenge:	Existing datasets may leak shallow heuristics via entity mentions, thus contributing to the high performance on RE benchmarks.
Approach:	They propose an entity-masked contrastive framework for relation extraction to gain a deeper understanding on textual context and type information while avoiding rote memorization of entities.
Outcome:	The proposed framework improves the effectiveness and robustness of neural models in different RE scenarios.

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Ruiyang Xu, Jialun Cao, Yaojie Lu, Ming Wen, Hongyu Lin, Xianpei Han, Ben He, Shing-Chi Cheung, Le Sun

Programming Languages in NLP

Challenge:	Existing code benchmarks focus on code generation, while those for code reasoning are insufficient.
Approach:	They propose a multi-lingual code reasoning benchmark that contains 19 programming languages and at least 600 subjects for each language.
Outcome:	The proposed model trains on Python and achieves 34.4% Pass@1 in other languages, revealing the cross-language generalization of LLMs.

Plug-and-Play Document Modules for Pre-trained Models (2023.acl-long)

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Chaojun Xiao, Zhengyan Zhang, Xu Han, Chi-Min Chan, Yankai Lin, Zhiyuan Liu, Xiangyang Li, Zhonghua Li, Zhao Cao, Maosong Sun

Learning Knowledge Bases with Parameters for Task-Oriented Dialogue Systems (2020.findings-emnlp)

Challenge:	Large-scale pre-trained models have been widely adopted for document-oriented NLP tasks, such as question answering.
Approach:	They propose to decouple document encoding from downstream tasks by introducing a document plugin into the backbone of a PTM.
Outcome:	The proposed model can encode documents once and for all across different scenarios.

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Andrea Madotto, Samuel Cahyawijaya, Genta Indra Winata, Yan Xu, Zihan Liu, Zhaojiang Lin, Pascale Fung

Learning When to Concentrate or Divert Attention: Self-Adaptive Attention Temperature for Neural Machine Translation (D18-1)

Challenge:	End-to-end systems rely on dialogue state tracking and annotations to fulfill user requests . modularized systems require multiple steps, including a direct interaction with the KB .
Approach:	They propose a method to embed the KB directly into the model parameters . they evaluate five task-oriented dialogue datasets with small, medium, and large KBs .
Outcome:	The proposed model can embed the KB directly into the model parameters without any DST or template responses, nor the kb as input.

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Junyang Lin, Xu Sun, Xuancheng Ren, Muyu Li, Qi Su

WildFeedback: Aligning LLMs With In-situ User Interactions And Feedback (2026.acl-long)

Challenge:	Neural Machine Translation models treat decoding at each time step equally with the same matrix . conventional methods treat decoder outputs at all time steps with the identical weight matrix causing inaccuracy .
Approach:	They propose a model with a mechanism to control the softness of attention by means of an attention temperature.
Outcome:	The proposed model outperforms baseline models on Chinese-English and English-Vietnamese translations.

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Taiwei Shi, Zhuoer Wang, Longqi Yang, Ying-Chun Lin, Zexue He, Mengting Wan, Pei Zhou, Sujay Kumar Jauhar, Sihao Chen, Shan Xia, Hongfei Zhang, Jieyu Zhao, Xiaofeng Xu, Xia Song, Jennifer Neville

Challenge:	Traditional alignment methods rely on human annotations and are subjective and misalignment with real-world user preferences.
Approach:	They propose a framework that leverages in-situ user feedback during conversations with LLMs to create preference datasets automatically.
Outcome:	The proposed framework identifies and classifies user feedback to LLM responses between conversation turns and creates examples of preferred and dispreferred responses according to user preferences.

ECO v1: Towards Event-Centric Opinion Mining (2022.findings-acl)

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Ruoxi Xu, Hongyu Lin, Meng Liao, Xianpei Han, Jin Xu, Wei Tan, Yingfei Sun, Le Sun

Multi-perspective Improvement of Knowledge Graph Completion with Large Language Models (2024.lrec-main)

Challenge:	Existing studies on event-centric opinion mining focus on entity-centric opinions . entity-centered opinions focus on sentimental polarity of events, while event-centered ones focus on content .
Approach:	They propose to perform event-centric opinion mining on event-argument structure and expression categorizing theory and benchmark it against a pioneer corpus.
Outcome:	The proposed task is feasible and challenging, and the results are beneficial for future studies.

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Derong Xu, Ziheng Zhang, Zhenxi Lin, Xian Wu, Zhihong Zhu, Tong Xu, Xiangyu Zhao, Yefeng Zheng, Enhong Chen

Multi-Modal Sarcasm Detection via Cross-Modal Graph Convolutional Network (2022.acl-long)

Challenge:	Knowledge graph completion (KGC) is a widely used method to tackle incompleteness in knowledge graphs (KGs).
Approach:	They propose a general framework to compensate for the deficiency of contextualized knowledge by querying large language models from various perspectives.
Outcome:	The proposed framework improves knowledge graph completion (KGC) by querying large language models from various perspectives.

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Bin Liang, Chenwei Lou, Xiang Li, Min Yang, Lin Gui, Yulan He, Wenjie Pei, Ruifeng Xu

Position Paper: Data-Centric AI in the Age of Large Language Models (2024.findings-emnlp)

Challenge:	Existing studies on multimodal sarcasm detection using textual and visual information have been limited to text-only approaches.
Approach:	They propose to construct a cross-modal graph for each multi-modal instance to explicitly draw the ironic relations between textual and visual modalities.
Outcome:	The proposed model achieves state-of-the-art in multi-modal sarcasm detection.

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Xinyi Xu, Zhaoxuan Wu, Rui Qiao, Arun Verma, Yao Shu, Jingtan Wang, Xinyuan Niu, Zhenfeng He, Jiangwei Chen, Zijian Zhou, Gregory Kang Ruey Lau, Hieu Dao, Lucas Agussurja, Rachael Hwee Ling Sim, Xiaoqiang Lin, Wenyang Hu, Zhongxiang Dai, Pang Wei Koh, Bryan Kian Hsiang Low

MVP-Tuning: Multi-View Knowledge Retrieval with Prompt Tuning for Commonsense Reasoning (2023.acl-long)

Challenge:	a paper proposes a data-centric perspective of AI research, focusing on large language models.
Approach:	They propose a data-centric viewpoint of AI research, focusing on large language models . they propose four scenarios centered around data, including data curation, attribution, knowledge transfer .
Outcome:	The proposed research focuses on large language models with data centric benchmarks . the proposed benchmarks can be used to develop new data curation methods .

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Yongfeng Huang, Yanyang Li, Yichong Xu, Lin Zhang, Ruyi Gan, Jiaxing Zhang, Liwei Wang

Crab: A Novel Configurable Role-Playing LLM with Assessing Benchmark (2025.acl-long)

Challenge:	Existing methods for commonsense reasoning rely on multi-hop knowledge retrieval and suffer low accuracy due toembedded noise in the acquired knowledge.
Approach:	They propose to use multi-hop knowledge retrieval to model knowledge and input text together.
Outcome:	The proposed method outperforms baselines on 5 commonsense reasoning datasets and is number one on theleaderboard.

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Kai He, Yucheng Huang, Wenqing Wang, Delong Ran, Dongming Sheng, Junxuan Huang, Qika Lin, Jiaxing Xu, Wenqiang Liu, Mengling Feng

Facilitating Fine-grained Detection of Chinese Toxic Language: Hierarchical Taxonomy, Resources, and Benchmarks (2023.acl-long)

Challenge:	Existing RP-LLMs employ only a single role with numerous dialogues, but Crab enables dynamic configuration of desired roles, thereby enhancing related flexibility and adaptability.
Approach:	They propose a Configurable Role-Playing LLM with Assessing Benchmark that combines a Role dataset curation, persona-emodying Llm construction, and comprehensive benchmark creation for RP dialogue generation.
Outcome:	The proposed model outperforms existing LLMs in performing fine-grained evaluations of RP while keeping dialogue per role minimal.

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Junyu Lu, Bo Xu, Xiaokun Zhang, Changrong Min, Liang Yang, Hongfei Lin

Challenge:	Existing datasets suffer from a lack of fine-grained annotations, such as the toxic type and expressions with indirect toxicity.
Approach:	They propose a benchmark model to detect toxic language by incorporating lexical features into a Chinese dataset to facilitate fine-grained annotations.
Outcome:	The proposed model is based on insulting vocabulary containing implicit profanity and is able to detect toxic language with lexical features.

Dynamic Knowledge Distillation for Pre-trained Language Models (2021.emnlp-main)

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Lei Li, Yankai Lin, Shuhuai Ren, Peng Li, Jie Zhou, Xu Sun

CascadeBERT: Accelerating Inference of Pre-trained Language Models via Calibrated Complete Models Cascade (2021.findings-emnlp)

Challenge:	Existing methods conduct knowledge distillation statically, e.g., student model aligns output distribution to teacher model on pre-defined training dataset.
Approach:	They propose a dynamic knowledge distillation that empowers the student to adjust the learning procedure according to its competency . they find it is promising and provide discussions on potential future directions towards more efficient methods .
Outcome:	The proposed method can boost student model performance while accelerating training . the proposed method reduces memory usage and accelerates model inference .

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Lei Li, Yankai Lin, Deli Chen, Shuhuai Ren, Peng Li, Jie Zhou, Xu Sun

Mitigating Biases of Large Language Models in Stance Detection with Counterfactual Augmented Calibration (2025.naacl-long)

Challenge:	Experimental results show that CascadeBERT can achieve an overall 15% improvement under 4x speed-up compared with existing dynamic early exiting methods on six classification tasks.
Approach:	They propose a framework which emits predictions in internal layers without passing through the entire model.
Outcome:	The proposed framework can achieve 15% improvement under 4x speed-up compared with existing methods on six classification tasks yielding more calibrated and accurate predictions.

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Ang Li, Jingqian Zhao, Bin Liang, Lin Gui, Hui Wang, Xi Zeng, Xingwei Liang, Kam-Fai Wong, Ruifeng Xu

Imitation Learning for Non-Autoregressive Neural Machine Translation (P19-1)

Challenge:	Large language models generate biased stances due to spurious correlations and preference towards certain individuals and topics.
Approach:	They propose a counterfactual Augmented Calibration Network to calibrate potential bias in stance detection of large language models.
Outcome:	The proposed calibration network can mitigate biases of large language models, achieving state-of-the-art results.

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Bingzhen Wei, Mingxuan Wang, Hao Zhou, Junyang Lin, Xu Sun

DocRED: A Large-Scale Document-Level Relation Extraction Dataset (P19-1)

Challenge:	Existing non-autoregressive translation models lack parallel decoding, which is a bottleneck for NMT decoding.
Approach:	They propose a framework for non-autoregressive machine translation that emulates the autoregressive model by sampling sentence length in parallel.
Outcome:	The proposed model achieves 31.85 BLEU on WMT16 RoEn and 30.68 BLUE on IWSLT16 EnDe on the IWSLD16, WMT14 and WMT15 datasets.

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Yuan Yao, Deming Ye, Peng Li, Xu Han, Yankai Lin, Zhenghao Liu, Zhiyuan Liu, Lixin Huang, Jie Zhou, Maosong Sun

Ensuring Safe and High-Quality Outputs: A Guideline Library Approach for Language Models (2024.naacl-long)

Challenge:	Existing relation extraction methods focus on extracting intra-sentence relations for single entities.
Approach:	They propose a relation extraction dataset from Wikipedia and Wikidata with three features . document-level relation extraction is a task to identify relational facts between entities .
Outcome:	The proposed dataset is the largest human-annotated dataset for document-level RE from plain text.

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Yi Luo, Zhenghao Lin, YuHao Zhang, Jiashuo Sun, Chen Lin, Chengjin Xu, Xiangdong Su, Yelong Shen, Jian Guo, Yeyun Gong

A Universal Discriminator for Zero-Shot Generalization (2023.acl-long)

Challenge:	Guide-Align is a guideline-oriented approach to augment the safety and quality of Large Language Models.
Approach:	They propose a guideline-oriented method to augment the safety and quality of large language models.
Outcome:	The proposed method outperforms existing methods on three benchmarks and shows significant improvements in security and quality.

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Haike Xu, Zongyu Lin, Jing Zhou, Yanan Zheng, Zhilin Yang

WECA: A WordNet-Encoded Collocation-Attention Network for Homographic Pun Recognition (D18-1)

Challenge:	Generative modeling has been the dominant approach for large-scale pretraining and zeroshot generalization.
Approach:	They propose a discriminator that predicts whether a text sample comes from the true data distribution and which option has the highest probability of coming from the real data distribution.
Outcome:	The proposed discriminative approach outperforms GANs on a number of NLP tasks by 16.0%, 7.8%, and 11.5% respectively.

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Yufeng Diao, Hongfei Lin, Di Wu, Liang Yang, Kan Xu, Zhihao Yang, Jian Wang, Shaowu Zhang, Bo Xu, Dongyu Zhang