Papers by Hang Zhang

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

Challenge:	Large language models have demonstrated impressive performance across a wide range of tasks, but this achievement comes with the trade-off of significant computational demands.
Approach:	They propose a scaling law that decomposes the overall validation loss and assigns different importance weights to tokens to assess a specific meta-capability.
Outcome:	The proposed model can predict the loss trending of models across different levels of computation without a gap between validation loss and model's downstream capabilities.

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

DORE: Document Ordered Relation Extraction based on Generative Framework (2022.findings-emnlp)

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

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Qipeng Guo, Yuqing Yang, Hang Yan, Xipeng Qiu, Zheng Zhang

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

Challenge:	Existing generative methods do not fit document-level relation extraction tasks where there are multiple entities and relational facts.
Approach:	They propose to generate a symbolic and ordered sequence from relation matrix which is easier to learn and introduce several negative sampling strategies to improve the performance with balanced signals.
Outcome:	The proposed method can improve the performance of the generative DocRE models on four datasets.

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

Programming Languages in NLP

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

UnifiedMLLM: Enabling Unified Representation for Multi-modal Multi-tasks With Large Language Model (2025.findings-naacl)

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Zhaowei Li, Wei Wang, YiQing Cai, Qi Xu, Pengyu Wang, Dong Zhang, Hang Song, Botian Jiang, Zhida Huang, Tao Wang

Octa: Omissions and Conflicts in Target-Aspect Sentiment Analysis (2020.findings-emnlp)

Challenge:	Representative models like LLaVA and MiniGPT-4 have great capabilities in various tasks.
Approach:	They propose a unified model to represent various multi-modal tasks using a single representation.
Outcome:	The proposed model outperforms existing models in a variety of tasks while maintaining generality and scalability.

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Zhe Zhang, Chung-Wei Hang, Munindar Singh

Noisy Pair Corrector for Dense Retrieval (2023.findings-emnlp)

Challenge:	Existing sentiment analysis models treat aspects and targets separately, causing conflicting sentiments.
Approach:	They propose an approach that jointly considers aspects and targets when inferring sentiments.
Outcome:	The proposed approach outperforms leading models by 1.6% to 4.3% on benchmark datasets . it uses selective attention mechanisms for selective attention between targets and context words .

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Hang Zhang, Yeyun Gong, Xingwei He, Dayiheng Liu, Daya Guo, Jiancheng Lv, Jian Guo

Information Retrieval

Challenge:	Existing dense retrieval models assume that query-document pairs are exactly matched, resulting in mismatched-pair noise.
Approach:	They propose a novel approach to train an effective model with mismatched-pair noise.
Outcome:	The proposed model performs well on natural question and triviaQA, code-search benchmarks and SO-DS.

CoCo: Coherence-Enhanced Machine-Generated Text Detection Under Low Resource With Contrastive Learning (2023.emnlp-main)

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Xiaoming Liu, Zhaohan Zhang, Yichen Wang, Hang Pu, Yu Lan, Chao Shen

Length Generalization of Causal Transformers without Position Encoding (2024.findings-acl)

Challenge:	Recent proposed methods fail to consider the linguistic structure of texts and lack the ability to handle the low-resource problem.
Approach:	They propose a coherence-based contrastive learning model named CoCo to detect MGTs under low-resource scenario.
Outcome:	The proposed model outperforms state-of-the-art methods on two datasets and two self-constructed datasets.

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Jie Wang, Tao Ji, Yuanbin Wu, Hang Yan, Tao Gui, Qi Zhang, Xuanjing Huang, Xiaoling Wang

Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding (2023.emnlp-demo)

Challenge:	Besides Transformers without position encodings, the success of NoPE provides a new way to overcome the challenge of generalizing to longer sentences.
Approach:	They propose a parameter-efficient tuning for searching attention heads’ best temperature hyper-parameters, which substantially expands NoPE’s context size.
Outcome:	The proposed tuning significantly expands NoPE's context size, allowing it to generalize to longer sentences with state-of-the-art generalization algorithms.

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Hang Zhang, Xin Li, Lidong Bing

A Unified Generative Framework for Various NER Subtasks (2021.acl-long)

Challenge:	Large Language Models (LLMs) are capable of understanding multi-modal content, but textonly human-computer interaction is not sufficient for many application scenarios.
Approach:	They propose a video-to-text generation task and a multi-modal framework that bootstraps cross-modal training from frozen pre-trained visual & audio encoders and frozen LLMs.
Outcome:	The proposed framework can understand both visual and auditory content in video and generate meaningful responses grounded in the visual and audio information presented in the videos.

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Hang Yan, Tao Gui, Junqi Dai, Qipeng Guo, Zheng Zhang, Xipeng Qiu

DetGPT: Detect What You Need via Reasoning (2023.emnlp-main)

Challenge:	Named Entity Recognition (NER) is the task of identifying spans that represent entities in sentences.
Approach:	They propose to formulate NER subtasks as entity span sequence generation task . framework can be used to solve all three kinds of NER tasks without tagging schema .
Outcome:	The proposed framework achieves state-of-the-art (SoTA) or near SoTA performance on eight English NER datasets.

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Renjie Pi, Jiahui Gao, Shizhe Diao, Rui Pan, Hanze Dong, Jipeng Zhang, Lewei Yao, Jianhua Han, Hang Xu, Lingpeng Kong, Tong Zhang

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

Challenge:	Recent advances in the field of computer vision have enabled more effective and sophisticated interactions between humans and machines.
Approach:	They propose a reasoning-based object detection paradigm that leverages state-of-the-art multi-modal models and open-vocabulary object detectors to perform reasoning within the context of the user’s instructions and the visual scene.
Outcome:	The proposed method enables users to interact with the system using natural language instructions, allowing for a higher level of interactivity.

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

Programming Languages in NLP

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

SPARD: Self-Paced Curriculum for RL Alignment via Integrating Reward Dynamics and Data Utility (2026.acl-long)

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Xuyang Zhi, Peilun Zhou, Chengqiang Lu, Hang Lv, Yiwei Liang, Rongyang Zhang, Yan Gao, null Yiwu, Yao Hu, Hongchao Gu, Defu Lian, Hao Wang, Enhong Chen

Challenge:	Large Language Models (LLMs) are shifting the focus from single verifiable tasks toward complex, open-ended real-world scenarios.
Approach:	They propose a framework that automatically adjusts reward weights and data importance to synchronize learning intent with data utility for optimal performance.
Outcome:	The proposed framework improves model capabilities across all domains and scales.

EHRAgent: Code Empowers Large Language Models for Few-shot Complex Tabular Reasoning on Electronic Health Records (2024.emnlp-main)

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Wenqi Shi, Ran Xu, Yuchen Zhuang, Yue Yu, Jieyu Zhang, Hang Wu, Yuanda Zhu, Joyce Ho, Carl Yang, May Dongmei Wang

Generating then Refining for Reliable Knowledge Base Question Answering (2026.acl-long)

Challenge:	EHRAgent enables clinicians to interact with EHRs using natural language . reliance on rule-based conversion systems often necessitates additional training or effort from data engineers.
Approach:	They propose a large language model agent that generates and executes code in natural language to facilitate clinicians in directly interacting with EHRs.
Outcome:	The proposed agent outperforms the strongest baseline by up to 29.6% in success rate on three real-world EHR datasets.

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Jianqi Gao, Hang Yu, Jian Cao, Ranran Bu, Jinghua Tang, Nengjun Zhu, Yonggang Zhang

AnyGPT: Unified Multimodal LLM with Discrete Sequence Modeling (2024.acl-long)

Challenge:	Existing knowledge base question answering methods generate LFs that are non-executable due to semantic hallucination issue of large language models.
Approach:	They propose a "generate-verify-refine" framework for reliable LF generation . they propose ARI-KBQA to generate query paths based on hop-by-hop reasoning .
Outcome:	The proposed framework significantly improves model performance with a reduced search space . ARI-KBQA can generate LFs that are non-executable due to semantic hallucination issue .

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Jun Zhan, Junqi Dai, Jiasheng Ye, Yunhua Zhou, Dong Zhang, Zhigeng Liu, Xin Zhang, Ruibin Yuan, Ge Zhang, Linyang Li, Hang Yan, Jie Fu, Tao Gui, Tianxiang Sun, Yu-Gang Jiang, Xipeng Qiu

Balancing Knowledge Breadth and Task Depth for Effective Domain Adaptation Fine-Tuning (2026.findings-acl)

Challenge:	Existing language models that use discrete representations for unified processing of various modalities are limited to text generation and do not include multimodal output.
Approach:	They propose a multimodal language model that utilizes discrete representations for unified processing of various modalities.
Outcome:	The proposed model can be trained stably without any alterations to existing models or training paradigms.

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Mu Zhang, Yuxiang Chu, Guangya Yu, Yongqi Fan, Weiyan Zhang, Hang Hu, Tong Ruan, Jingping Liu

Sentiment-Aware Word and Sentence Level Pre-training for Sentiment Analysis (2022.emnlp-main)

Challenge:	a lack of knowledge breadth and task depth can hinder curriculum learning in domains such as medicine and finance.
Approach:	They propose a two-dimensional curriculum learning framework that coordinates model training along two orthogonal axes: the knowledge dimension and the task dimension.
Outcome:	The proposed framework improves accuracy on medical evaluations by 2.49% and on financial evaluations 1.2% compared with the second-best method.

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Shuai Fan, Chen Lin, Haonan Li, Zhenghao Lin, Jinsong Su, Hang Zhang, Yeyun Gong, JIan Guo, Nan Duan

LLM Reasoning as Trajectories: Step-Specific Representation Geometry and Correctness Signals (2026.acl-long)

Challenge:	Existing pre-trained language representation models (PLMs) capture sentiment information from word-level while under-considering sentence-level information.
Approach:	They propose a Sentiment-aware pre-trained language model with combined Word-level and Sentence-level Pre-training tasks that enhance the PLM’s knowledge about sentiment words.
Outcome:	The proposed model achieves state-of-the-art on various sentence-level and aspect-level sentiment classification benchmarks.

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Lihao Sun, Hang Dong, Bo Qiao, Qingwei Lin, Dongmei Zhang, Saravan Rajmohan

Representation Learning

Challenge:	Existing models generate tokens by updating high-dimensional representations and decoding from them at each timestep.
Approach:	They propose a framework that allows reasoning correction and length control based on derived ideal trajectories.
Outcome:	The proposed model can predict correctness and length control based on ideal trajectories.

Code Needs Comments: Enhancing Code LLMs with Comment Augmentation (2024.findings-acl)

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Demin Song, Honglin Guo, Yunhua Zhou, Shuhao Xing, Yudong Wang, Zifan Song, Wenwei Zhang, Qipeng Guo, Hang Yan, Xipeng Qiu, Dahua Lin

Programming Languages in NLP

Challenge:	Large Language Models (LLMs) require a deep understanding of programming languages and their correlation with natural languages (NLs).
Approach:	They propose a data augmentation method that generates comments for existing code and a filtering strategy that filters out code data poorly correlated with natural language.
Outcome:	The proposed method outperforms the model trained on the augmented data and the model further trained on data without augmentation on two widely-used programming skill benchmarks.

Uncertainty-Aware Unlikelihood Learning Improves Generative Aspect Sentiment Quad Prediction (2023.findings-acl)

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Mengting Hu, Yinhao Bai, Yike Wu, Zhen Zhang, Liqi Zhang, Hang Gao, Shiwan Zhao, Minlie Huang

Efficient Transformer-based Large Scale Language Representations using Hardware-friendly Block Structured Pruning (2020.findings-emnlp)

Challenge:	Existing studies focus on what to generate but ignore what not to generate . a template-agnostic method boosts original learning and reduces mistakes simultaneously .
Approach:	They propose a template-agnostic method to control the token-level generation . they introduce Monte Carlo dropout to understand the built-in uncertainty of pre-trained language models .
Outcome:	The proposed method boosts original learning and reduces mistakes simultaneously on four public datasets.

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Bingbing Li, Zhenglun Kong, Tianyun Zhang, Ji Li, Zhengang Li, Hang Liu, Caiwen Ding

ErrorRadar: Benchmarking Complex Mathematical Reasoning of Multimodal Large Language Models Via Error Detection (2026.findings-acl)

Challenge:	Pretrained large-scale language models have been criticized for their limited weight storage and computational speed on hardware platforms.
Approach:	They propose an efficient transformer-based large-scale language representation using hardware-friendly block structure pruning.
Outcome:	The proposed model achieves 5.0x accuracy on GLUE benchmarks and 1.79x compression rate on DistilBERT.

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Yibo Yan, Shen Wang, Jiahao Huo, Hang Li, Boyan Li, Jiamin Su, Xiong Gao, YiFan Zhang, Tianlong Xu, Zhendong Chu, Aoxiao Zhong, Kun Wang, Hui Xiong, Philip S. Yu, Xuming Hu, Qingsong Wen

Toward Building General Foundation Models for Language, Vision, and Vision-Language Understanding Tasks (2023.findings-emnlp)

Challenge:	Current mathematical benchmarks focus on evaluating MLLMs’ problem-solving ability, yet there is a crucial gap in addressing more complex scenarios such as error detection.
Approach:	They propose to evaluate multimodal error detection by evaluating two sub-tasks error step identification and error categorization.
Outcome:	The proposed task evaluates MLLMs' ability to handle multimodal questions compared to text-only models.

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Xinsong Zhang, Yan Zeng, Jipeng Zhang, Hang Li

Simple but Effective Compound Geometric Operations for Temporal Knowledge Graph Completion (2024.acl-long)

Challenge:	Existing foundation models can only perform the best in one type of understanding tasks.
Approach:	They propose a method for training a general foundation model, X-FM, using text, image, and image-text data.
Outcome:	The proposed method outperforms existing foundation models on language, vision, and vision-language understanding tasks.

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Rui Ying, Mengting Hu, Jianfeng Wu, Yalan Xie, Xiaoyi Liu, Zhunheng Wang, Ming Jiang, Hang Gao, Linlin Zhang, Renhong Cheng

TofuEval: Evaluating Hallucinations of LLMs on Topic-Focused Dialogue Summarization (2024.naacl-long)

Challenge:	Current methods embed factual knowledge into continuous vector space and apply geometric operations to learn potential patterns in temporal knowledge graphs.
Approach:	They propose a temporal knowledge graph completion method that uses two geometric operations to learn missing facts in temporal graphs.
Outcome:	The proposed method significantly outperforms existing temporal knowledge graph embedding models.

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Liyan Tang, Igor Shalyminov, Amy Wong, Jon Burnsky, Jake Vincent, Yu’an Yang, Siffi Singh, Song Feng, Hwanjun Song, Hang Su, Lijia Sun, Yi Zhang, Saab Mansour, Kathleen McKeown

Natural Language Interfaces

Challenge:	Existing LLMs hallucinate significant amounts of factual errors in the dialogue domain, regardless of the model’s size.
Approach:	They propose to evaluate topic-focused dialogue summarization by using large language models (LLMs) they use human annotations to evaluate factual consistency and explain factually inconsistent sentences.
Outcome:	The proposed evaluation benchmark on topic-focused dialogue summarization shows that existing LLMs hallucinate significant amounts of factual errors regardless of the model’s size.

Exploring the Generalizability of Factual Hallucination Mitigation via Enhancing Precise Knowledge Utilization (2025.findings-emnlp)

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Siyuan Zhang, Yichi Zhang, Yinpeng Dong, Hang Su

TRUST: Towards Robust Social Bot Detection via Uncertainty-Guided Pseudo-Labeling and Graph Structure Purification (2026.findings-acl)

Challenge:	Existing methods to improve LLMs' ability to align their responses with objective facts suffer from poor generalization and trade-offs in other different capabilities.
Approach:	They propose to introduce PKUE (Precise Knowledge Utilization Enhancement) which fine-tunes the model on self-generated responses to precise and simple factual questions through preference optimization.
Outcome:	The proposed enhancements improve LLM’s ability to precisely leverage its knowledge and improve FactualBench, a comprehensive and precise factual QA dataset containing 181k Chinese data spanning 21 domains.

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Ruixuan Xu, Mengting Hu, Zhunheng Wang, Ming Jiang, Rui Ying, Zhen Zhang, Hang Gao, Shuaipeng Liu, Renhong Cheng

ReFT: Reasoning with Reinforced Fine-Tuning (2024.acl-long)

Challenge:	Existing graph-based detection models are vulnerable to deceptive message propagation, where bots deliberately interact with legitimate users.
Approach:	They propose a framework to mitigate deceptive message propagation by node-level uncertainty estimation and graph structure purification.
Outcome:	The proposed framework improves on three benchmark datasets and six GNN backbones on real-world social bots.

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Luong Trung, Xinbo Zhang, Zhanming Jie, Peng Sun, Xiaoran Jin, Hang Li

Towards Provably Secure Generative AI: Reliable Consensus Sampling (2026.findings-acl)

Challenge:	Existing approaches to improve the generalization of large language models are using Supervised Fine-Tuning (SFT) this approach does not show sufficient generalization ability because it only relies on the given CoT data.
Approach:	They propose to use Chain-of-Thought annotations to train Large Language Models using supervised fine-tuning to improve generalization.
Outcome:	The proposed approach outperforms SFT on GSM8K, MathQA, and SVAMP datasets and shows a superior generalization ability.

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Yu Cui, Hang Fu, Sicheng Pan, Zhuoyu Sun, Yifei Liu, Yuhong Nie, Bo Ran, Baohan Huang, Xufeng Zhang, Haibin Zhang, Cong Zuo, Licheng Wang

Robustness in NLP

Challenge:	Existing research on generative AI security is driven by mutually reinforcing attack and defense methodologies grounded in empirical experience.
Approach:	They propose a new algorithm that uses a random sampling algorithm to control risk.
Outcome:	The proposed algorithm improves robustness and utility while maintaining latency comparable to existing algorithms.

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

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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

Adaptive Text2GQL: Integrating Structural Twig Linking and Evolutionary In-Context Learning (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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Fang Niu, Chaokun Wang, Hang Zhang, Songyao Wang

BattleAgent: Multi-modal Dynamic Emulation on Historical Battles to Complement Historical Analysis (2024.emnlp-demo)

Challenge:	Existing approaches struggle with structural hallucinations and lack adaptability in cold-start scenarios.
Approach:	They propose a unified, training-free framework for translating natural language into Graph Query Languages.
Outcome:	The proposed framework improves accuracy and executability over baselines in Graph2GQLs.

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Shuhang Lin, Wenyue Hua, Lingyao Li, Che-Jui Chang, Lizhou Fan, Jianchao Ji, Hang Hua, Mingyu Jin, Jiebo Luo, Yongfeng Zhang

Benchmarking Diverse-Modal Entity Linking with Generative Models (2023.findings-acl)

Challenge:	Recent advances in large language models have demonstrated impressive reasoning capabilities, indicating their potential to serve as the foundation for agents.
Approach:	They propose a detailed emulation system that combines large vision-language model and multi-agent system to emulate dynamic interactions between multiple agents over a period of time.
Outcome:	The proposed system combines large vision-language model and multi-agent system to emulate dynamic interactions between agents and their environments over a period of time.

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Sijia Wang, Alexander Hanbo Li, Henghui Zhu, Sheng Zhang, Pramuditha Perera, Chung-Wei Hang, Jie Ma, William Yang Wang, Zhiguo Wang, Vittorio Castelli, Bing Xiang, Patrick Ng

A Survey of Uncertainty Estimation Methods on Large Language Models (2025.findings-acl)

Challenge:	Existing models for diverse-mode entity linking (EL) work well on per modality configurations, but it is more challenging to design a unified model for diverse modality.
Approach:	They propose a generative diverse-modal model that integrates text, image and table . they propose combining a multimodal encoder-decoder paradigm with a fine-tuning GDMM .
Outcome:	The proposed model outperforms state-of-the-art models by 8.51 F1 on average for diverse-modal EL.

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Zhiqiu Xia, Jinxuan Xu, Yuqian Zhang, Hang Liu

Guardian-as-an-Advisor: Advancing Next-Generation Guardian Models for Trustworthy LLMs (2026.findings-acl)

Challenge:	Large language models (LLMs) have demonstrated remarkable capabilities but could produce biased, hallucinated, or non-factual responses.
Approach:	They propose to conduct extensive experimental evaluations of LLM uncertainty estimation methods . large language models have demonstrated remarkable capabilities across tasks .
Outcome:	The proposed method could produce biased, hallucinated, or non-factual responses . a lack of comprehensive surveys on LLM uncertainty estimation is a problem .

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Yue Huang, Haomin Zhuang, Jiayi Ye, Han Bao, Yanbo Wang, Hang Hua, Siyuan Wu, Pin-Yu Chen, Xiangliang Zhang

XFormParser: A Simple and Effective Multimodal Multilingual Semi-structured Form Parser (2025.coling-main)

Challenge:	prevailing taxonomies neglect robustness and honesty, yielding safer-on-paper but less useful systems.
Approach:	They propose a soft-gating pipeline where a guardian predicts a binary risk label plus a concise explanation and prepends this advice to the original query for re-inference.
Outcome:	The proposed model maintains safety while reducing over-refusal.

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Xianfu Cheng, Hang Zhang, Jian Yang, Xiang Li, Weixiao Zhou, Fei Liu, Kui Wu, Xiangyuan Guan, Tao Sun, Xianjie Wu, Tongliang Li, Zhoujun Li

Multilinguality

Challenge:	Document AI parsing semi-structured image form is a key information extraction task.
Approach:	They propose a multimodal and multilingual semi-structured FORM PARSER which integrates SER and relation extraction into a unified framework.
Outcome:	The proposed framework achieves up to 1.79% improvement on RE tasks in multilingual and zero-shot settings.

GroundingGPT: Language Enhanced Multi-modal Grounding Model (2024.acl-long)

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Zhaowei Li, Qi Xu, Dong Zhang, Hang Song, YiQing Cai, Qi Qi, Ran Zhou, Junting Pan, Zefeng Li, Vu Tu, Zhida Huang, Tao Wang

E2LLM: Encoder Elongated Large Language Models for Long-Context Understanding and Reasoning (2025.emnlp-main)

Challenge:	Existing multi-modal large language models focus on capturing global information while neglecting the fine-grained local information in multimodal inputs.
Approach:	They propose an end-to-end language enhanced multi-modal grounding model that performs fine-grained grounding tasks for image, video and audio.
Outcome:	The proposed model achieves impressive fine-grained understanding of multi-modal inputs while maintaining or improving its global comprehension capabilities.

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Zihan Liao, Jun Wang, Hang Yu, Lingxiao Wei, Jianguo Li, Jun Wang, Wei Zhang

UBench: Benchmarking Uncertainty in Large Language Models with Multiple Choice Questions (2025.findings-acl)

Challenge:	Considerable efforts have been and are still being put into increasing the context length of Large Language Models (LLMs)
Approach:	They propose an approach that divides long contexts into chunks, compresses each into soft prompts using a pretrained text encoder, and aligns these representations with a decoder-only LLM via an adapter.
Outcome:	The proposed approach outperforms 8 state-of-the-art methods in effectiveness and efficiency for document summarization and question answering, and achieves the best performance on LongBench v2 among models of comparable size.

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Xunzhi Wang, Zhuowei Zhang, Gaonan Chen, Qiongyu Li, Bitong Luo, Zhixin Han, Haotian Wang, Zhiyu Li, Hang Gao, Mengting Hu

ZeroAE: Pre-trained Language Model based Autoencoder for Transductive Zero-shot Text Classification (2023.findings-acl)

Challenge:	Existing methods for benchmarking the uncertainty of large language models face challenges . existing methods require internal model access, additional training, or high computational costs .
Approach:	They propose a new benchmark for evaluating the uncertainty of large language models based on confidence intervals . UBench encompasses 11,978 multiple choice questions spanning knowledge, language, understanding, and reasoning capabilities.
Outcome:	The proposed method outperforms existing methods for benchmarking the uncertainty of large language models.

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Kaihao Guo, Hang Yu, Cong Liao, Jianguo Li, Haipeng Zhang

Low-Resource NLP

Challenge:	Existing methods for text classification use only encoders or decoders that do not allow for the use of labels in unseen domains.
Approach:	They propose an autoencoder that encodes text into two disentangled spaces and decodes it to generate text with labels in the unseen domains.
Outcome:	The proposed model outperforms the existing methods in label-partially-unseen and label-fully-un-seeen scenarios and even outperfects the SOTA methods.

Can Your Model Tell a Negation from an Implicature? Unravelling Challenges With Intent Encoders (2024.acl-long)

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Yuwei Zhang, Siffi Singh, Sailik Sengupta, Igor Shalyminov, Hang Su, Hwanjun Song, Saab Mansour

Text-to-Table: A New Way of Information Extraction (2022.acl-long)

Challenge:	Conversational systems often rely on embedding models for intent classification and intent clustering tasks.
Approach:	They propose a toolkit that gives a more holistic view of intent embedding models by considering three tasks– (1) intent classification, (2) intent clustering, and (3) a novel triplet task.
Outcome:	The proposed model improves on the linguistic dimensions while affecting performance on downstream task metrics.

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Xueqing Wu, Jiacheng Zhang, Hang Li

A Unified Generative Framework for Aspect-based Sentiment Analysis (2021.acl-long)

Challenge:	Existing methods for information extraction are not well understood . text-to-table is a problem that aims to extract information from text data .
Approach:	They propose a new problem setting of information extraction, called text-to-table . they formalize text- to-table as a sequence-tosequence problem .
Outcome:	The proposed method outperforms existing methods on text-to-table tasks.

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Hang Yan, Junqi Dai, Tuo Ji, Xipeng Qiu, Zheng Zhang

Disco-RAG: Discourse-Aware Retrieval-Augmented Generation (2026.acl-long)

Challenge:	Existing complicated ABSA models focus on subtasks, which leads to complicated solutions . et al., j. c. d. r., and j dr. s. v. present a unified approach to solve seven subtask tasks in one framework.
Approach:	They redefine every subtask target as a sequence mixed by pointer indexes and sentiment class indexe . they exploit the pre-training sequence-to-sequence model BART to solve all ABSA subtasks in an end-to end framework.
Outcome:	The proposed framework achieves substantial performance gain and provides a real unified solution for the whole ABSA subtasks.

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Dongqi Liu, Hang Ding, Qiming Feng, Xurong Xie, Zhucun Xue, Chengjie Wang, Jian Li, Jiangning Zhang, Yabiao Wang

Information Retrieval

Challenge:	Existing RAG strategies treat retrieved passages in a flat and unstructured way, which prevents the model from capturing structural cues and constrains its ability to synthesize knowledge from dispersed evidence across documents.
Approach:	They propose a framework that explicitly injects discourse signals into the generation process.
Outcome:	Experiments on question answering and long-document summarization benchmarks show the efficacy of the proposed framework.

GLGE: A New General Language Generation Evaluation Benchmark (2021.findings-acl)

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Dayiheng Liu, Yu Yan, Yeyun Gong, Weizhen Qi, Hang Zhang, Jian Jiao, Weizhu Chen, Jie Fu, Linjun Shou, Ming Gong, Pengcheng Wang, Jiusheng Chen, Daxin Jiang, Jiancheng Lv, Ruofei Zhang, Winnie Wu, Ming Zhou, Nan Duan

TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale (2026.acl-industry)

Challenge:	Multi-task benchmarks focus on a range of Natural Language Understanding (NLU) tasks without considering the Natural Language Generation (NLG) models.
Approach:	They propose a multi-task benchmark for evaluating the generalization capabilities of NLG models across eight language generation tasks.
Outcome:	The proposed benchmarks are based on GLUE and Su-perGLUE for English and several other languages.

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Jun Wang, Ziyin Zhang, Rui Wang, Hang Yu, Peng Di, Rui Wang

Challenge:	a 5minute downtime for an incident could result in a loss of 40 million dollars and erosion of user trust.
Approach:	They propose a multi-stage event unification engine that synergizes efficient indexing techniques with Large Language Models (LLMs) to make informed decisions on event merging.
Outcome:	The proposed system outperforms baseline methods in routing accuracy, clustering quality, and Signal-to-Noise Ratio.

Investigating Glyph-Phonetic Information for Chinese Spell Checking: What Works and What’s Next? (2023.findings-acl)

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Xiaotian Zhang, Yanjun Zheng, Hang Yan, Xipeng Qiu

Syntactic Text Processing

Challenge:	Pre-trained Chinese language models have shown impressive performance on a wide range of NLP tasks, but the generalization ability of these models has not been well understood.
Approach:	They propose to use glyph-phonetic information to improve Chinese spell checking models . they propose a new, more challenging, and practical setting for testing the generalizability of CSC models.
Outcome:	The proposed model incorporates glyph-phonetic information and is more challenging and practical.

Toward Optimal LLM Alignments Using Two-Player Games (2025.findings-emnlp)

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Rui Zheng, Hongyi Guo, Zhihan Liu, Xiaoying Zhang, Yuanshun Yao, Xiaojun Xu, Zhaoran Wang, Zhiheng Xi, Tao Gui, Qi Zhang, Xuanjing Huang, Yang Liu, Hang Li

Benchmarking Chinese Commonsense Reasoning of LLMs: From Chinese-Specifics to Reasoning-Memorization Correlations (2024.acl-long)

Challenge:	Alignment of large language models (LLM) is a process that ensures the model’s responses to user prompts align with human intentions and social values.
Approach:	They propose an alignment method based on a two-agent game consisting of an adversarial agent and a defensive agent.
Outcome:	The proposed method improves on a two-agent game with an adversarial agent and a defensive agent.

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Jiaxing Sun, Weiquan Huang, Jiang Wu, Chenya Gu, Wei Li, Songyang Zhang, Hang Yan, Conghui He

Dialogue Meaning Representation for Task-Oriented Dialogue Systems (2022.findings-emnlp)

Challenge:	Currently, many benchmarks evaluate the commonsense reasoning of large language models (LLMs), but most are English-based, limiting non-English evaluations.
Approach:	They propose to use Chinese commonsense reasoning to evaluate LLMs' commonsensing ability.
Outcome:	The proposed benchmark covers both globally known and Chinese-specific commonsense reasoning abilities and can be used as a reference for future research.

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Xiangkun Hu, Junqi Dai, Hang Yan, Yi Zhang, Qipeng Guo, Xipeng Qiu, Zheng Zhang

Natural Language Interfaces

Challenge:	Existing work on dialogue meaning representations is limited in scalability for complex expressions.
Approach:	They propose a pliable and easily extendable representation for task-oriented dialogue . they propose an inheritance hierarchy mechanism focusing on domain extensibility .
Outcome:	The proposed representation can be easily extended to a task-oriented dialogue dataset.

Knowledge Enhanced Pre-training for Cross-lingual Dense Retrieval (2024.lrec-main)

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Hang Zhang, Yeyun Gong, Dayiheng Liu, Shunyu Zhang, Xingwei He, Jiancheng Lv, Jian Guo

MM-Doc-R1: Training Agents for Long Document Visual Question Answering through Multi-turn Reinforcement Learning (2026.findings-acl)

Challenge:	Existing mPLMs neglect the importance of knowledge in cross-lingual dense retrieval.
Approach:	They propose a novel mPLM that leverages knowledge to learn language-agnostic semantic representations from a multilingual knowledge base and an annotation of Wiki.
Outcome:	The proposed model achieves strong multilingual and cross-lingual retrieval performance with significant improvements over existing mPLMs.

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Jiahang Lin, Kai Hu, Binghai Wang, Yuhao Zhou, Zhiheng Xi, Honglin Guo, Shichun Liu, Junzhe Wang, Shihan Dou, Enyu Zhou, Hang Yan, Zhenhua Han, Tao Gui, Qi Zhang, Xuanjing Huang

Visual Data in NLP

Challenge:	Existing work on long document visual question answering is based on Retrieval-Augmented Generation (RAG) where textual or visual content is encoded into embeddings and relevance is determined by similarity scores with respect to the original query.
Approach:	They propose a framework that employs an agentic, vision-aware workflow to address long document visual question answering through iterative information discovery and synthesis.
Outcome:	The proposed framework outperforms existing RL systems by 10.4% on the MMLongbench-Doc benchmark and demonstrates superior training performance over GRPO.

Boosting Textural NER with Synthetic Image and Instructive Alignment (2024.findings-acl)

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Jiahao Wang, Wenjun Ke, Peng Wang, Hang Zhang, Dong Nie, Jiajun Liu, Guozheng Li, Ziyu Shang

Metric-guided Distillation: Distilling Knowledge from the Metric to Ranker and Retriever for Generative Commonsense Reasoning (2022.emnlp-main)

Challenge:	Named entity recognition (NER) is a key task reliant on textual data.
Approach:	They propose a method to transform NER into a multimodal task by using images from the internet as auxiliaries.
Outcome:	The proposed method surpasses all text-only baselines and improves F1 score by 1.4% to 2.3% on prominent MNER datasets.

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Xingwei He, Yeyun Gong, A-Long Jin, Weizhen Qi, Hang Zhang, Jian Jiao, Bartuer Zhou, Biao Cheng, Sm Yiu, Nan Duan

RAPID: Efficient Retrieval-Augmented Long Text Generation with Writing Planning and Information Discovery (2025.findings-acl)

Challenge:	Existing work on commonsense generation requires models to have relational reasoning and compositional generalization capabilities.
Approach:	They propose a metric distillation rule to distill knowledge from a standard metric to a ranker and transfer it to re-ranking a retriever.
Outcome:	The proposed method surpasses the previous SOTA.

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Hongchao Gu, Dexun Li, Kuicai Dong, Hao Zhang, Hang Lv, Hao Wang, Defu Lian, Yong Liu, Enhong Chen

Is Compound Aspect-Based Sentiment Analysis Addressed by LLMs? (2024.findings-emnlp)

Challenge:	Existing methods for knowledge-intensive long texts struggle with issues like hallucinations, topic incoherence, and significant latency.
Approach:	They propose a retrieval-augmented long text generation framework with writing Planning and Information to address these challenges.
Outcome:	The proposed framework outperforms state-of-the-art methods on a freshWiki-2024 dataset.

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Yinhao Bai, Zhixin Han, Yuhua Zhao, Hang Gao, Zhuowei Zhang, Xunzhi Wang, Mengting Hu

SeaLLMs - Large Language Models for Southeast Asia (2024.acl-demos)

Challenge:	Aspect-based sentiment analysis (ABSA) aims to predict aspect-based elements from text . large language models (LLMs) have impressive abilities in handling human instructions .
Approach:	They propose a framework to evaluate LLMs' ability to handle complex ABSA tasks . they use constrained prompts to automatically organize the returned predictions .
Outcome:	The proposed framework outperforms supervised methods in some cases, but it is still lacking in other areas.

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Xuan-Phi Nguyen, Wenxuan Zhang, Xin Li, Mahani Aljunied, Zhiqiang Hu, Chenhui Shen, Yew Ken Chia, Xingxuan Li, Jianyu Wang, Qingyu Tan, Liying Cheng, Guanzheng Chen, Yue Deng, Sen Yang, Chaoqun Liu, Hang Zhang, Lidong Bing

Adaptive Schema-aware Event Extraction with Retrieval-Augmented Generation (2025.findings-emnlp)

Challenge:	Existing large language models favor high-resource languages, such as English, at the expense of low-resourced and regional languages.
Approach:	They propose a series of language models that specifically focuses on Southeast Asian languages.
Outcome:	SeaLLM models outperform ChatGPT-3.5 in non-Latin languages by large margins . linguistic disparity impedes access to state-of-the-art AI technologies for non-English-speaking populations .

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Sheng Liang, Hang Lv, Zhihao Wen, Yaxiong Wu, Yongyue Zhang, Hao Wang, Yong Liu

AMBERT: A Pre-trained Language Model with Multi-Grained Tokenization (2021.findings-acl)

Challenge:	Event extraction is a task in natural language processing that involves identifying and extracting event information from unstructured text.
Approach:	They propose a paradigm that combines schema paraphrasing with schema retrieval-augmented generation.
Outcome:	The proposed paradigm retrieves paraphrased schemas and accurately generates targeted structures.

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Xinsong Zhang, Pengshuai Li, Hang Li

GALLa: Graph Aligned Large Language Models for Improved Source Code Understanding (2025.acl-long)

Challenge:	Pre-trained language models such as BERT have shown great power in natural language understanding . fine-grained tokenizations have advantages and disadvantages for learning of pre-tried models .
Approach:	They propose a pretrained language model based on both fine-grained and coarse-grain tokenizations . they propose to use both tokenization techniques to learn pre-trained models .
Outcome:	The proposed model outperforms BERT on benchmark datasets for Chinese and English . it can perform better with the same computational cost as BERT, the authors show .

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Ziyin Zhang, Hang Yu, Sage Lee, Peng Di, Jianguo Li, Rui Wang

CAPSTONE: Curriculum Sampling for Dense Retrieval with Document Expansion (2023.emnlp-main)

Challenge:	Programming languages have rich semantics that are represented by graphs and not available from the surface form of source code.
Approach:	They propose to use graph neural networks and cross-modal alignment technologies to inject structural information of code into LLMs as an auxiliary task during finetuning.
Outcome:	The proposed framework improves on five code tasks with six different baseline LLMs, while incurring no cost at inference time.

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Xingwei He, Yeyun Gong, A-Long Jin, Hang Zhang, Anlei Dong, Jian Jiao, Siu Yiu, Nan Duan

Information Retrieval

Challenge:	Experimental results show that dense retrieval models are better at obtaining query-informed representations.
Approach:	They propose a dual-encoder approach that computes latent representations of query and document independently, but inference replaces the real query with a generated one.
Outcome:	The proposed approach outperforms previous dense retrieval models on in-domain and out-of-domain datasets.

Doc-V*: Coarse-to-Fine Interactive Visual Reasoning for Multi-Page Document VQA (2026.acl-long)

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Yuanlei Zheng, Pei Fu, Hang Li, Ziyang Wang, Yuyi Zhang, Wenyu Ruan, Xiaojin Zhang, Zhongyu Wei, Zhenbo Luo, Jian Luan, Wei Chen, Xiang Bai

PaSa: An LLM Agent for Comprehensive Academic Paper Search (2025.acl-long)

Challenge:	Existing OCR-free approaches to document visual question answering are brittle and passive.
Approach:	They propose an OCR-free agentic framework that casts multi-page DocVQA as sequential evidence aggregation.
Outcome:	The proposed framework outperforms open-source and proprietary models in five benchmarks and improves out-of-domain performance by 47.9% over baseline.

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Yichen He, Guanhua Huang, Peiyuan Feng, Yuan Lin, Yuchen Zhang, Hang Li, Weinan E

GA-S3: Comprehensive Social Network Simulation with Group Agents (2025.findings-acl)

Challenge:	We introduce PaSa, an advanced Paper Search agent powered by large language models . despite being trained on synthetic data, PaSA outperforms existing baselines on RealScholarQuery .
Approach:	They introduce PaSa, an advanced Paper Search agent powered by large language models . they optimize PaSA using a synthetic dataset, AutoScholarQuery, which includes 35k fine-grained queries .
Outcome:	The paper analyzes the performance of a paper search agent using a synthetic dataset . it significantly outperforms existing benchmarks on RealScholarQuery .

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Yunyao Zhang, Zikai Song, Hang Zhou, Wenfeng Ren, Yi-Ping Phoebe Chen, Junqing Yu, Wei Yang

Challenge:	Existing social network simulations focus on discrete events or system dynamics instead of elucidating underlying mechanisms or causal relationships.
Approach:	They propose a Social network simulation system that leverages newly designed Group Agents to make intelligent decisions regarding various online events.
Outcome:	The proposed system can make intelligent decisions regarding online events at a manageable cost.

CoLLiE: Collaborative Training of Large Language Models in an Efficient Way (2023.emnlp-demo)

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Kai Lv, Shuo Zhang, Tianle Gu, Shuhao Xing, Jiawei Hong, Keyu Chen, Xiaoran Liu, Yuqing Yang, Honglin Guo, Tengxiao Liu, Yu Sun, Qipeng Guo, Hang Yan, Xipeng Qiu

CodeRetriever: A Large Scale Contrastive Pre-Training Method for Code Search (2022.emnlp-main)

Challenge:	Large language models (LLMs) are increasingly pivotal in a wide range of tasks . however, the resources required for training these models necessitate efficient solutions .
Approach:	They propose a library that facilitates collaborative training of large language models . they use 3D parallelism, parameter-efficient fine-tuning methods and optimizers .
Outcome:	The proposed library has proven superior training efficiency in comparison with prevalent solutions in pre-training and fine-tuning scenarios.

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Xiaonan Li, Yeyun Gong, Yelong Shen, Xipeng Qiu, Hang Zhang, Bolun Yao, Weizhen Qi, Daxin Jiang, Weizhu Chen, Nan Duan

Intermediate Layer Distillation with the Reused Teacher Classifier: A Study on the Importance of the Classifier of Attention-based Models (2024.findings-emnlp)

Challenge:	Existing code pre-training approaches often adopt (masked) language modeling as the training objective which targets on learning to predict (macked) tokens in a given code context.
Approach:	They propose a code-text contrastive learning model which learns function-level code semantic representations through large-scale code corpus.
Outcome:	The proposed model achieves new state-of-the-art with significant improvement over existing pre-trained models on eleven domain/language-specific code search tasks with six programming languages in different code granularity.

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Hang Zhang, Seyyed Mozafari, James Clark, Brett Meyer, Warren Gross

Leveraging Large Language Models for Learning Complex Legal Concepts through Storytelling (2024.acl-long)

Challenge:	Existing methods underestimate the importance of utilizing the teacher's discriminative classifier and face challenges in establishing proper layer mappings.
Approach:	They propose to reuse pre-trained teacher classifiers to improve student performance . they use projectors to match hidden size of the teacher model to student .
Outcome:	The proposed method outperforms existing methods on 97.7% of the teacher BERT base without additional trainable parameters.

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Hang Jiang, Xiajie Zhang, Robert Mahari, Daniel Kessler, Eric Ma, Tal August, Irene Li, Alex Pentland, Yoon Kim, Deb Roy, Jad Kabbara

Spelling Error Correction with Soft-Masked BERT (2020.acl-main)

Challenge:	a novel application of large language models (LLMs) to legal education helps non-experts learn complex legal concepts . authors find storytelling helps nonexperts understand complex legal terms and concepts compared to definitions .
Approach:	They propose a novel application of large language models to legal education . they use LLMs to generate legal stories explaining complex legal concepts .
Outcome:	The proposed method improves comprehension and interest among non-native speakers compared to definitions . the novel method also shows that non-experts retain more stories .

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Shaohua Zhang, Haoran Huang, Jicong Liu, Hang Li

Embodied-Reasoner: Synergizing Visual Search, Reasoning, and Action for Embodied Interactive Tasks (2026.acl-long)

Challenge:	Experimental results show that the proposed method is significantly better than the baselines including the one solely based on BERT.
Approach:	They propose a neural architecture which uses a network for error detection and a system for error correction based on BERT, with the latter connected to the other using what they call soft-masking technique.
Outcome:	The proposed method performs better than baselines including the one solely based on BERT, and is general and may be employed in other language detection-correction problems.

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Wenqi Zhang, Mengna Wang, Gangao Liu, Huixin Xu, Yiwei Jiang, Yongliang Shen, Guiyang Hou, Zhe Zheng, Hang Zhang, Xin Li, Jiajun Liu, Weiming Lu, Peng Li, Yueting Zhuang

PersonaLLM: Investigating the Ability of Large Language Models to Express Personality Traits (2024.findings-naacl)

Challenge:	Recent advances in reasoning models have demonstrated remarkable capabilities on mathematical and coding tasks, but their effectiveness in embodied domains remains largely unexplored.
Approach:	They propose a reasoning model for interactive embodied tasks that synthesizes 9.3k coherent Observation-Thought-Action trajectories containing 64k ego-centric images and 90k diverse reasoning processes.
Outcome:	The proposed model outperforms existing visual reasoning models by +9%, 24%, and +13% on long-horizon tasks.

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Hang Jiang, Xiajie Zhang, Xubo Cao, Cynthia Breazeal, Deb Roy, Jad Kabbara

Free-MAD: Consensus-Free Multi-Agent Debate (2026.findings-acl)

Challenge:	Recent studies have shown that LLMs can generate content that aligns with their assigned personality traits, but there is limited research on whether they consistently reflect specific personality traits.
Approach:	They propose to study the behavior of LLM-based agents which they refer to as LLM personas and simulate them to measure their personality traits.
Outcome:	The proposed model is based on the Big Five personality model and has been validated by human evaluations and automatic evaluations.

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Yu Cui, Hang Fu, Haibin Zhang, Licheng Wang, Cong Zuo

What Really Matters for Table LLMs? A Meta-Evaluation of Model and Data Effects (2026.findings-eacl)

Challenge:	Existing multi-agent debate methods rely on multiple rounds of interaction among agents to reach consensus, and the final output is decided by majority voting in the last round.
Approach:	They propose a multi-agent debate framework that eliminates the need for consensus among agents and reconstructs the debate phase by introducing anti-conformity.
Outcome:	Experiments on eight benchmark datasets show that Free-MAD significantly improves reasoning performance while requiring only a single-round debate and thus reducing token costs.

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Naihao Deng, Sheng Zhang, Henghui Zhu, Shuaichen Chang, Jiani Zhang, Alexander Hanbo Li, Chung-Wei Hang, Hideo Kobayashi, Yiqun Hu, Patrick Ng