Papers by Hang Li

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

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

Visual-Language Navigation Pretraining via Prompt-based Environmental Self-exploration (2022.acl-long)

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

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Xiwen Liang, Fengda Zhu, Li Lingling, Hang Xu, Xiaodan Liang

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

Challenge:	Existing methods of fine-tuning vision-language navigation models require extra human-labeled data and lack self-exploration capabilities in environments.
Approach:	They propose a method that can self-explore environments without human labeling . they use a large-scale cross-modal pretrained model to build an in-domain dataset .
Outcome:	The proposed model can self-explore environments without human labeling without human supervision and generates structured instructions without human intervention.

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Hang Zhang, Xin Li, Lidong Bing

Ask-Before-Detection: Identifying and Mitigating Conformity Bias in LLM-Powered Error Detector for Math Word Problem Solutions (2025.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 Li, Tianlong Xu, Kaiqi Yang, Yucheng Chu, Yanling Chen, Yichi Song, Qingsong Wen, Hui Liu

Numerical Reasoning

Challenge:	Recent studies have demonstrated the potential of large language models (LLMs) for automatic error detection in math word problems (MWPs).
Approach:	They propose a framework that generates adaptive reference solutions using LLMs to enhance error detection by reducing conformity bias in MWPs.
Outcome:	The proposed framework mitigates the performance gap between conventional and alternative solutions in MWPs, especially when combined with reasoning-enhancing techniques like chain-of-thought prompting.

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

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

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

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

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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.

CoBa: Convergence Balancer for Multitask Finetuning of Large Language Models (2024.emnlp-main)

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Zi Gong, Hang Yu, Cong Liao, Bingchang Liu, Chaoyu Chen, Jianguo Li

A Neural-Symbolic Approach to Natural Language Understanding (2022.findings-emnlp)

Challenge:	Existing multi-task learning approaches for large language models fall short due to computational intensive or lack of simultaneous task convergence.
Approach:	They propose a new multi-task learning approach that dynamically adjusts task weights during the training process, ensuring that the validation loss of all tasks progresses towards convergence at an even pace.
Outcome:	The proposed approach improves the performance of large language models by up to 13% compared to the second-best approaches.

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Zhixuan Liu, Zihao Wang, Yuan Lin, Hang Li

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

Challenge:	Pre-trained language models have enabled deep neural networks to perform natural language understanding tasks, but their performance can drastically deteriorate when logical reasoning is needed.
Approach:	They propose a framework for NLU based on analogical reasoning based upon neural processing and logical reasoning using both neural and symbolic processing.
Outcome:	The proposed framework outperforms state-of-the-art methods on two NLU tasks, question answering (QA) and natural language inference (NLI).

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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

A Sequence-to-Sequence Approach to Dialogue State Tracking (2021.acl-long)

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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Yue Feng, Yang Wang, Hang Li

RankPrompt: Step-by-Step Comparisons Make Language Models Better Reasoners (2024.lrec-main)

Challenge:	Existing methods for dialogue state tracking are still challenging, but they are improving . a new approach to dialogue state monitoring is proposed, called Seq2Seq-DU .
Approach:	They propose a new dialogue state tracking module that formalizes DST as a sequence-to-sequence problem.
Outcome:	The proposed method outperforms existing methods on benchmark datasets in different settings.

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Chi Hu, Yuan Ge, Xiangnan Ma, Hang Cao, Qiang Li, Yonghua Yang, Tong Xiao, Jingbo Zhu

Dialogue Systems & Conversational Agents

Challenge:	Existing solutions to reasoning tasks require extensive human annotations or fail in scenarios with inconsistent responses.
Approach:	They propose a new method that enables LLMs to self-rank their responses without additional resources.
Outcome:	The proposed method improves reasoning performance of ChatGPT and GPT-4 with 13% improvement over existing methods.

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

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

Sentiment Analysis

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.

An Embarrassingly Easy but Strong Baseline for Nested Named Entity Recognition (2023.acl-short)

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Hang Yan, Yu Sun, Xiaonan Li, Xipeng Qiu

Information Extraction & Text Mining

Challenge:	Named entity recognition (NER) is the task to detect and classify entity spans in text.
Approach:	They propose to use Convolutional Neural Network to model spatial relations in NER . they use three commonly used nested NER datasets to compare their results .
Outcome:	The proposed model outperforms several proposed methods with the same pre-trained encoders in three nested NER datasets.

Turn Waste into Worth: Rectifying Top-k Router of MoE (2024.emnlp-main)

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Zhiyuan Zeng, Qipeng Guo, Zhaoye Fei, Zhangyue Yin, Yunhua Zhou, Linyang Li, Tianxiang Sun, Hang Yan, Dahua Lin, Xipeng Qiu

Green & Sustainable NLP

Challenge:	Top-k router suffers from redundancy computation and memory costs due to unbalanced routing . some experts are overflow, where exceeding tokens are dropped, while others are empty, which are padded with zeros, negatively impacting model performance.
Approach:	They propose a top-k router that is unbalanced and uses a multi-gPU system to handle dropped tokens and padding.
Outcome:	The proposed model surpasses the top-1 router by 4.7% in terms of performance . the top-k router suffers from redundancy computation and memory costs .

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

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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

Watermarking LLMs with Weight Quantization (2023.findings-emnlp)

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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Linyang Li, Botian Jiang, Pengyu Wang, Ke Ren, Hang Yan, Xipeng Qiu

Noise Stability Regularization for Improving BERT Fine-tuning (2021.naacl-main)

Challenge:	Large language models are being deployed at an astonishing speed, exposing users to high risks.
Approach:	They propose a method that plants watermarks in quantization process of large language models without pre-defined triggers during inference.
Outcome:	The proposed method protects model weights without pre-defined triggers . it works when the model is used in the fp32 mode and remains hidden when the models are quantized to int8 .

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Hang Hua, Xingjian Li, Dejing Dou, Chengzhong Xu, Jiebo Luo

TAG: Gradient Attack on Transformer-based Language Models (2021.findings-emnlp)

Challenge:	Recent studies show that fine-tuning pre-trained language models is unstable when there are only a small number of training samples available.
Approach:	They propose to use a method to regularize noise in deep nets to improve fine-tuning on NLP tasks.
Outcome:	The proposed method improves fine-tuning on natural language processing tasks by incorporating noise to the input and demonstrating generalizability and stability.

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Jieren Deng, Yijue Wang, Ji Li, Chenghong Wang, Chao Shang, Hang Liu, Sanguthevar Rajasekaran, Caiwen Ding

Hybrid Alignment Training for Large Language Models (2024.findings-acl)

Challenge:	Recent studies show that publicly shared gradients in the training process can reveal the private training data to a third-party.
Approach:	They propose a gradient attack algorithm to reconstruct the local training data using GLUE benchmarks.
Outcome:	The proposed algorithm achieves 1.5x recover rate and 2.5x ROUGE-2 over previous methods without the need of ground truth label.

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Chenglong Wang, Hang Zhou, Kaiyan Chang, Bei Li, Yongyu Mu, Tong Xiao, Tongran Liu, JingBo Zhu

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

Challenge:	Existing approaches to align large language models with instructions and preferences are conflicting . et al., 2023b) show that hybrid alignment training can outperform baselines .
Approach:	They propose a hybrid alignment training approach based on alternating alignment and modified elastic weight consolidation methods to achieve better collaboration between different alignment tasks.
Outcome:	The proposed approach outperforms baseline alignment training methods on summarization and dialogue tasks.

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

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

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

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

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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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

Advancing Fine-Grained Visual Understanding with Multi-Scale Alignment in Multi-Modal Models (2025.emnlp-main)

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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Wei Wang, Zhaowei Li, Qi Xu, Linfeng Li, YiQing Cai, Botian Jiang, Hang Song, Xingcan Hu, Pengyu Wang, Li Xiao

Are Large Language Models (LLMs) Good Social Predictors? (2024.findings-emnlp)

Challenge:	Recent advances in multi-modal large language models have demonstrated remarkable capabilities in multimodal understanding, reasoning, and interaction.
Approach:	They propose a method that effectively aligns and integrates multi-scale knowledge of objects . they use a pipeline that provides over 300K essential training data to enhance alignment .
Outcome:	The proposed method effectively aligns and integrates multi-scale knowledge of objects, including texts, coordinates, and images.

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Kaiqi Yang, Hang Li, Hongzhi Wen, Tai-Quan Peng, Jiliang Tang, Hui Liu

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

Challenge:	Existing studies suggest that Large Language Models can generate human-like responses, but it is unclear how well they work and where the plausible predictions derive from.
Approach:	They propose to use LLMs to generate human-like responses by mutability and accessibility of social inputs to perform a social prediction task.
Outcome:	The proposed model performs well in three realistic settings and a novel social prediction task.

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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

DRAGON: Domain-specific Robust Automatic Data Generation for RAG Optimization (2026.findings-eacl)

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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Haiyang Shen, Hang Yan, Zhongshi Xing, Mugeng Liu, Yue Li, Zhiyang Chen, Yuxiang Wang, Jiuzheng Wang, Yun Ma

Robustness in NLP

Challenge:	Existing retrieval-augmented generation paradigms rely heavily on public knowledge . Existing RAGs reliant on public information and often falter when faced with domain-specific queries.
Approach:	They propose a framework that combines a data-construction modeling approach with a scalable synthetic data-generation pipeline to optimize domain-specific retrieval performance.
Outcome:	The proposed framework optimizes domain-specific retrieval performance and bolsters retriever robustness.

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

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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.

Enhancing Transformers for Generalizable First-Order Logical Entailment (2025.acl-long)

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Tianshi Zheng, Jiazheng Wang, Zihao Wang, Jiaxin Bai, Hang Yin, Zheye Deng, Yangqiu Song, Jianxin Li

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

Challenge:	Moreover, transformers have demonstrated proficiency in logical reasoning over natural language.
Approach:	They propose a logic-aware architecture that improves the performance in generalizable first-order logical entailment by combining distribution shifts and unseen knowledge.
Outcome:	The proposed architecture outperforms methods designed specifically for knowledge graph query answering on a dataset with a large dataset.

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Xueqing Wu, Jiacheng Zhang, Hang Li

Evaluating Document Coherence Modeling (2021.tacl-1)

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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Aili Shen, Meladel Mistica, Bahar Salehi, Hang Li, Timothy Baldwin, Jianzhong Qi

Accelerating BERT Inference for Sequence Labeling via Early-Exit (2021.acl-long)

Challenge:	a new study examines pretrained language models' ability to model discourse and pragmatic phenomena.
Approach:	They propose a sentence intrusion detection task using a dictionary dataset . they show that pretrained LMs perform impressively in in-domain evaluation .
Outcome:	The proposed dataset shows that pretrained LMs perform impressively in in-domain evaluation, but experience a substantial drop in the cross-domain setting, indicating limited generalization capacity.

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Xiaonan Li, Yunfan Shao, Tianxiang Sun, Hang Yan, Xipeng Qiu, Xuanjing Huang

Learning from Emptiness: De-biasing Listwise Rerankers with Content-Agnostic Probability Calibration (2026.acl-short)

Challenge:	Existing early-exit mechanisms are designed for sequence-level tasks, rather than sequence labeling.
Approach:	They propose to extend sentence-level early-exit to accelerate inference of PTMs . they propose a token-level mechanism that allows partial tokens to exit early at different layers .
Outcome:	The proposed approach can save up to 66%75% inference cost with minimal performance degradation.

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Hang Lv, Hongchao Gu, Ruiqing Yang, Liangyue Li, Zulong Chen, Defu Lian, Hao Wang, Enhong Chen

MLeVLM: Improve Multi-level Progressive Capabilities based on Multimodal Large Language Model for Medical Visual Question Answering (2024.findings-acl)

Challenge:	Existing methods for listwise reranking exhibit intrinsic position bias . existing methods are constrained by an inherent trade-off between efficiency and flexibility .
Approach:	They propose a training-free framework that mechanically decouples positional bias from ranking decisions.
Outcome:	a training-free framework decouples position bias from ranking decisions . evaluations show it outperforms training-based methods and outperformed expensive methods .

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Dexuan Xu, Yanyuan Chen, Jieyi Wang, Yue Huang, Hanpin Wang, Zhi Jin, Hongxing Wang, Weihua Yue, Jing He, Hang Li, Yu Huang

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

Challenge:	Existing MVQA models ignore multi-level progressive capabilities due to unspecific data and plain architecture.
Approach:	They propose a multi-level visual language model for medical visual question answering (MVQA) which covers multi- level questions and answers as well as reasoning processes from visual clues to semantic cognition.
Outcome:	The proposed model outperforms existing medical multimodal large language models on a multi-level instruction dataset and a feature alignment module.

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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.

Secoco: Self-Correcting Encoding for Neural Machine Translation (2021.findings-emnlp)

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Tao Wang, Chengqi Zhao, Mingxuan Wang, Lei Li, Hang Li, Deyi Xiong

Multilinguality

Challenge:	Neural machine translation (NMT) is a challenging field due to the wide variety of noises in real-world scenarios.
Approach:	They propose a framework that explicitly deals with noisy inputs for robust neural machine translation by introducing self-correcting predictors.
Outcome:	The proposed framework can correct noisy inputs and delete specific errors with the translation decoding process.

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

Mathematical Word Problem Generation from Commonsense Knowledge Graph and Equations (2021.emnlp-main)

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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Tianqiao Liu, Qiang Fang, Wenbiao Ding, Hang Li, Zhongqin Wu, Zitao Liu

F-Eval: Asssessing Fundamental Abilities with Refined Evaluation Methods (2024.acl-long)

Challenge:	Existing models for generating mathematical word problems are lacking in educational assessment.
Approach:	They propose an end-to-end neural model to generate diverse mathematical word problems from commonsense knowledge graph and equations.
Outcome:	The proposed model outperforms the SOTA models in terms of evaluation metrics and topic relevance.

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Yu Sun, Keyuchen Keyuchen, Shujie Wang, Peiji Li, Qipeng Guo, Hang Yan, Xipeng Qiu, Xuanjing Huang, Dahua Lin

Challenge:	Large language models (LLMs) have been evaluated for their instruction-following capabilities but lack references to their fundamental abilities.
Approach:	They propose a bilingual evaluation benchmark to evaluate the fundamental abilities of large language models including expression, commonsense and logic.
Outcome:	The proposed evaluation methods show higher correlation coefficients and larger distinction than other evaluators.

Red Teaming Large Reasoning Models (2026.acl-long)

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Jiawei Chen, Yang Yang, Chao Yu, Yu Tian, Zhi Cao, Xue Yang, Linghao Li, Hang Su, Zhaoxia Yin

LNN-EL: A Neuro-Symbolic Approach to Short-text Entity Linking (2021.acl-long)

Challenge:	Large Reasoning Models (LRMs) have emerged as a powerful advancement in multi-step reasoning tasks, but they introduce safety and reliability risks, such as CoT-hijacking and prompt-induced inefficiencies.
Approach:	They propose a unified benchmark to assess the trustworthiness of Large Reasoning Models.
Outcome:	The proposed benchmark evaluates truthfulness, safety and efficiency on 26 models.

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Hang Jiang, Sairam Gurajada, Qiuhao Lu, Sumit Neelam, Lucian Popa, Prithviraj Sen, Yunyao Li, Alexander Gray

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

Challenge:	Existing work deals with EL in the context of longer text, such as a sentence.
Approach:	They propose a neuro-symbolic approach that uses interpretable rules based on first-order logic to achieve better performance with black-box neural approaches.
Outcome:	The proposed approach achieves better performance than heuristics-based approaches on short-text EL . it can easily blend existing rule templates with multiple types of features, and even with scores resulting from previous EL methods.

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

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

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

Fact-based Text Editing (2020.acl-main)

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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Hayate Iso, Chao Qiao, Hang Li

COSIGN: Contextual Facts Guided Generation for Knowledge Graph Completion (2024.naacl-long)

Challenge:	Existing methods for fact-based text editing are not suitable for all situations.
Approach:	They propose a method for automatically generating a dataset with a draft text, revised text, and several facts represented in triples.
Outcome:	The proposed method outperforms the encoder-decoder approach on two datasets and shows that it conducts inference faster than the encoded-decoding approach.

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Jinpeng Li, Hang Yu, Xiangfeng Luo, Qian Liu

Structured Data in NLP

Challenge:	Existing methods for knowledge graph completion (KGC) are limited in generality and scalability due to poor contextual facts.
Approach:	They propose a contextual facts collector and contextual facts organizer to enhance the inference ability of GM-based methods for various KGC tasks.
Outcome:	The proposed model outperforms state-of-the-art methods in terms of performance.

Paraphrase Generation with Deep Reinforcement Learning (D18-1)

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Zichao Li, Xin Jiang, Lifeng Shang, Hang Li

Text Generation

Challenge:	Paraphrase generation is an important but challenging task in natural language processing . traditional symbolic approaches to paraphrase generation include rule-based methods, thesaurus-based approaches and statistical machine translation (SMT)
Approach:	They propose a deep reinforcement learning approach to automatic paraphrase generation . they propose supervised learning and reinforcement learning for evaluators .
Outcome:	The proposed framework outperforms state-of-the-art methods in paraphrase generation on two datasets.

ZeroGen: Efficient Zero-shot Learning via Dataset Generation (2022.emnlp-main)

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Jiacheng Ye, Jiahui Gao, Qintong Li, Hang Xu, Jiangtao Feng, Zhiyong Wu, Tao Yu, Lingpeng Kong

HSCodeComp: A Realistic and Expert-level Agent Benchmark for Hierarchical Rule Application (2026.acl-long)

Challenge:	Existing approaches to generate training data with pre-trained language models have been found effective in various scenarios.
Approach:	They propose an unsupervised zero-shot learning method that generates a dataset from scratch and trains a tiny task model under supervision of the synthesized dataset.
Outcome:	The proposed method is annotated-free and efficient, but can provide useful insights from the perspective of data-free model-agnostic knowledge distillation and unreferenced text generation evaluation.

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Tian Lan, Yiqian Yang, Qianghuai Jia, Li Zhu, Hui Jiang, Hang Zhu, Weihua Luo, Longyue Wang

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

Challenge:	Existing agent benchmarks neglect hierarchical rule application in real-world domains . a critical gap persists in numerous real-life professional domains where decision-making is governed by expert-written rules.
Approach:	They propose a benchmark requiring agents to assign a unique 10-digit Harmonized System (HS) Code to products by aligning their fuzzy attributes with strict tariff classification rules.
Outcome:	The proposed benchmarks lack hierarchical rule application capability in real-world domains . the proposed benchmark is based on e-commerce and is open-source .

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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

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

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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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

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

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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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

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

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

Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetune Paradigm (2022.acl-long)

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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Shaoyi Huang, Dongkuan Xu, Ian Yen, Yijue Wang, Sung-En Chang, Bingbing Li, Shiyang Chen, Mimi Xie, Sanguthevar Rajasekaran, Hang Liu, Caiwen Ding

Unified Active Retrieval for Retrieval Augmented Generation (2024.findings-emnlp)

Challenge:	Conventional wisdom in pruning Transformer-based language models is that it reduces model expressiveness, but new research shows pruning increases risk of overfitting when performed at the fine-tuning phase.
Approach:	They propose to reduce pruning risk under pretrain-and-finetune paradigm . they propose to use knowledge distillation to improve pruning performance .
Outcome:	The proposed method outperforms the leading competitors on the GLUE benchmark.

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Qinyuan Cheng, Xiaonan Li, Shimin Li, Qin Zhu, Zhangyue Yin, Yunfan Shao, Linyang Li, Tianxiang Sun, Hang Yan, Xipeng Qiu

Information Retrieval

Challenge:	Existing active retrieval methods struggle with handling various types of instructions.
Approach:	They propose a unified active retrieval framework for retrieval-augmented generation . they propose to combine four orthogonal criteria into plug-and-play classification tasks .
Outcome:	The proposed framework outperforms existing methods on four representative types of user instructions on four types of instructions.

Evaluating Large Language Models on Wikipedia-Style Survey Generation (2024.findings-acl)

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Fan Gao, Hang Jiang, Rui Yang, Qingcheng Zeng, Jinghui Lu, Moritz Blum, Tianwei She, Yuang Jiang, Irene Li

Getting More Juice Out of Your Data: Hard Pair Refinement Enhances Visual-Language Models Without Extra Data (2025.naacl-long)

Challenge:	Recent studies have shown that large language models can perform well in general tasks, but their effectiveness and limitations in domainspecific tasks remain unclear.
Approach:	They examine the proficiency of Large Language Models (LLMs) in generating succinct survey articles specific to the niche field of NLP in computer science.
Outcome:	The LLMs perform better in generating succinct survey articles specific to the niche field of NLP in computer science, compared to human-authored surveys, but they exhibit bias in evaluation.

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Haonan Wang, Minbin Huang, Runhui Huang, Lanqing Hong, Hang Xu, Tianyang Hu, Xiaodan Liang, Zhenguo Li, Hong Cheng, Kenji Kawaguchi

UTC-IE: A Unified Token-pair Classification Architecture for Information Extraction (2023.acl-long)

Challenge:	Contrastive Language-Image Pre-training (CLIP) is a standard for cross-modal image-text representation learning.
Approach:	They propose a framework that enhances pre-trained CLIP models by exploiting challenging text-image pairs within existing datasets.
Outcome:	The proposed framework improves CLIP models by exploiting text-image pairs in training.

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Hang Yan, Yu Sun, Xiaonan Li, Yunhua Zhou, Xuanjing Huang, Xipeng Qiu

Information Extraction & Text Mining

Challenge:	Information Extraction (IE) tasks have been solved with different models because of their output structures.
Approach:	They propose a Unified Token-pair Classification architecture for Information Extraction that introduces Plusformer on top of the token-pear feature matrix.
Outcome:	The proposed approach outperforms task-specific and unified models on all tasks in 10 datasets and achieves better results on 2 joint IE datasets.

EviReport: From Reasoned Outlines to Evidence Tracked Long-Form Reports (2026.findings-acl)

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Zihan Liu, Jianhui li, Zexin Wang, Fei Sun, Jingjing LI, Zheyuan Li, Ke Xiang, Hang Cui, Houhua Gong, Changhua Pei, Gaogang Xie

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

Challenge:	Evidence-intensive reports often produce fluent but under-supported drafts . eviReport is an evidence-grounded workflow for automated long-form report generation .
Approach:	They propose an evidence-tracked workflow that organizes corpus evidence into compact, traceable units and retrieves query-relevant subgraphs into retrieval-ready packages.
Outcome:	The proposed workflow outperforms baselines in factual coverage, factual accuracy and visual evidence integration.

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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

Case2Code: Scalable Synthetic Data for Code Generation (2025.coling-main)

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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Yunfan Shao, Linyang Li, Yichuan Ma, Peiji Li, Demin Song, Qinyuan Cheng, Shimin Li, Xiaonan Li, Pengyu Wang, Qipeng Guo, Hang Yan, Xipeng Qiu, Xuanjing Huang, Dahua Lin

Programming Languages in NLP

Challenge:	Large Language Models (LLMs) have shown outstanding breakthroughs in code generation.
Approach:	They propose a case-to-code induction task that exploits the expressiveness and correctness of programs by incorporating LLMs into their training.
Outcome:	The proposed task improves distribution case-to-code induction and various coding generation tasks.

QCRD: Quality-guided Contrastive Rationale Distillation for Large Language Models (2025.emnlp-main)

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Wei Wang, Zhaowei Li, Qi Xu, YiQing Cai, Hang Song, Qi Qi, Ran Zhou, Zhida Huang, Tao Wang, Li Xiao

CodeIE: Large Code Generation Models are Better Few-Shot Information Extractors (2023.acl-long)

Challenge:	Recent research has focused on smaller, task-specific models enhanced by distilling knowledge from LLMs, but the diversity and quality of negative knowledge remains understudied.
Approach:	They propose a quality-guided contrastive rationale distillation framework that aims to enhance reasoning capabilities through contrastive knowledge learning.
Outcome:	The proposed method consistently outperforms existing distillation techniques yielding higher-quality rationales.

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Peng Li, Tianxiang Sun, Qiong Tang, Hang Yan, Yuanbin Wu, Xuanjing Huang, Xipeng Qiu

CTAL: Pre-training Cross-modal Transformer for Audio-and-Language Representations (2021.emnlp-main)

Challenge:	Large language models pre-trained on massive corpora have shown impressive few-shot learning ability on many NLP tasks.
Approach:	They propose to recast structured output in the form of code instead of natural language and use generative LLMs of code to perform IE tasks.
Outcome:	The proposed method outperforms fine-tuning moderate-size pre-trained models and prompting NL-LLMs under few-shot settings.

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Hang Li, Wenbiao Ding, Yu Kang, Tianqiao Liu, Zhongqin Wu, Zitao Liu

FLAT: Chinese NER Using Flat-Lattice Transformer (2020.acl-main)

Challenge:	Existing audio-language task-specific predictive approaches focus on building complicated late-fusion mechanisms.
Approach:	They propose a cross-modal transformer for audio-and-language that learns inter-modal connections between audio and language through two proxy tasks on a large amount of audio- and-language pairs.
Outcome:	The proposed model improves on multiple audio-and-language tasks and can be used in fine-tuning phase.

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Xiaonan Li, Hang Yan, Xipeng Qiu, Xuanjing Huang

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

Challenge:	Named entity recognition (NER) models are difficult to use because of the complex nature of the lattice structure and the low inference speed.
Approach:	They propose a character-word lattice structure that converts lattics into flat structures consisting of spans.
Outcome:	The proposed model outperforms other lexicon-based models on four datasets and is highly parallel.

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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