Papers by Lei Wu

The JDDC Corpus: A Large-Scale Multi-Turn Chinese Dialogue Dataset for E-commerce Customer Service (2020.lrec-1)

Challenge:	Large Audio Language Models (LALMs) exhibit a degradation in knowledge and reasoning capabilities . empirical results show that CORD significantly bridges the audio–text performance gap .
Approach:	They propose a framework that performs online cross-modal self-distillation to bridge the acoustic-semantic gap between LALMs and text-based models.
Outcome:	The proposed framework bridges the acoustic-semantic gap between LALMs and text-based models . it employs on-policy reverse KL divergence with importance-aware weighting .

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Meng Chen, Ruixue Liu, Lei Shen, Shaozu Yuan, Jingyan Zhou, Youzheng Wu, Xiaodong He, Bowen Zhou

Natural Language Interfaces

Challenge:	Existing datasets for human-like dialogue tasks are deficient due to the complexity of human conversations.
Approach:	They construct a large-scale Chinese E-commerce conversation corpus with 1 million dialogues, 20 million utterances, and 150 million words.
Outcome:	The proposed dataset includes 1 million multi-turn dialogues, 20 million utterances, and 150 million words.

MSEarth: A Multimodal Benchmark for Earth Science Phenomenon Discovery with MLLMs (2026.acl-long)

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Xiangyu Zhao, Wanghan Xu, Bo Liu, Yuhao Zhou, Fenghua Ling, Ben Fei, Xiaoyu Yue, Lei Bai, Wenlong Zhang, Xiao-Ming Wu

ESGenius: Benchmarking LLMs on Environmental, Social, and Governance (ESG) and Sustainability Knowledge (2025.emnlp-main)

Challenge:	Existing datasets often rely on synthetic data or figure-caption pairs, failing to capture the depth and complexity of geoscientific reasoning.
Approach:	They propose a multimodal scientific dataset and benchmark curated from open-access publications.
Outcome:	MSEarth features over 289K figures with captions enriched by contextual discussions and reasoning from original papers.

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Chaoyue He, Xin Zhou, Yi Wu, Xinjia Yu, Yan Zhang, Lei Zhang, Di Wang, Shengfei Lyu, Hong Xu, Wang Xiaoqiao, Wei Liu, Chunyan Miao

Stimulate the Critical Thinking of LLMs via Debiasing Discussion (2025.emnlp-main)

Challenge:	ESGenius is a comprehensive benchmark for evaluating Large Language Models on ESG and sustainability knowledge.
Approach:	They introduce ESGenius, a benchmark for evaluating and enhancing ESG proficiency . they use a rigorous two-stage evaluation protocol and a repository of foundational frameworks .
Outcome:	ESGenius is a benchmark for evaluating and enhancing the proficiency of Large Language Models (LLMs) in ESG and sustainability-focused question answering.

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Ruiyu Xiao, Lei Wu, Yuanxing Liu, Weinan Zhang, Ting Liu

Robustness in NLP

Challenge:	Existing studies show that large language models (LLMs) are often prone to stance homogeneity and human preference biases when faced with conflicting perspectives.
Approach:	They propose a novel two-stage training framework to address stance homogeneity bias and human preference bias by generating multi-model discussion datasets and optimizing reinforcement learning from human feedback to align with discussion correctness.
Outcome:	The proposed framework reduces stance homogeneity bias and human preference bias and improves generalization capabilities on non-discussion scenarios and out-of-domain datasets.

Learning Geometry-Aware Representations for New Intent Discovery (2024.acl-long)

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Kai Tang, Junbo Zhao, Xiao Ding, Runze Wu, Lei Feng, Gang Chen, Haobo Wang

ZoFia: Zero-Shot Fake News Detection with Entity-Guided Retrieval and Multi-LLM Interaction (2026.findings-acl)

Challenge:	Existing methods for intent classification fail to distinguish new intents due to intertwined centers . a novel framework that learns geometry-aware representations to maximally separate all intents is proposed .
Approach:	They propose a new intent discovery framework that learns geometry-aware representations to maximally separate all intents.
Outcome:	The proposed framework achieves a new state-of-the-art performance on three benchmarking datasets.

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Lvhua Wu, Xuefeng Jiang, Sheng Sun, Yan Lei, Tian Wen, Yuwei Wang, Min Liu

Unsupervised Multi-scale Expressive Speaking Style Modeling with Hierarchical Context Information for Audiobook Speech Synthesis (2022.coling-1)

Challenge:	Large language models (LLMs) are limited by knowledge cutoff and can generate factual hallucinations when handling time-sensitive news.
Approach:	They propose a two-stage zero-shot fake news detection framework that uses a hierarchical salience and saliency-calibrated minimum margin of relevance algorithm to extract core entities accurately.
Outcome:	The proposed framework outperforms existing zero-shot baselines and even most few-shot methods on two public datasets.

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Xueyuan Chen, Shun Lei, Zhiyong Wu, Dong Xu, Weifeng Zhao, Helen Meng

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

Challenge:	a recent study has shown that expressiveness of audiobooks is limited by the averaged global-scale speaking style representation.
Approach:	They propose an unsupervised multi-scale context-sensitive text-to-speech model for audiobooks . they use hierarchical context encoder to predict global-scale contextual style embeddings .
Outcome:	The proposed model outperforms existing models on a real-world Mandarin audio dataset.

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Zehui Lin, Liwei Wu, Mingxuan Wang, Lei Li

PLATO-Ad: A Unified Advertisement Text Generation Framework with Multi-Task Prompt Learning (2022.emnlp-industry)

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

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Zeyang Lei, Chao Zhang, Xinchao Xu, Wenquan Wu, Zheng-yu Niu, Hua Wu, Haifeng Wang, Yi Yang, Shuanglong Li

Extrapolating Multilingual Understanding Models as Multilingual Generators (2023.findings-emnlp)

Challenge:	Online advertisement text generation models have achieved remarkable success in generating high-quality text ads, but some challenges remain, such as low-resource scenarios and training efficiency for multiple ad tasks.
Approach:	They propose a unified text ad generation framework with multi-task prompt learning to tackle low-resource ade generation problem and a multi-step prompt learning mechanism to efficiently solve multiple aed generation tasks.
Outcome:	The proposed framework outperforms the state-of-the-art on offline and online metrics.

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Bohong Wu, Fei Yuan, Hai Zhao, Lei Li, Jingjing Xu

What Does Infect Mean to Cardio? Investigating the Role of Clinical Specialty Data in Medical LLMs (2026.eacl-long)

Challenge:	Existing multilingual understanding models are not capable of generating high-quality text compared with decoder-based causal language models.
Approach:	They propose a method to adapt a multilingual encoder to a language generator with a small number of additional parameters.
Outcome:	The proposed approach outperforms initialization-based methods with 9.4 BLEU on machine translation, 8.1 Rouge-L on question generation, and 5.5 METEOR on story generation.

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Xinlan Yan, Di Wu, Yibin Lei, Christof Monz, Iacer Calixto

Question Answering

Challenge:	S-MedQA is an English question-answering dataset designed for benchmarking large language models in fine-grained clinical specialties.
Approach:	They propose to use an English medical question-answering dataset to benchmark large language models in clinical specialties.
Outcome:	The proposed dataset is designed to benchmark large language models in medical specialties.

Inflated Excellence or True Performance? Rethinking Medical Diagnostic Benchmarks with Dynamic Evaluation (2026.acl-long)

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Xiangxu Zhang, Lei Li, Yanyun Zhou, Xiao Zhou, Yingying Zhang, Xian Wu

Challenge:	Current evaluations of large language models (LLMs) are limited in capturing key challenges of clinical diagnostic scenarios.
Approach:	They propose a dynamic benchmark for medical diagnostics that provides a stress test of diagnostic robustness.
Outcome:	The proposed model provides a stress test of diagnostic robustness and veracity, helpfulness and consistency.

Hence, Socrates is mortal: A Benchmark for Natural Language Syllogistic Reasoning (2023.findings-acl)

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Yongkang Wu, Meng Han, Yutao Zhu, Lei Li, Xinyu Zhang, Ruofei Lai, Xiaoguang Li, Yuanhang Ren, Zhicheng Dou, Zhao Cao

Can We Edit Factual Knowledge by In-Context Learning? (2023.emnlp-main)

Challenge:	SylloBase is a benchmark for syllogistic reasoning, a critical capability widely required in natural language understanding tasks, such as text entailment and question answering.
Approach:	They propose to use a benchmark to learn syllogistic reasoning on a set of templates and to use them to generate and understand slogisms.
Outcome:	The proposed benchmark covers a complete taxonomy of syllogism reasoning patterns, and contains both automatically and manually constructed samples.

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Ce Zheng, Lei Li, Qingxiu Dong, Yuxuan Fan, Zhiyong Wu, Jingjing Xu, Baobao Chang

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

Challenge:	In-context knowledge editing (IKE) is a new paradigm for NLP research that can be applied to large language models with tens or hundreds of parameters.
Approach:	They propose to use in-context knowledge editing (IKE) without gradient updating to edit factual knowledge without a gradient update.
Outcome:	The proposed method achieves a competitive success rate compared to gradient-based methods on GPT-J but with fewer side effects.

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Junbo Niu, Zheng Liu, Zhuangcheng Gu, Bin Wang, Linke Ouyang, Zhiyuan Zhao, Tao Chu, Tianyao He, Fan Wu, Qintong Zhang, Zhenjiang Jin, Guang Liang, Rui Zhang, Wenzheng Zhang, Yuan Qu, Zhifei Ren, Yuefeng Sun, Zirui Tang, Boyu Niu, Yuanhong Zheng, Dongsheng Ma, Ziyang Miao, Hejun Dong, Siyi Qian, Junyuan Zhang, Fangdong Wang, Jingzhou Chen, Xiaomeng Zhao, Liqun Wei, Wei Li, Shasha Wang, RuiLiang Xu, Yuanyuan Cao, Lu Chen, Qianqian Wu, Huaiyu Gu, Lindong Lu, Dechen Lin, null Shenguanlin, Xuanhe Zhou, Linfeng Zhang, Yuhang Zang, Xiaoyi Dong, Jiaqi Wang, Bo Zhang, Lei Bai, Pei Chu, Weijia Li, Jiang Wu, Lijun Wu, Zhenxiang Li, Guangyu Wang, Zhongying Tu, Chao Xu, Kai Chen, Bowen Zhou, Dahua Lin, Wentao Zhang, Conghui He

Green & Sustainable NLP

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

Bayesian Optimization for Controlled Image Editing via LLMs (2025.findings-acl)

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Chengkun Cai, Haoliang Liu, Xu Zhao, Zhongyu Jiang, Tianfang Zhang, Zongkai Wu, John Lee, Jenq-Neng Hwang, Lei Li

Visual Data in NLP

Challenge:	achieving precise control over generated content and maintaining semantic consistency remain significant limitations, particularly concerning grounding techniques and the necessity for model fine-tuning.
Approach:	They propose an off-the-shelf approach that integrates Large Language Models with Bayesian Optimization to facilitate precise and user-friendly image editing.
Outcome:	The proposed approach outperforms existing methods in editing accuracy and semantic preservation, as validated using different LLMs including Claude3 and GPT-4.

GEM: A General Evaluation Benchmark for Multimodal Tasks (2021.findings-acl)

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Lin Su, Nan Duan, Edward Cui, Lei Ji, Chenfei Wu, Huaishao Luo, Yongfei Liu, Ming Zhong, Taroon Bharti, Arun Sacheti

Writing-RL: Advancing Long-form Writing via Adaptive Curriculum Reinforcement Learning (2026.acl-long)

Challenge:	Existing datasets that focus on natural language tasks are not considered as a general evaluation benchmark for multimodal tasks.
Approach:	They present a general evaluation benchmark for multimodal tasks, GEM 1 . they compare it with existing multimodal vision-language datasets .
Outcome:	The proposed model is compared with existing vision-language datasets focusing on natural language tasks . it is the largest vision-linguistic dataset covering image-language tasks and video-language task at the same time .

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Xuanyu Lei, Chenliang Li, Yuning Wu, Kaiming Liu, Weizhou Shen, Peng Li, Ming Yan, Fei Huang, Ya-Qin Zhang, Yang Liu

Challenge:	Recent advances in Large Language Models (LLMs) have enabled strong performance in long-form writing, but current training paradigms remain limited.
Approach:	They propose an Adaptive Curriculum Reinforcement Learning framework to advance long-form writing capabilities beyond SFT.
Outcome:	Experiments on 7B-scale writer models show that Writing-RL improves long-form writing performance over strong SFT baselines.

Explore the Reasoning Capability of LLMs in the Chess Testbed (2025.naacl-short)

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Shu Wang, Lei Ji, Renxi Wang, Wenxiao Zhao, Haokun Liu, Yifan Hou, Ying Nian Wu

Neural Machine Translation for Agglutinative Languages via Data Rejuvenation (2025.acl-srw)

Challenge:	a recent study shows that large language models struggle with long-term, complex reasoning tasks.
Approach:	They propose to integrate annotated strategy and tactic into large language models to improve reasoning capability.
Outcome:	The proposed model performs better than GPT, Claude, and Gemini models . it integrates annotated strategy and tactic into the model .

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Chen Zhao, Yatu Ji, Ren Qing-Dao-Er-Ji, Nier Wu, Lei Shi, Fu Liu, Yepai Jia

UniRE: A Unified Label Space for Entity Relation Extraction (2021.acl-long)

Challenge:	Recent years, advances in Neural Machine Translation (NMT) heavily rely on large-scale parallel corpora.
Approach:	They propose to combine fine-grained inactive sample identification with target-side rejuvenation to improve translation quality from agglutinative languages.
Outcome:	The proposed framework improves on four low-resource agglutinative language tasks.

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Yijun Wang, Changzhi Sun, Yuanbin Wu, Hao Zhou, Lei Li, Junchi Yan

The Role of Deductive and Inductive Reasoning in Large Language Models (2025.acl-long)

Challenge:	Existing joint entity relation extraction models setup two separate label spaces for the two sub-tasks .
Approach:	They propose to eliminate the different treatment on the two sub-tasks’ label spaces by applying a unified classifier to predict each cell’s label.
Outcome:	The proposed model achieves competitive accuracy with the best extractor and is faster.

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Chengkun Cai, Xu Zhao, Haoliang Liu, Zhongyu Jiang, Tianfang Zhang, Zongkai Wu, Jenq-Neng Hwang, Lei Li

Distilling Large Embeddings via Hyperspherical Householder Quantization (2026.acl-long)

Challenge:	Large Language Models (LLMs) have demonstrated impressive capabilities in reasoning tasks, yet their reliability in problem-solving remains debatable.
Approach:	They propose a framework that integrates both deductive and inductive reasoning approaches to enhance LLM reasoning by progressively adapting its reasoning pathways based on problem complexity.
Outcome:	The proposed framework achieves 70.3% accuracy on AIW, compared to 62.2% for Tree of Thought, while maintaining lower computational costs.

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Yihang Wang, Bin Wu, Yueyang Su, Tianfu Zhang, Yiqi Du, Lei Yu, Jiafeng Guo, Xueqi Cheng

Emotion Transfer with Enhanced Prototype for Unseen Emotion Recognition in Conversation (2025.emnlp-main)

Challenge:	Existing methods for quantizing large embeddings rely on Euclidean quantization, which is poorly aligned with the angular geometry induced by contrastive embeddment training.
Approach:	They propose a geometry-aware distillation method that compresses large embeddings into short discrete representations via iterative Householder transformations on the unit hypersphere.
Outcome:	The proposed method reduces decoding cost and maintains strong semantic retrieval accuracy.

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Kun Peng, Cong Cao, Hao Peng, Guanlin Wu, Zhifeng Hao, Lei Jiang, Yanbing Liu, Philip S. Yu

Sentiment Analysis

Challenge:	Existing research on emotion recognition in conversation does not reach a consensus on classification theories . despite this, there is no clear consensus on how to recognize previously unseen emotions in real-world applications.
Approach:	They propose a prototype-based emotion transfer framework that can be used in real-world applications.
Outcome:	The proposed framework shows promise but still faces key challenges in the field of emotion recognition in conversation.

Parameter Importance is Not Static: Evolving Parameter Isolation for Supervised Fine-Tuning (2026.acl-long)

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Zekai Lin, Chao Xue, Di Liang, Xingsheng Han, Peiyang Liu, Xianjie Wu, Lei Jiang, Yu Lu, Bob Simons, Shuang Liang, Minlong Peng

ArchiDocGen: Multi-Agent Framework for Expository Document Generation in the Architectural Industry (2025.acl-industry)

Challenge:	Recent approaches to fine-tuning of large language models suffer from task interference and catastrophic forgetting.
Approach:	They propose a fine-tuning framework that adapts isolation decisions based on online estimates of parameter importance.
Outcome:	The proposed framework reduces interference and forgetting while releasing outdated parameters to recover plasticity.

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Junjie Jiang, Haodong Wu, Yongqi Zhang, Songyue Guo, Bingcen Liu, Caleb Chen Cao, Ruizhe Shao, Chao Guan, Peng Xu, Lei Chen

Text Generation

Challenge:	drafting method statements is labor-intensive and time-consuming . traditional methods involve using static templates filled in manually by engineers .
Approach:	They propose a framework that automates method statement generation by using multi-agent collaboration.
Outcome:	The proposed framework achieves 4.38 ContentScore, excelling in specialization, completeness, organization, and clarity.

Leveraging Human and Machine Preferences for Zero-shot Detection of AI-Generated Text (2026.findings-acl)

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Lei Jiang, Desheng Wu, Xiaolong Zheng, Cuicui Luo

Why Supervised Fine-Tuning Fails to Learn: A Systematic Study of Incomplete Learning in Large Language Models (2026.acl-long)

Challenge:	Recent advances in large language models have enabled generated texts to closely mimic human writing, posing significant challenges to the detection of AI-generated content.
Approach:	They propose a human-machine prediction discrepancy adapter for AI-generated text detection . they use a joint fine-tuning strategy and a discrepany-aware reweighting mechanism .
Outcome:	The proposed framework improves the detection performance of five representative models under various evaluation scenarios.

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Chao Xue, Yao Wang, Mengqiao Liu, Di Liang, Xingsheng Han, Peiyang Liu, Xianjie Wu, Chenyao Lu, Lei Jiang, Yu Lu, Haibo Shi, Shuang Liang, Minlong Peng, Flora D. Salim

Language Tags Matter for Zero-Shot Neural Machine Translation (2021.findings-acl)

Challenge:	Incomplete learning is widespread and heterogeneous in large language models . authors identify five recurrent sources of incomplete learning: missing prerequisite knowledge, conflicts between SFT supervision and pre-training knowledge, internal inconsistencies within SFT data, left-side forgetting during sequential fine-tuning, and insufficient optimization for rare or complex patterns.
Approach:	They propose a diagnostic-first framework that maps incomplete learning to causes . they identify five recurrent sources of incomplete learning: missing prerequisite knowledge, conflicts between supervision and pre-training knowledge, internal inconsistencies, left-side forgetting during sequential fine-tuning, and insufficient optimization for rare or complex patterns.
Outcome:	The proposed framework maps incomplete learning to causes using observable training and inference signals.

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Liwei Wu, Shanbo Cheng, Mingxuan Wang, Lei Li

From Hypothesis to Publication: A Comprehensive Survey of AI-Driven Research Support Systems (2025.findings-emnlp)

Challenge:	Existing studies on multilingual machine translation have ignored the importance of LTs.
Approach:	They propose to use language tag (LT) strategies to indicate translation directions in MNMT to enhance consistency and alleviate off-target issues in zero-shot directions.
Outcome:	The proposed model could translate between unsupervised languages and achieve a +8 BLEU score difference over other LT strategies in translation tasks.

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Zekun Zhou, Xiaocheng Feng, Lei Huang, Xiachong Feng, Ziyun Song, Ruihan Chen, Liang Zhao, Weitao Ma, Yuxuan Gu, Baoxin Wang, Dayong Wu, Guoping Hu, Ting Liu, Bing Qin

Structural Bias for Aspect Sentiment Triplet Extraction (2022.coling-1)

Challenge:	rapid development of artificial intelligence (AI) technologies has inspired researchers to explore how AI can accelerate and enhance research.
Approach:	They organize the relevant studies into three main categories: hypothesis formulation, hypothesis validation, and manuscript publication.
Outcome:	The authors summarize the current state of research in three main areas: hypothesis formulation, hypothesis validation, and manuscript publication.

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Chen Zhang, Lei Ren, Fang Ma, Jingang Wang, Wei Wu, Dawei Song

Sentiment Analysis

Challenge:	Existing structural bias adapters for aspect sentiment triplet extraction are under-confident . a large-scale dataset for ASTE shows the adapter is effective and efficient to a larger scale.
Approach:	They propose to use a structural adapter to integrate structural bias into pretrained language models . they propose to add a relative position structure in place of the syntactic dependency structure .
Outcome:	The proposed adapter achieves state-of-the-art performance over strong baselines, but with a light parameter demand and low latency.

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

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

Robustness in NLP

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

MTG: A Benchmark Suite for Multilingual Text Generation (2022.findings-naacl)

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Yiran Chen, Zhenqiao Song, Xianze Wu, Danqing Wang, Jingjing Xu, Jiaze Chen, Hao Zhou, Lei Li

Text Generation

Challenge:	Using MTG, we train and evaluate multilingual text generation models using human-annotated data.
Approach:	They propose a multilingual multiway text generation dataset with 400k human-annotated data that includes four generation tasks across five languages.
Outcome:	The proposed dataset includes four generation tasks across five languages (English, German, French, Spanish and Chinese) it provides comprehensive evaluations with diverse generation scenarios.

Alleviating Hallucinations from Knowledge Misalignment in Large Language Models via Selective Abstention Learning (2025.acl-long)

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Lei Huang, Xiaocheng Feng, Weitao Ma, Yuchun Fan, Xiachong Feng, Yuxuan Gu, Yangfan Ye, Liang Zhao, Weihong Zhong, Baoxin Wang, Dayong Wu, Guoping Hu, Lingpeng Kong, Tong Xiao, Ting Liu, Bing Qin

ASAP: A Chinese Review Dataset Towards Aspect Category Sentiment Analysis and Rating Prediction (2021.naacl-main)

Challenge:	Large language models (LLMs) suffer from severe hallucination issues due to the knowledge misalignment between the pre-training stage and the supervised fine-tuning stage.
Approach:	They propose a training objective with an abstention mechanism that selectively rejects tokens that misalign with the desired knowledge distribution via a special [REJ] token.
Outcome:	The proposed model selectively rejects tokens that misalign with the desired knowledge distribution via a special [REJ] token.

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Jiahao Bu, Lei Ren, Shuang Zheng, Yang Yang, Jingang Wang, Fuzheng Zhang, Wei Wu

Sentiment Analysis

Challenge:	Sentiment analysis is a key task in e-commerce to detect fine-to-coarse sentiment polarities.
Approach:	They propose to use a large-scale Chinese restaurant review dataset ASAP to investigate the sentiment polarities underlying user reviews.
Outcome:	The proposed model outperforms state-of-the-art models on both tasks.

Discovering Dialog Structure Graph for Coherent Dialog Generation (2021.acl-long)

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Jun Xu, Zeyang Lei, Haifeng Wang, Zheng-Yu Niu, Hua Wu, Wanxiang Che

Where to Go for the Holidays: Towards Mixed-Type Dialogs for Clarification of User Goals (2022.acl-long)

Challenge:	Existing studies on dialog structure graphs from open-domain dialogs have limited number of dialog states and can be laborious and costly to annotate manually.
Approach:	They propose to use dialog structure graph as a model to discover hierarchical latent dialog states and their transitions from corpus to facilitate dialog management in a RL based dialog system.
Outcome:	The proposed model can discover meaningful dialog structure graph and significantly improve multi-turn coherence on two benchmark corpora.

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Zeming Liu, Jun Xu, Zeyang Lei, Haifeng Wang, Zheng-Yu Niu, Hua Wu

Progressive Planning and Reinforced Reasoning: Large Language Model-Guided Multi-hop Question Answering over Knowledge Graph (2026.findings-acl)

Challenge:	a dialog system posits that users have figured out clear and specific goals . but in many real-world scenarios, users struggle to figure out specific goals by determining all the necessary slots.
Approach:	They propose a mixed-type dialog model with a Prompt-based continual learning mechanism . they collect 5k dialog sessions and 168k utterances for 4 dialog types and 5 domains .
Outcome:	The proposed model provides user-goal-related knowledge to help figure out clear and specific goals . it can be extended to any specific type by utilizing existing dialog corpora effectively.

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Xiang Li, Runhai Jiao, Ruifan Li, Dongnan Wu, Ruojiao Qiao, Lei Liu

Learning to Ask Denotative and Connotative Questions for Knowledge-based VQA (2024.findings-emnlp)

Challenge:	Existing approaches to multi-hop question answering lack effective intermediate guidance and policy networks focus on local neighborhood information, making it difficult to anticipate the long-term consequences of decisions.
Approach:	They propose a framework that converts decomposed sub-question sequences into stepwise decision guidance and a structure-aware lookahead policy network to enhance the agent's global state awareness and decision foresight in complex environments.
Outcome:	The proposed framework surpasses state-of-the-art methods while showing strong generalization.

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Xiaoying Xing, Peixi Xiong, Lei Fan, Yunxuan Li, Ying Wu

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

Challenge:	Large language models have attracted increasing attention due to their prominent performance on various tasks.
Approach:	They propose to let LLMs learn to ask informative questions to collect visual information . they introduce concepts of denotation and connotation to promote image and question understanding .
Outcome:	The proposed model can generate high-quality questions and efficiently collect required information without expensive training or annotations.

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

Reason Only When Needed: Efficient Generative Reward Modeling via Model-Internal Uncertainty (2026.findings-acl)

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

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Chao Xue, Yao Wang, Mengqiao Liu, Di Liang, Xingsheng Han, Peiyang Liu, Xianjie Wu, Chenyao Lu, Lei Jiang, Yu Lu, Haibo Shi, Shuang Liang, Minlong Peng, Flora D. Salim

Can Large Language Models Effectively Support Decision-Making in Sudden Emergencies? (2026.findings-acl)

Challenge:	Existing approaches to generating reward models rely on voting-based mechanisms to evaluate CoT outputs.
Approach:	They propose an efficient generative reward modeling framework grounded in model-internal uncertainty.
Outcome:	The proposed framework reduces inference cost while improving answer accuracy.

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Mengna Zhu, Jibing Wu, Lihua Liu, Yuran Gong, Yang Hao, Fu Yachao, Mao Wang, Lei Hou, Juanzi Li

From Curated Data to Scalable Models: Continual Pre-training of Dense and MoE Large Language Models for Tibetan (2026.acl-long)

Challenge:	Existing research has focused on the earlier stages of emergency response . lack of suitable datasets for reliable and compliance-aware decision-oriented modeling and evaluation is limiting current research .
Approach:	They propose a first real-world emergency decision-making dataset EDM-Bench . they propose 'rule-enhanced reasoning framework' that integrates external regulatory knowledge with constrained inference mechanisms to improve both decision safety and interpretability.
Outcome:	The proposed framework improves decision safety and interpretability by integrating regulatory knowledge with constrained inference mechanisms.

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Lei Yang, Leiyu Pan, Bojian Xiong, Renren Jin, Shaowei Zhang, Yue Chen, Ling Shi, Jiang Zhou, Junru Wu, Zhen Wang, Jianxiang Peng, Juesi Xiao, Tianyu Dong, Zhuowen Han, Zhuo Chen, Yuqi Ren, Deyi Xiong

Slot Transferability for Cross-domain Slot Filling (2021.findings-acl)

Challenge:	Large language models have achieved remarkable success across a wide range of tasks, yet their performance remains heavily biased toward high-resource languages.
Approach:	They propose a pipeline for advancing Tibetan language modeling through multilingual continual pre-training with Tibetan, Chinese, and English.
Outcome:	The proposed model outperforms open-source and Tibetan-focused models on diverse tasks.

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Hengtong Lu, Zhuoxin Han, Caixia Yuan, Xiaojie Wang, Shuyu Lei, Huixing Jiang, Wei Wu

Semantic Parsing

Challenge:	Existing work on slot filling uses labeled data from source domains to train a model for target domains.
Approach:	They propose a model-agnostic Slot Transferability Measure (STM) to evaluate the transferability from a source slot to a target slot.
Outcome:	The proposed method outperforms state-of-the-art models on multiple datasets and models.

RAP-ID: Mechanistic Prompt Injection Detection via Impostor Behavior Analysis (2026.findings-acl)

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Yuchen Yang, Lei Peng, Yujie He, Yang yu, Zhongxin Wu, Yanlei Shi

Dynamic Anticipation and Completion for Multi-Hop Reasoning over Sparse Knowledge Graph (2020.emnlp-main)

Challenge:	Existing defenses rely on externally deployed guardrail models or response inspection . current defenses depend on external guardrails or response inspecting .
Approach:	They propose a mechanistic, train-free detection framework that operates exclusively on internal state dynamics during the initial forward pass.
Outcome:	The proposed framework achieves competitive performance with significant overall improvements compared to heuristic methods.

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Xin Lv, Xu Han, Lei Hou, Juanzi Li, Zhiyuan Liu, Wei Zhang, Yichi Zhang, Hao Kong, Suhui Wu

CausalGaze: Unveiling Hallucinations via Counterfactual Graph Intervention in Large Language Models (2026.findings-acl)

Challenge:	Existing reasoning methods for sparse KGs are incomplete and lack of evidential paths to target entities makes multi-hop reasoning difficult.
Approach:	They propose a multi-hop reasoning model over sparse KGs to solve this problem . they use latent prediction of embedding-based models to make the model perform more potential path search over sparses .
Outcome:	The proposed method outperforms state-of-the-art models on five datasets from Freebase, NELL and Wikidata.

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Linggang Kong, Lei Wu, Yunlong Zhang, Xiaofeng Zhong, Wang Zhen, Yongjie Wang, Yao Pan

R3: End-to-End Reasoning-based Planning for Multi-step Retrosynthesis via Reinforcement Learning (2026.acl-long)

Challenge:	Existing classification-based methods capture noise and spurious correlations while overlooking the underlying causal mechanisms.
Approach:	They propose a hallucination detection framework based on structural causal models that captures static and passive signals from internal states and employs counterfactual interventions to disentangle causal reasoning paths from incidental noise.
Outcome:	Experiments on four datasets and three widely used LLMs show that the proposed framework improves AUROC and interpretability.

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YiFei Wang, Qizhi Pei, Jiangtao Feng, Yuntian Shi, Yi Duan, Lihao Wang, Lei Bai, Lijun Wu, Wei-Ying Ma, Hao Zhou

SenDetEX: Sentence-Level AI-Generated Text Detection for Human-AI Hybrid Content via Style and Context Fusion (2025.emnlp-main)

Challenge:	Experimental results show that R3 is a superior alternative to traditional search algorithms for multistep retrosynthesis planning.
Approach:	They propose a framework that reformulates multistep retrosynthetic planning as a generative reasoning task.
Outcome:	The proposed framework achieves state-of-the-art Top-1 accuracy of 43.7% on retrobench . it leverages Large Language Models to reformulate multistep retrosynthesis as a generative reasoning task.

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Lei Jiang, Desheng Wu, Xiaolong Zheng

Text Generation

Challenge:	Text generated by Large Language Models (LLMs) now rivals human writing, raising concerns about its misuse.
Approach:	They propose a framework for sentence-level AI-generated text detection via style and context fusion.
Outcome:	The proposed framework outperforms baseline models in detection accuracy while exhibiting transferability and robustness.

Prove Your Point!: Bringing Proof-Enhancement Principles to Argumentative Essay Generation (2024.emnlp-main)

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Ruiyu Xiao, Lei Wu, Yuhang Gou, Weinan Zhang, Ting Liu

Challenge:	Existing arguments generation methods often overlook connections between opinions . Existing methods struggle with providing compelling proof .
Approach:	They propose a two-stage framework for argumentative essay generation with a focus on logical enhancement.
Outcome:	The proposed framework generates argumentative essays with better logical validity and persuasiveness than baseline models.

Question Tells You Where the Answer Is: Intention-aware Long-Context KV Cache Compression (2026.acl-long)

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Liang Zhao, Xiaocheng Feng, Weihong Zhong, Lei Huang, Kun Zhu, Baoxin Wang, Dayong Wu, Guoping Hu, Ting Liu, Bing Qin

Challenge:	Recent methods to reduce the KV cache size fail to identify crucial KVs for generation while excluding others accurately, resulting in severe information loss.
Approach:	They propose an intention-aware KV cache eviction method that identifies and retains crucial KVs according to the attention distribution of intention, which semantically reflects the user’s goal and determines which part of the context is relevant.
Outcome:	The proposed method can maintain the model performance while reducing the KV cache size from 128K to 2K, leading to a 6.3x increase in decoding speed and 7.8x enhancement in memory efficiency compared to the default setting.

Dialogue Medical Information Extraction with Medical-Item Graph and Dialogue-Status Enriched Representation (2023.findings-emnlp)

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Lei Gao, Xinnan Zhang, Xian Wu, Shen Ge, Yefeng Zheng

How Vocabulary Sharing Facilitates Multilingualism in LLaMA? (2024.findings-acl)

Challenge:	Existing approaches ignore relationships between medical items and statuses in the multi-turn doctor-patient dialogue.
Approach:	They propose a task to extract structured medical information from free text dialogues . they propose 'Dialogue Medical Information Extraction' to model relationships between items .
Outcome:	The proposed model outperforms previous models and achieves state-of-the-art performance on the public benchmark data set.

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Fei Yuan, Shuai Yuan, Zhiyong Wu, Lei Li

MoE Adapter for Large Audio Language Models: Sparsity, Disentanglement, and Gradient-Conflict-Free (2026.findings-acl)

Challenge:	Large Language Models (LLMs) show strong performance on English tasks, but their performance in other languages is limited.
Approach:	They conducted an exhaustive analysis of the multilingual capability of LLMs by examining the performance gap before and after embedding fine-tuning across 101 languages.
Outcome:	The proposed model improves on the attributes of four quadrants in the model and provides actionable and efficient guidelines for tuning these languages.

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Yishu Lei, Shuwei He, Hu Jing, Dan Zhang, Xianlong Luo, Danxiang Zhu, Shikun Feng, Rui Liu, Jingzhou HE, Yu Sun, Hua Wu, Haifeng Wang

Smart-Searcher: Incentivizing the Dynamic Knowledge Acquisition of LLMs via Reinforcement Learning (2025.findings-emnlp)

Challenge:	Existing research on Large Language Models (LLMs) limited to textual input modality . acoustic information is intrinsically heterogeneous, entangling attributes such as speech, music, and environmental context.
Approach:	They propose a sparse Mixture-of-Experts architecture to decouple acoustic information by routing audio tokens to specialized experts.
Outcome:	The proposed architecture outperforms existing models on audio semantic and paralinguistic tasks while retaining shared experts for global context.

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Huatong Song, Jinhao Jiang, Wenqing Tian, Zhipeng Chen, Yuhuan Wu, Jiahao Zhao, Yingqian Min, Xin Zhao, Lei Fang, Ji-Rong Wen

XPrompt: Exploring the Extreme of Prompt Tuning (2022.emnlp-main)

Challenge:	Large Language Models (LLMs) are powerful but prone to hallucinations due to static knowledge. Retrieval-augmented generation (RAG) helps by injecting external information, but current methods are costly, generalize poorly, or ignore the model’s internal knowledge.
Approach:	They propose a framework to train large language models to leverage both internal and external knowledge sources.
Outcome:	The proposed framework outperforms existing methods and achieves efficient retrieval-augmented reasoning.

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Fang Ma, Chen Zhang, Lei Ren, Jingang Wang, Qifan Wang, Wei Wu, Xiaojun Quan, Dawei Song

DT-QDC: A Dataset for Question Comprehension in Online Test (2020.coling-main)

Challenge:	Prompt tuning learns soft prompts to condition pre-trained Language Models for performing downstream tasks in a parameter-efficient manner.
Approach:	They propose a Prompt tuning model with an eXtremely small scale that learns soft prompts to condition the frozen Pre-trained Language Models for performing downstream tasks in a parameter-efficient manner.
Outcome:	The proposed model outperforms the vanilla Prompt-Tuning and can significantly improve across tasks and model scales.

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Sijin Wu, Yujiu Yang, Nicholas Yung, Zhengchen Shen, Zeyang Lei

Compressing Sentence Representation for Semantic Retrieval via Homomorphic Projective Distillation (2022.findings-acl)

Challenge:	Existing methods to measure difficulty of questions are not accurate enough to guide learning.
Approach:	They propose to use a Chinese DT-QDC dataset to measure difficulty of questions and provide a new model that can improve the judgment of difficulty from different perspectives.
Outcome:	The proposed methods outperform baselines by 7.79% on F1-score and 15.92% on MAE, 28.26% on MSE, and 28.2% on MSC on the new DT-QDC dataset.

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Xuandong Zhao, Zhiguo Yu, Ming Wu, Lei Li

Meta-Task Prompting Elicits Embeddings from Large Language Models (2024.acl-long)

Challenge:	Existing pre-trained language models produce large sentence embeddings, resulting in performance gap between large and small models.
Approach:	They propose a method that augments a small Transformer encoder model with learnable projection layers to produce compact sentences while mimicking a large pre-trained language model to retain the sentence representation quality.
Outcome:	The proposed method achieves 2.7-4.5 points performance gain on STS and SR tasks while maintaining the quality of the pre-trained language models.

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Yibin Lei, Di Wu, Tianyi Zhou, Tao Shen, Yu Cao, Chongyang Tao, Andrew Yates

Pre-trained Language Models Can be Fully Zero-Shot Learners (2023.acl-long)

Challenge:	Existing methods for large language modeling are based on task-related instructions or prompts.
Approach:	They propose a method for generating high-quality sentence embeddings from Large Language Models (LLMs) using meta-task prompts.
Outcome:	The proposed method produces high-quality sentences without fine-tuning . it excels on STS benchmarks and in downstream tasks, surpassing models with similar prompts .

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Xuandong Zhao, Siqi Ouyang, Zhiguo Yu, Ming Wu, Lei Li

Act as you think: Reinforcing Consistent Reasoning in Medical Visual Question Answering (2026.acl-long)

Challenge:	Existing approaches to pre-trained language models require fine-tuning on labeled datasets or manually constructing proper prompts.
Approach:	They propose a nonparametric prompting PLM for fully zero-shot language understanding . they compare it to previous methods for text classification and text entailment .
Outcome:	The proposed method outperforms previous methods on diverse tasks.

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Songtao Jiang, Yuan Wang, Ruizhe Chen, Yan Zhang, Ruilin Luo, Bohan Lei, Yeying Jin, Sibo Song, ZhiBo Yang, Jimeng Sun, Jian Wu, Zuozhu Liu

Robustness in NLP

Challenge:	Recent advances have improved the accuracy of medical visual question answering (Med-VQA) however, the high stakes nature of the medical domain has precipitated a shift towards interpretability and transparency of reasoning processes.
Approach:	They propose a reinforcement learning from verifiable rewards framework that rewards internal consistency and logical coherence.
Outcome:	The proposed framework rewards internal consistency and logical coherence, and is highly versatile, the authors show.

ProReason: Multi-Modal Proactive Reasoning with Decoupled Eyesight and Wisdom (2025.emnlp-main)

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Jingqi Zhou, Sheng Wang, Jingwei Dong, Kai Liu, Lei Li, Jiahui Gao, Jiyue Jiang, Lingpeng Kong, Chuan Wu

Representational Isomorphism and Alignment of Multilingual Large Language Models (2024.findings-emnlp)

Challenge:	Large vision-language models often prioritize language knowledge over image information on visual reasoning tasks, incurring performance degradation.
Approach:	They propose a visual reasoning framework that decouples vision-reasoning capabilities and multi-run proactive perception.
Outcome:	The proposed framework outperforms existing models on benchmarks for open-source and closed-source models with 13.2% performance gain.

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Di Wu, Yibin Lei, Andrew Yates, Christof Monz

A Survey on In-context Learning (2024.emnlp-main)

Challenge:	Existing isomorphism of sentence representations can facilitate representational alignments in zero-shot and few-shot settings.
Approach:	They propose to apply a contrastive objective to LLMs with a small number of translation pairs to improve models' performance on Semantic Textual Similarity tasks.
Outcome:	The proposed representation-level approach significantly improves on Semantic Textual Similarity (STS) tasks across languages even without a monolingual objective.

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Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Jingyuan Ma, Rui Li, Heming Xia, Jingjing Xu, Zhiyong Wu, Baobao Chang, Xu Sun, Lei Li, Zhifang Sui

Low-Resource NLP

Challenge:	In-context learning (ICL) is a new paradigm for natural language processing . large language models (LLMs) demonstrate the ability to learn from a few examples .
Approach:	They propose to explore ICL to evaluate and extrapolate the ability of large language models.
Outcome:	The proposed methods can be used to evaluate and extrapolate the ability of large language models.

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

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

Leave No Document Behind: Benchmarking Long-Context LLMs with Extended Multi-Doc QA (2024.emnlp-main)

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

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Minzheng Wang, Longze Chen, Fu Cheng, Shengyi Liao, Xinghua Zhang, Bingli Wu, Haiyang Yu, Nan Xu, Lei Zhang, Run Luo, Yunshui Li, Min Yang, Fei Huang, Yongbin Li

UniKeyphrase: A Unified Extraction and Generation Framework for Keyphrase Prediction (2021.findings-acl)

Challenge:	Existing benchmarks for evaluating long-context language models employ irrelevant noise texts to artificially extend the length of test cases, diverging from the real-world scenarios of long-constituency applications.
Approach:	They propose a long-context benchmark, Loong, aligning with realistic scenarios through extended multi-document question answering (QA) .
Outcome:	The proposed model can scale up the context window of large language models to perform in-depth analysis of multiple long documents.

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Huanqin Wu, Wei Liu, Lei Li, Dan Nie, Tao Chen, Feng Zhang, Di Wang

Easy Samples Are All You Need: Self-Evolving LLMs via Data-Efficient Reinforcement Learning (2026.findings-acl)

Challenge:	Mainstream methods that ignore the diversity among keyphrases or weakly capture the relation between tasks implicitly ignore keyphrase diversity.
Approach:	They propose a novel end-to-end learning framework that jointly learns to extract and generate keyphrases by exploiting latent semantic relation between extraction and generation.
Outcome:	The proposed approach outperforms mainstream methods on a benchmarked document on keyphrase prediction.

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Zhiyin Yu, Bo Zhang, Qibin Hou, Zhonghai Wu, Xiao Luo, Lei Bai

Probabilistic Graph Reasoning for Natural Proof Generation (2021.findings-acl)

Challenge:	Experimental results show that EasyRL consistently outperforms state-of-the-art baselines due to the substantial annotation cost and issues such as model collapse or reward hacking.
Approach:	They propose a supervised RL approach with a divide-and-conquer strategy that simulates the human cognitive acquisition curve using easy labeled data.
Outcome:	The proposed approach outperforms state-of-the-art models on mathematical and scientific benchmarks using only 10% of easy labeled data.

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Changzhi Sun, Xinbo Zhang, Jiangjie Chen, Chun Gan, Yuanbin Wu, Jiaze Chen, Hao Zhou, Lei Li

METRO: Towards Strategy Induction from Expert Dialogue Transcripts for Non-collaborative Dialogues (2026.acl-long)

Challenge:	Existing approaches to reasoning over formal representations do not explicitly consider inter-dependency between answers and proofs.
Approach:	They propose a novel approach for joint answer prediction and proof generation using an induced graphical model.
Outcome:	The proposed approach achieves 10%-30% improvement on QA accuracy in evaluations under diverse conditions.

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Haofu Yang, Jiaji Liu, Chen Huang, Faguo Wu, Wenqiang Lei, See-Kiong Ng

TalkLoRA: Communication-Aware Mixture of Low-Rank Adaptation for Large Language Models (2026.acl-long)

Challenge:	Developing non-collaborative dialogue agents traditionally requires manual codification of expert strategies.
Approach:	They propose a method that formalizes expert knowledge into a Strategy Forest from raw transcripts.
Outcome:	The proposed method outperforms existing methods by 9%-10% in two benchmarks.

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Lin Mu, Haiyang Wang, Li Ni, Lei Sang, Zhize Wu, Peiquan Jin, Yiwen Zhang

A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and Solutions (2026.acl-long)

Challenge:	Existing LoRA methods assume that experts operate independently, leading to unstable routing, expert dominance.
Approach:	They propose a communication-aware MoELoRA framework that relaxes this assumption by introducing expert-level communication prior to routing.
Outcome:	The proposed framework outperforms vanilla LoRA and MoELoRA on diverse language understanding tasks while maintaining expert dominance.

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Zhiyin Yu, Yuchen Mou, Juncheng Yan, Junyu Luo, Chunchun Chen, Xing Wei, Yunhui Liu, Hongru Sun, Yuxing Zhang, Jun Xu, Yatao Bian, Ming Zhang, Wei Ye, Tieke He, Jie Yang, Guanjie Zheng, Zhonghai Wu, Bo Zhang, Lei Bai, Xiao Luo

Exploiting Contrastive Learning and Numerical Evidence for Confusing Legal Judgment Prediction (2023.findings-emnlp)

Challenge:	Existing research on reinforcement learning for LLMs under data scarcity has not been unified.
Approach:	They propose a top-up hierarchical framework built around three complementary perspectives: data-centric, training-centric and framework-centric.
Outcome:	The proposed framework provides a clear conceptual foundation for understanding the design space of data-efficient RL for large language models and to guide researchers working in this emerging area.

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Leilei Gan, Baokui Li, Kun Kuang, Yating Zhang, Lei Wang, Anh Luu, Yi Yang, Fei Wu

Imposing Label-Relational Inductive Bias for Extremely Fine-Grained Entity Typing (N19-1)

Challenge:	Existing studies fail to distinguish different classification errors with a standard cross-entropy classification loss and ignore the numbers in the fact description for predicting the term of penalty.
Approach:	They propose to extract crime amounts from fact description and use them to learn distinguishable representations to exploit the numbers in the fact description for predicting the term of penalty.
Outcome:	The proposed method achieves state-of-the-art results on real-world datasets and ablation studies demonstrate the effectiveness of each component.

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Wenhan Xiong, Jiawei Wu, Deren Lei, Mo Yu, Shiyu Chang, Xiaoxiao Guo, William Yang Wang

S2ST-Omni: Hierarchical Language-Aware SpeechLLM Adaptation for Multilingual Speech-to-Speech Translation (2026.findings-acl)

Challenge:	Existing entity typing systems exploit type hierarchy provided by KB schema to model label correlations.
Approach:	They propose a graph layer that encodes global label co-occurrence statistics and word-level similarities.
Outcome:	The proposed model achieves a 15.3% relative F1 improvement on a large dataset with over 10,000 free-form types.

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Yu Pan, Xiongfei Wu, Yang Yuguang, Jixun Yao, Maxime Cordy, Lei Ma, Jianjun Zhao

ChartMind: A Comprehensive Benchmark for Complex Real-world Multimodal Chart Question Answering (2025.emnlp-main)

Challenge:	S2ST-Omni integrates a speech-to-text frontend with a modular, plug-and-play text-tospeech backend.
Approach:	They propose a compositional S2ST framework that integrates a speech-to-text frontend with a modular, plug-and-play text-tospeech backend.
Outcome:	The proposed framework outperforms existing frameworks in translation and synthesis . it integrates a speech-to-text translation frontend with a plug-and-play text-tospeech backend .

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Jingxuan Wei, Nan Xu, Junnan Zhu, null Haoyanni, Gaowei Wu, Qi Chen, Bihui Yu, Lei Wang

Natural Language Interfaces

Challenge:	Chart question answering (CQA) is a multimodal task for evaluating the reasoning capabilities of vision-language models.
Approach:	They propose a chart question answering benchmark that incorporates multilingual contexts and supports open-domain textual outputs.
Outcome:	The proposed framework outperforms the previous three common CQA paradigms: instruction-following, OCR-enhanced, and chain-of-thought.

UNO Arena for Evaluating Sequential Decision-Making Capability of Large Language Models (2024.emnlp-main)

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Zhanyue Qin, Haochuan Wang, Deyuan Liu, Ziyang Song, Cunhang Fan, Zhao Lv, Jinlin Wu, Zhen Lei, Zhiying Tu, Dianhui Chu, Xiaoyan Yu, Dianbo Sui

DICP: Deep In-Context Prompt for Event Causality Identification (2025.findings-emnlp)

Challenge:	Existing LLMs demonstrate powerful capabilities between tasks, but can they make sequential decisions?
Approach:	They propose to evaluate sequential decision-making capability of large language models (LLMs) using novel metrics based Monte Carlo methods.
Outcome:	The proposed benchmark improves sequential decision-making performance compared to the vanilla LLM player.

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Lin Mu, Jun Shen, Li Ni, Lei Sang, Zhize Wu, Peiquan Jin, Yiwen Zhang

Beyond the Individual: Virtualizing Multi-Disciplinary Reasoning for Clinical Intake via Collaborative Agents (2026.findings-acl)

Challenge:	Existing prompt-learning-based methods concatenate in-context examples only at the input layer, limiting the model’s ability to capture abstract semantic cues necessary for identifying complex causal relationships.
Approach:	They propose a model that injects in-context examples into the deeper layer of a pre-trained language model (PLM) this model leverages hierarchical semantic representations formed in deeper layers, thereby enhancing its capacity to learn high-level causal abstractions.
Outcome:	The proposed model improves on two widely used datasets and shows that it can learn high-level causal abstractions.

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Huangwei Chen, Wu Li, Junhao Jia, Yining Chen, Xiaotao Pang, Ya-Long Chen, Li Gonghui, Haishuai Wang, Jiajun Bu, Lei Wu

Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations (2024.acl-long)

Challenge:	Initial outpatient consultations are costly and difficult to scale to real-time intake.
Approach:	They propose a synchronous virtual MDT framework that formalizes the consultation state using a structured SOAP representation, separating evidence collection from diagnostic reasoning to improve traceability and bias control.
Outcome:	The proposed framework outperforms state-of-the-art models on ClinicalBench and a real-world RAPID-IPN dataset in documentation quality and consultation capability.

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Peiyi Wang, Lei Li, Zhihong Shao, Runxin Xu, Damai Dai, Yifei Li, Deli Chen, Yu Wu, Zhifang Sui

Numerical Reasoning

Challenge:	Existing methods for process-oriented math reward models rely on manual annotation.
Approach:	They propose a process-oriented math process reward model called Math-shepherd which assigns a reward score to each step of math problem solutions.
Outcome:	The proposed model breaks the bottleneck of manual supervision in two scenarios.

ALLabel: Three-stage Active Learning for LLM-based Entity Recognition using Demonstration Retrieval (2025.emnlp-main)

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Zihan Chen, Lei Shi, Weize Wu, Qiji Zhou, Yue Zhang

LLaMA-Berry: Pairwise Optimization for Olympiad-level Mathematical Reasoning via O1-like Monte Carlo Tree Search (2025.naacl-long)

Challenge:	Large language models (LLMs) are increasingly used to solve the entity recognition task.
Approach:	They propose a framework to select the most informative and representative samples for LLM in-context learning.
Outcome:	The proposed framework outperforms baselines on three specialized domain datasets.

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Di Zhang, Jianbo Wu, Jingdi Lei, Tong Che, Jiatong Li, Tong Xie, Xiaoshui Huang, Shufei Zhang, Marco Pavone, Yuqiang Li, Wanli Ouyang, Dongzhan Zhou

Jiawei Wu, Lei Li, William Yang Wang

Challenge:	LLaMA-Berry is an advanced mathematical reasoning framework to enhance the problem-solving ability of large language models (LLMs).
Approach:	They propose a Monte Carlo Tree Search and Self-Refine framework to optimize reasoning paths and a pairwise reward model to evaluate different paths globally.
Outcome:	The proposed framework overcomes inefficiencies and limitations of step-wise and greedy search algorithms, enabling more efficient exploration of solution spaces.

Reinforced Co-Training (N18-1)

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TableEval: A Real-World Benchmark for Complex, Multilingual, and Multi-Structured Table Question Answering (2025.emnlp-main)

Challenge:	Existing co-training methods ignore sampling bias and fail to explore the data space.
Approach:	They propose a method to select high-quality unlabeled samples to better co-train on by learning a selection policy with a small labeled dataset.
Outcome:	The proposed method can obtain more accurate text classification results on clickbait detection and generic text classification tasks.

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Junnan Zhu, Jingyi Wang, Bohan Yu, Xiaoyu Wu, Junbo Li, Lei Wang, Nan Xu

Edge-free but Structure-aware: Prototype-Guided Knowledge Distillation from GNNs to MLPs (2025.coling-main)

Challenge:	Existing TableQA benchmarks focus on simple flat tables and suffer from data leakage . current benchmarks are monolingual and fail to capture cross-lingual variability .
Approach:	They propose a table-based TableQA benchmark to evaluate LLMs on real-world tasks.
Outcome:	The proposed benchmarks show that they achieve high agreement with human judgment . the proposed framework improves on the alignment between model responses and reference answers .

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Taiqiang Wu, Zhe Zhao, Jiahao Wang, Xingyu Bai, Lei Wang, Ngai Wong, Yujiu Yang

Green & Sustainable NLP

Challenge:	Existing methods to train low-latency multilayer perceptrons (MLPs) on graph tasks are based on graph nodes and lack graph structural information.
Approach:	They propose to distill graph structural information from Graph Neural Networks (GNNs) to low-latency multilayer perceptrons (MLPs) on graph tasks.
Outcome:	The proposed method does not require graph edges (edge-free setting) yet learns structure-aware MLPs.

GraphLoRA: Structure-Aware Low-Rank Adaptation for Large Language Model Recommendation (2026.findings-acl)

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Lin Mu, Guoji Wang, Li Ni, Lei Sang, Zhize Wu, Peiquan Jin, Yiwen Zhang

How Good Are LLMs at Out-of-Distribution Detection? (2024.lrec-main)

Challenge:	Existing methods for translating collaborative information into textual prompts or injecting pre-trained embeddings into the LLM treat structural information as static input and fail to capture high-order relational dependencies.
Approach:	They propose a framework that generalizes low-rank adaptation from independent to structure-aware propagation by embedding a trainable graph message-passing network within the low-ranked adaptation pathway.
Outcome:	Experiments on multiple benchmarks show that GraphLoRA outperforms state-of-the-art recommendation methods and achieves superior generalization.

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Bo Liu, Li-Ming Zhan, Zexin Lu, Yujie Feng, Lei Xue, Xiao-Ming Wu

Contrastive Learning for Many-to-many Multilingual Neural Machine Translation (2021.acl-long)

Challenge:	Out-of-distribution (OOD) detection is crucial for ensuring AI safety . large language models (LLMs) are becoming more prevalent due to their scale, pre-training objectives, and paradigms used for inference.
Approach:	They propose to use large language models to investigate out-of-distribution (OOD) detection in machine learning.
Outcome:	The proposed method outperforms other OOD detectors in zero-grad and fine-tuning scenarios.

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Xiao Pan, Mingxuan Wang, Liwei Wu, Lei Li

Making Pretrained Language Models Good Long-tailed Learners (2022.emnlp-main)

Challenge:	Existing multilingual machine translation approaches focus on English-centric directions, while non-English directions lag behind.
Approach:	They propose a multilingual machine translation system with an emphasis on non-English directions.
Outcome:	The proposed model outperforms existing models on English-centric and non-English directions on multilingual translation benchmarks.

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Chen Zhang, Lei Ren, Jingang Wang, Wei Wu, Dawei Song

Task-Oriented Clustering for Dialogues (2021.findings-emnlp)

Challenge:	Prompt-tuning has shown appealing performance in few-shot classification . however, it is less promising in long-tailed classification due to long tail .
Approach:	They propose to use prompt-tuning to make pretrained language models at least good long-tailed learners by bridging the gap between prompt- and commonly used finetun.
Outcome:	The proposed method makes pretrained language models at least good long-tailed learners, bridging the gap between prompt-tuning and finetunation.

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Chenxu Lv, Hengtong Lu, Shuyu Lei, Huixing Jiang, Wei Wu, Caixia Yuan, Xiaojie Wang

A.S.E: A Repository-Level Benchmark for Evaluating Security in AI-Generated Code (2026.findings-acl)

Challenge:	Existing methods for task-oriented dialogue clustering are difficult to apply directly due to inherent differences between them.
Approach:	They propose a Dialogue Task Clustering Network model for task-oriented clustering . they use context-aware utterance representations and cross-dialogue utterrance cluster representations .
Outcome:	The proposed model outperforms baselines on three public datasets on all metrics.

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Keke Lian, Wang Bin, Lei Zhang, Libo Chen, Junjie Wang, Ziming Zhao, Yujiu Yang, Miaoqian Lin, Haotong Duan, Haoran Zhao, Shuang Liao, Mingda Guo, Quan Jiazheng, Yilu Zhong, Chenhao He, Chen Zichuan, Jie Wu, Haoling Li, Zhaoxuan Li, Jiongchi Yu, Hui LI, Dong Zhang

Programming Languages in NLP

Challenge:	Existing security evaluation benchmarks lack relevance to real-world AI programming tasks . current LLMs struggle with secure coding, research shows .
Approach:	They propose a repository-level evaluation benchmark to assess security of AI-generated code.
Outcome:	The proposed framework mirrors real-world AI programming tasks and offers valuable insights into the state of AI code generation.

Dolphin: Document Image Parsing via Heterogeneous Anchor Prompting (2025.findings-acl)

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Hao Feng, Shu Wei, Xiang Fei, Wei Shi, Yingdong Han, Lei Liao, Jinghui Lu, Binghong Wu, Qi Liu, Chunhui Lin, Jingqun Tang, Hao Liu, Can Huang

SCOUT: Selective Coupling via Optimal Unbalanced Transport for Interpretable Text Classification (2026.acl-long)

Challenge:	Current document image parsing solutions rely on specialized models or generate content autoregressively.
Approach:	They propose a multimodal document image parsing model that integrates specialized models with autogeneous content generation.
Outcome:	The proposed model achieves state-of-the-art performance across diverse page-level and element-level settings while ensuring superior efficiency.

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Junhao Jia, Hanwen Zheng, Yueyi Wu, Huangwei Chen, Haishuai Wang, Jiajun Bu, Lei Wu

Explainability & Interpretability in NLP

Challenge:	Standard interpretable models often rely on scalar similarities that obscure the true evidentiary basis of a prediction.
Approach:	They propose a new paradigm that grounds prototype reasoning in the selective correspondence of discriminative fragments.
Outcome:	The proposed model outperforms rationale extraction and post-hoc attribution methods on seven benchmarks.

Improving Contextual Faithfulness of Large Language Models via Retrieval Heads-Induced Optimization (2025.acl-long)

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Lei Huang, Xiaocheng Feng, Weitao Ma, Yuchun Fan, Xiachong Feng, Yangfan Ye, Weihong Zhong, Yuxuan Gu, Baoxin Wang, Dayong Wu, Guoping Hu, Bing Qin

ENPAR:Enhancing Entity and Entity Pair Representations for Joint Entity Relation Extraction (2021.eacl-main)

Challenge:	Existing frameworks for retrieval-augmented large language models (LLMs) are lacking in LFQA faithfulness testing.
Approach:	They propose a framework to teach retrieval-augmented large language models to explicitly discriminate between faithful and unfaithful generations.
Outcome:	The proposed framework outperforms GPT-4o in LFQA scenarios and outperformed existing benchmarks.

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Yijun Wang, Changzhi Sun, Yuanbin Wu, Hao Zhou, Lei Li, Junchi Yan

Towards Zero-Shot Persona Dialogue Generation with In-Context Learning (2023.findings-acl)

Challenge:	Existing methods for joint entity relation extraction use multitask learning frameworks, but annotations for additional tasks are hard to obtain.
Approach:	They propose a pre-training method to improve the joint extraction performance with just extra entity annotations.
Outcome:	The proposed method outperforms existing methods on ACE05, SciERC, and NYT and outperformed BERT on other tasks.

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Xinchao Xu, Zeyang Lei, Wenquan Wu, Zheng-Yu Niu, Hua Wu, Haifeng Wang