Papers by An Wang

Multimodal Topic-Enriched Auxiliary Learning for Depression Detection (2020.coling-main)

Challenge:	Recent advances in retrieval-augmented generation (RAG) have substantially improved question-answering systems, particularly for factoid ‘5Ws’ questions.
Approach:	They propose a data organization paradigm where large language models transform documents into more structured and loosely interconnected LUs.
Outcome:	Experiments in open-domain and industrial settings show that the proposed paradigm outperforms existing paradigms and shows high adaptability across diverse document formats.

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Minghui An, Jingjing Wang, Shoushan Li, Guodong Zhou

Removing Prompt-template Bias in Reinforcement Learning from Human Feedback (2025.findings-acl)

Challenge:	Existing studies on depression detection rely on textual and visual content to determine whether a human being is depressed or non-depressed.
Approach:	They propose a multimodal topic-enriched Auxiliary Learning approach that captures topic information from texts and images for depression detection.
Outcome:	The proposed approach improves the performance of the primary task by using topic information from text and images.

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Chaojie Wang, Haonan Shi, Long Tian, Bo An, Shuicheng Yan

Attention-based Relational Graph Convolutional Network for Target-Oriented Opinion Words Extraction (2021.eacl-main)

Challenge:	Reinforcement Learning from Human Feedback (RLHF) has shown promise for enhancing pre-trained large language models to generate responses that align with human preferences and societal values.
Approach:	They propose a method to estimate prompt-template bias term during reward modeling and use it to calibrate reward scores.
Outcome:	The proposed method can be flexibly combined with existing algorithms of removing length bias, leading to a further improvement in the aspect of enhancing the quality of generated responses.

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Junfeng Jiang, An Wang, Akiko Aizawa

Detecting Subtle Differences between Human and Model Languages Using Spectrum of Relative Likelihood (2024.emnlp-main)

Challenge:	empirical results show that our model significantly outperforms all existing models on four benchmark datasets.
Approach:	They propose a novel attention-based relational graph convolutional neural network to exploit syntactic information over dependency graphs.
Outcome:	The proposed model outperforms existing models on four benchmark datasets.

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Yang Xu, Yu Wang, Hao An, Zhichen Liu, Yongyuan Li

Psycholinguistics

Challenge:	Existing methods for detecting modelgenerated texts from human texts are limited by the fact that absolute likelihood values of texts are bound to certain linguistic and cognitive constraints.
Approach:	They propose to use relative likelihood values instead of absolute ones to extract useful features from the spectrum-view of likelihood for the human-model text detection task.
Outcome:	The proposed method can reveal subtle differences between human and model languages, which find theoretical roots in psycholinguistics studies.

Towards Faithful Neural Table-to-Text Generation with Content-Matching Constraints (2020.acl-main)

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Zhenyi Wang, Xiaoyang Wang, Bang An, Dong Yu, Changyou Chen

Text Generation

Challenge:	Existing methods for text generation ignore faithfulness between generated text and table . current methods ignore faithfulity, leading to generated information that goes beyond table content .
Approach:	They propose a Transformer-based generation framework to enforce faithfulness between generated text and table . they propose metric to evaluate faithfulness and automatic metric for automatic generating .
Outcome:	The proposed framework outperforms state-of-the-art methods in automatic evaluations and human evaluations.

AMO-Bench: Large Language Models Still Struggle in High School Math Competitions (2026.findings-acl)

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Junlin Liu, Shengnan An, Shuang Zhou, Dan Ma, Yehao Lin, Xinxuan Lv, Xuanlin Wang, Xiaoyu Li, Ziwen Wang, Xuezhi Cao, Xunliang Cai

Reasoning

Challenge:	Existing benchmarks for mathematical reasoning are becoming less effective due to performance saturation.
Approach:	They propose to use a mathematical reasoning benchmark with Olympiad difficulty to evaluate top-tier LLMs.
Outcome:	The proposed benchmarks are cross-validated by experts to meet IMO difficulty standards and entirely original problems to prevent performance leakages from data memorization.

LaMP-Val: Large Language Models Empower Personalized Valuation in Auction (2025.findings-emnlp)

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Jie Sun, Tianyu Zhang, Houcheng Jiang, Kexin Huang, Xiang Shu, Zhibo Zhu, Lintao Ma, Xingyu Lu, Jun Zhou, Junkang Wu, Chi Luo, An Zhang, Jiancan Wu, Xiang Wang

ProtT3: Protein-to-Text Generation for Text-based Protein Understanding (2024.acl-long)

Challenge:	Currently, most research focuses on the bidding algorithms used within auction mechanisms.
Approach:	They propose a personalized valuation framework that integrates Large Language Models to incorporate personalized semantic preference into users valuation process.
Outcome:	The proposed framework incorporates Large Language Models to incorporate personalized semantic preference into users valuation process.

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Zhiyuan Liu, An Zhang, Hao Fei, Enzhi Zhang, Xiang Wang, Kenji Kawaguchi, Tat-Seng Chua

AssistedDS: Benchmarking How External Domain Knowledge Assists LLMs in Automated Data Science (2025.findings-emnlp)

Challenge:	Language Models excel in understanding textual descriptions of proteins, but struggle to process texts.
Approach:	They propose a framework for Protein-to-Text Generation for Text-based Protein Understanding that integrates a PLM as its protein understanding module.
Outcome:	The proposed framework surpasses existing baselines and is highly efficient in protein-to-text generation.

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An Luo, Xun Xian, Jin Du, Fangqiao Tian, Ganghua Wang, Ming Zhong, Shengchun Zhao, Xuan Bi, Zirui Liu, Jiawei Zhou, Jayanth Srinivasa, Ashish Kundu, Charles Fleming, Mingyi Hong, Jie Ding

Robustness in NLP

Challenge:	Large language models (LLMs) have advanced the automation of data science workflows, yet it remains unclear whether they can critically leverage external domain knowledge as human data scientists do in practice.
Approach:	They propose a benchmark to evaluate how large language models handle external domain knowledge in tabular prediction tasks.
Outcome:	The proposed model evaluates whether it can critically leverage external domain knowledge as human data scientists do in practice.

Hello Again! LLM-powered Personalized Agent for Long-term Dialogue (2025.naacl-long)

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Hao Li, Chenghao Yang, An Zhang, Yang Deng, Xiang Wang, Tat-Seng Chua

LongSpec: Long-Context Lossless Speculative Decoding with Efficient Drafting and Verification (2026.acl-long)

Challenge:	Existing dialogue systems focus on brief single-session interactions, neglecting real-world needs for long-term companionship and personalized interactions.
Approach:	They propose a model-agnostic framework for long-term dialogue agents . they use event summary and persona management to enable reasoning .
Outcome:	The proposed framework incorporates three independently tunable modules dedicated to event perception, persona extraction, and response generation.

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Penghui Yang, Cunxiao Du, Fengzhuo Zhang, Haonan Wang, Tianyu Pang, Chao Du, Bo An

Green & Sustainable NLP

Challenge:	Large Language Models (LLMs) can process extremely long contexts, requiring efficient inference over extended inputs.
Approach:	They propose a model that uses a constant-sized key-value cache to train long-context models.
Outcome:	Experimental results show that LongSpec achieves 3.26x speedup over strong Flash Attention baselines and 2.34x wall clock time on four math reasoning tasks.

L2C: Describing Visual Differences Needs Semantic Understanding of Individuals (2021.eacl-main)

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An Yan, Xin Wang, Tsu-Jui Fu, William Yang Wang

ReactXT: Understanding Molecular “Reaction-ship” via Reaction-Contextualized Molecule-Text Pretraining (2024.findings-acl)

Challenge:	Existing methods for captioning images without understanding individual's semantics are not effective . a new task, visual comparison, has drawn increasing attention in the field of language and vision .
Approach:	They propose a learning-to-compare model which learns to understand semantic structures of two images and compares them while learning to describe each one.
Outcome:	The proposed model outperforms the baseline and human evaluation on the Birds-to-Words dataset.

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Zhiyuan Liu, Yaorui Shi, An Zhang, Sihang Li, Enzhi Zhang, Xiang Wang, Kenji Kawaguchi, Tat-Seng Chua

DNA: Denoised Neighborhood Aggregation for Fine-grained Category Discovery (2023.emnlp-main)

Challenge:	Molecular-text modeling is an emerging research field that aims to facilitate molecule-relevant tasks with a textual interface and textual knowledge.
Approach:	They propose a new method for reaction-text modeling that uses three types of input contexts to incrementally pretrain LMs.
Outcome:	The proposed method improves experimental procedure prediction and molecule captioning and offers competitive results in retrosynthesis.

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Wenbin An, Feng Tian, Wenkai Shi, Yan Chen, Qinghua Zheng, QianYing Wang, Ping Chen

MT-Video-Bench: A Holistic Video Understanding Benchmark for Evaluating Multimodal LLMs in Multi-Turn Dialogues (2026.findings-acl)

Challenge:	Existing methods to learn compact cluster representations from coarsely labeled data are noisy and degrade the quality of learning.
Approach:	They propose a framework that encodes semantic structures of data into the embedding space . they retrieve k-nearest neighbors of a query as positive keys to capture similarities .
Outcome:	The proposed framework can retrieve more accurate neighbors and outperform state-of-the-art models by a large margin.

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Yaning Pan, Qianqian Xie, Guohui Zhang, Zekun Moore Wang, Yongqian Wen, Yuanxing Zhang, Haoxuan Hu, Zhiyu Pan, Yibing Huang, Zhidong Gan, Yonghong Lin, An Ping, Shihao Li, Yanghai Wang, Tianhao Peng, Jiaheng Liu

CSLM: A Framework for Question Answering Dataset Generation through Collaborative Small Language Models (2024.findings-emnlp)

Challenge:	Existing evaluation benchmarks for Multimodal Large Language Models (MLLMs) focus on single-turn question answering, overlooking the complexity of multi-turn dialogues in real-world scenarios.
Approach:	They propose a video understanding benchmark for MLLMs in multi-turn dialogues that assesses six core competencies that focus on perceptivity and interactivity.
Outcome:	The MT-Video-Bench evaluates 1,000 multi-turn dialogues from diverse domains and reveals significant performance discrepancies and limitations in handling multi-turned video dialogues.

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Yiming Wang, Yang Liu, Lingchen Wang, An Xiao

Building a Japanese Document-Level Relation Extraction Dataset Assisted by Cross-Lingual Transfer (2024.lrec-main)

Challenge:	Collecting high-quality question-answer (QA) pairs is vital for training large language models, but computational demands and associated costs often render such approaches prohibitive for the average researcher.
Approach:	They propose a small-scaled, open-source solution that generates QA pairs from documents or raw corpora using large-scale models like Llama-70B.
Outcome:	Experiments on domain-specific datasets show that the proposed model can generate high-quality QA pairs, making it accessible to a broader range of researchers.

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Youmi Ma, An Wang, Naoaki Okazaki

Information Extraction & Text Mining

Challenge:	Document-level Relation Extraction (DocRE) is the task of extracting all semantic relationships from a document.
Approach:	They propose to transfer an English document to Japanese to promote DocRE in other languages.
Outcome:	The proposed model reduces the human edit steps by 50% compared with the previous approach.

Learn from Failure: Fine-tuning LLMs with Trial-and-Error Data for Intuitionistic Propositional Logic Proving (2024.acl-long)

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Chenyang An, Zhibo Chen, Qihao Ye, Emily First, Letian Peng, Jiayun Zhang, Zihan Wang, Sorin Lerner, Jingbo Shang

MedEval: A Multi-Level, Multi-Task, and Multi-Domain Medical Benchmark for Language Model Evaluation (2023.emnlp-main)

Challenge:	Recent advances in Automated Theorem Proving have shown the effectiveness of leveraging a (large) language model that generates tactics (i.e. proof steps) to search through proof states.
Approach:	They propose to use a large language model that generates tactics to search through proof states.
Outcome:	The proposed model solves more unseen theorems with lower trial searches than the current model, which only learns from failed attempts.

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Zexue He, Yu Wang, An Yan, Yao Liu, Eric Chang, Amilcare Gentili, Julian McAuley, Chun-Nan Hsu

ReLM: Leveraging Language Models for Enhanced Chemical Reaction Prediction (2023.findings-emnlp)

Challenge:	Existing medical datasets require high quality domain-specific datasets.
Approach:	They propose a multi-level, multi-task, and multi-domain medical benchmark to facilitate the development of language models for healthcare.
Outcome:	The proposed model provides granular potential usage and supports a wide range of tasks.

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Yaorui Shi, An Zhang, Enzhi Zhang, Zhiyuan Liu, Xiang Wang

PACE: Prefix-Protected and Difficulty-Aware Compression for Efficient Reasoning (2026.findings-acl)

Challenge:	Existing methods for predicting chemical reactions are limited by insufficient training data and inability to utilize textual information.
Approach:	They propose a framework that leverages chemical knowledge encoded in language models to assist GNNs, thereby enhancing the accuracy of real-world chemical reaction predictions.
Outcome:	The proposed framework improves state-of-the-art GNN-based methods across chemical reaction datasets especially in out-of distribution settings.

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Ruixiang Feng, Yuntao Wen, Silin Zhou, Ke Shi, Yifan Wang, Ran Le, Zhenwei An, Zongchao Chen, Chen Yang, Guangyue Peng, Yiming Jia, Dongsheng Wang, Tao Zhang, Lisi Chen, Yang Song, Shen Gao, Shuo Shang

Responsible & Trustworthy NLP

Challenge:	Existing LRMs often suffer from "overthinking" and excessively long reasoning traces . a dual-level framework for length compression of LRM is proposed .
Approach:	They propose a framework for prefix-protected and difficulty-aware compression under hierarchical supervision.
Outcome:	The proposed framework reduces token usage while improving accuracy on math benchmarks.

Tell Me What You Don’t Know: Enhancing Refusal Capabilities of Role-Playing Agents via Representation Space Analysis and Editing (2025.findings-acl)

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Wenhao Liu, Siyu An, Junru Lu, Muling Wu, Tianlong Li, Xiaohua Wang, Changze Lv, Xiaoqing Zheng, Di Yin, Xing Sun, Xuanjing Huang

Representation Learning

Challenge:	Role-playing Agents (RPAs) struggle to recognize and respond to hard queries that conflict with their role-play knowledge.
Approach:	They propose a lightweight representation editing approach that conveniently shifts conflicting requests to the rejection region, thereby enhancing the model’s refusal accuracy.
Outcome:	The proposed model improves RPAs’ refusal ability of conflicting requests while maintaining their general role-playing capabilities.

LaDiC: Are Diffusion Models Really Inferior to Autoregressive Counterparts for Image-to-Text Generation? (2024.naacl-long)

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Yuchi Wang, Shuhuai Ren, Rundong Gao, Linli Yao, Qingyan Guo, Kaikai An, Jianhong Bai, Xu Sun

LipoAgent: Coordinating Fine-Tuned LLM Agents for Safer Lipid Design (2026.findings-acl)

Challenge:	Existing models for text-to-image generation have been underperforming in image-totext generation tasks.
Approach:	They propose a framework that uses a split BERT to create a dedicated latent space for captions and integrates a regularization module to manage varying text lengths.
Outcome:	The proposed framework achieves state-of-the-art performance on the MS COCO dataset with 38.2 BLEU@4 and 126.2 CIDEr .

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Leshu Li, An Lu, Haiyu Wang, Zhibin Feng, Conghui Duan, Qing Bao, Zongmin Zhao, Sai Qian Zhang

Green & Sustainable NLP

Challenge:	Lipid nanoparticles (LNPs) are among the most clinically mature platforms for nucleic acid delivery, yet designing lipids that are effective and biologically safe remains a major bottleneck.
Approach:	They propose a safety-aware multi-agent LLM framework for lipid discovery that enforces toxicity as a prerequisite for efficiency prediction.
Outcome:	The proposed framework achieves an average improvement in mRNA transfection efficiency prediction across multiple foundation models.

Repulsive Attention: Rethinking Multi-head Attention as Bayesian Inference (2020.emnlp-main)

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Bang An, Jie Lyu, Zhenyi Wang, Chunyuan Li, Changwei Hu, Fei Tan, Ruiyi Zhang, Yifan Hu, Changyou Chen

Multimodal Invariant Sentiment Representation Learning (2025.findings-acl)

Challenge:	Existing studies show that multi-head attention is an effective module in deep neural networks, but there are no explicit mechanisms guaranteeing this property.
Approach:	They propose a non-parametric approach that explicitly improves the repulsiveness in multi-head attention and consequently strengthens model’s expressiveness.
Outcome:	The proposed approach improves the repulsiveness in multi-head attention and strengthens model’s expressiveness.

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Aoqiang Zhu, Min Hu, Xiaohua Wang, Jiaoyun Yang, Yiming Tang, Ning An

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

Challenge:	Existing methods for multimodal sensing ignore significant sentiment distribution imbalances and cross-modal sentiment conflicts, hindering performance improvement.
Approach:	They propose a method to learn stable multimodal invariant sentiment representations by incorporating distributional discrepancies and sentiment conflicts into the model training.
Outcome:	The proposed method improves MSA performance and achieves new state-of-the-art.

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

Reasoning

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

Semi-Supervised Formality Style Transfer with Consistency Training (2022.acl-long)

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Ao Liu, An Wang, Naoaki Okazaki

Low-Resource NLP

Challenge:	Formality style transfer (FST) is a task that involves paraphrasing an informal sentence into a formal one without altering its meaning.
Approach:	They propose a semi-supervised framework to better utilize source-side unlabeled sentences based on consistency training.
Outcome:	The proposed framework can achieve state-of-the-art results even with less than 40% of the parallel data.

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

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

MorphoBench: A Benchmark with Difficulty Adaptive to Model Reasoning (2026.findings-acl)

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

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Xukai Wang, Xuanbo Liu, Mingrui Chen, Haitian Zhong, Xuanlin Yang, Bohan Zeng, Jinbo Hu, Hao Liang, Junbo Niu, Xuchen Li, Ruitao Wu, Ruichuan An, Yang Shi, Liu Liu, Qiang Liu, Zhouchen Lin, Xu-Yao Zhang, Wentao Zhang, Bin Dong

Reasoning

Challenge:	Existing benchmarks designed to evaluate the reasoning capabilities of large models are limited in scope and lack flexibility to adapt difficulty according to evolving reasoning capacities of models.
Approach:	They propose a benchmark that incorporates multidisciplinary questions to evaluate the reasoning capabilities of large models and can adjust and update question difficulty based on the reasoning abilities of advanced models.
Outcome:	The proposed benchmark incorporates multidisciplinary questions to evaluate the reasoning capabilities of large models and can adjust and update question difficulty based on the reasoning abilities of advanced models.

StepSearch: Igniting LLMs Search Ability via Step-Wise Proximal Policy Optimization (2025.emnlp-main)

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Xuhui Zheng, Kang An, Ziliang Wang, Yuhang Wang, Yichao Wu

Information Retrieval

Challenge:	Recent work has demonstrated unprecedented capabilities in sophisticated linguistic comprehension and generative tasks.
Approach:	They propose a framework for search LLMs that trains with step-wise proximal policy optimization method to improve QA performance.
Outcome:	The proposed framework outperforms global-reward benchmarks on multi-hop QA with a stepwise proximal policy optimization method and richer and more detailed intermediate search rewards and token-level process supervision.

Sketch and Refine: Towards Faithful and Informative Table-to-Text Generation (2021.findings-acl)

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Peng Wang, Junyang Lin, An Yang, Chang Zhou, Yichang Zhang, Jingren Zhou, Hongxia Yang

Structured Data in NLP

Challenge:	Existing methods for table-to-text generation suffer from poor faithfulness and low coverage.
Approach:	They propose a method that combines Autoregressive and Non-Autoregressive generation to generate a table-to-text from a key-value table using a skeleton and an edit-based non-autoregressively generation model.
Outcome:	The proposed method outperforms the existing methods on WikiPerson and WikiBio datasets on coverage and faithfulness.

Rethinking Semantic Parsing for Large Language Models: Enhancing LLM Performance with Semantic Hints (2025.acl-short)

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Kaikai An, Shuzheng Si, Helan Hu, Haozhe Zhao, Yuchi Wang, Qingyan Guo, Baobao Chang

HMoE: Heterogeneous Mixture of Experts for Language Modeling (2025.emnlp-main)

Challenge:	Semantic Parsing improves performance of smaller models, but it is unclear whether it extends similarly to large language models.
Approach:	They propose a prompting approach that embeds semantic hints within the prompt to improve LLM performance.
Outcome:	The proposed approach improves LLMs’ performance across various tasks, highlighting the potential of integrating semantic information to improve LLM capabilities.

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An Wang, Xingwu Sun, Ruobing Xie, Shuaipeng Li, Jiaqi Zhu, Zhen Yang, Pinxue Zhao, Weidong Han, Zhanhui Kang, Di Wang, Naoaki Okazaki, Cheng-zhong Xu

Sparsifying Mamba (2025.findings-emnlp)

Challenge:	Mixture of Experts (MoE) models use homogeneous experts with diverse capacities, resulting in a lack of expert specialization and parameter utilization.
Approach:	They propose a framework where experts differ in size and possess diverse capacities . they propose HMoE to encourage frequent activation of smaller experts .
Outcome:	The proposed framework outperforms homogeneous homogenous MoE models on evaluation benchmarks and achieves lower loss rate with fewer activated parameters.

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An Wang, Ruobing Xie, Shuaipeng Li, Xingwu Sun, Zhanhui Kang

Fine-grained Category Discovery under Coarse-grained supervision with Hierarchical Weighted Self-contrastive Learning (2022.emnlp-main)

Challenge:	Existing attempts to integrate sparsification with Mamba fail to leverage Mamba's internal structure for fine-grained sparsifying.
Approach:	They propose to use Mamba to integrate sparsification into Mamba and propose a flexible and effective mechanism for parameter scalability.
Outcome:	The proposed framework can independently achieve parameter scalability and has stronger performance.

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Wenbin An, Feng Tian, Ping Chen, Siliang Tang, Qinghua Zheng, QianYing Wang

ImaginE: An Imagination-Based Automatic Evaluation Metric for Natural Language Generation (2023.findings-eacl)

Challenge:	Existing methods for novel category discovery focus on the scenario where known and novel categories are of the same granularity.
Approach:	They propose a novel scenario for fine-grained category discovery under coarse-grain supervision that allows for adapting models to categories of different granularity from known ones.
Outcome:	The proposed model can adapt models to categories of different granularity from known ones and reduce labeling cost.

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Wanrong Zhu, Xin Wang, An Yan, Miguel Eckstein, William Yang Wang

Generative Data Augmentation for Aspect Sentiment Quad Prediction (2023.starsem-1)

Challenge:	Existing evaluation methods for natural language generation rely on token-level or embedding-level comparisons with text references.
Approach:	They propose to use text-to-image generator to generate an image as the embodied imagination for the text snippet and compute the imagination similarity using contextual embeddings.
Outcome:	The proposed metric improves existing evaluation metrics’ correlations with human similarity judgments in both reference-based and reference-free scenarios.

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An Wang, Junfeng Jiang, Youmi Ma, Ao Liu, Naoaki Okazaki

Sentiment Analysis

Challenge:	Existing approaches to analyze text contain rewrites and inconsistency between text and quads.
Approach:	They propose a new approach to analyze aspect terms, opinion terms, sentiment polarity in text . they augment quads and train a quads-to-text model to generate corresponding texts .
Outcome:	The proposed method outperforms existing methods and achieves state-of-the-art performance on two datasets.

RolePlot: A Systematic Framework for Evaluating and Enhancing the Plot-Progression Capabilities of Role-Playing Agents (2025.acl-long)

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Pinyi Zhang, Siyu An, Lingfeng Qiao, Yifei Yu, Jingyang Chen, Jie Wang, Di Yin, Xing Sun, Kai Zhang

Dialogue Systems & Conversational Agents

Challenge:	Existing research has focused on role-playing agents’ ability to portray specified characters, but their ability to advance the plot requires substantial improvements to deliver more engaging interaction.
Approach:	They propose a role-playing framework to evaluate and enhance the plot-progression capabilities of role-players.
Outcome:	The proposed framework improves RPAs’ ability to time plot developments and yields a significant increase in conversation turns and sustained higher arousal levels.

Zero-Shot Spoken Language Understanding via Large Language Models: A Preliminary Study (2024.lrec-main)

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Zhihong Zhu, Xuxin Cheng, Hao An, Zhichang Wang, Dongsheng Chen, Zhiqi Huang

Aligning Large Language Models to Follow Instructions and Hallucinate Less via Effective Data Filtering (2025.acl-long)

Challenge:	Recent advances in large language models (LLMs) have shown promising results in zero-shot settings, which motivates us to explore prompt-based methods.
Approach:	They propose a two-stage framework which transforms the SLU task into a question-answering problem by directly prompting LLMs.
Outcome:	The proposed framework can be built by directly prompting LLMs to understand user needs without training data.

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Shuzheng Si, Haozhe Zhao, Gang Chen, Cheng Gao, Yuzhuo Bai, Zhitong Wang, Kaikai An, Kangyang Luo, Chen Qian, Fanchao Qi, Baobao Chang, Maosong Sun

Proxy-Driven Robust Multimodal Sentiment Analysis with Incomplete Data (2025.acl-long)

Challenge:	Existing studies show that training LLMs on data containing unfamiliar knowledge during instruction tuning can encourage hallucinations.
Approach:	They propose a framework that measures how familiar the LLM is with instruction data and introduce an expert-aligned reward model to ensure the quality of selected samples.
Outcome:	The proposed framework reduces hallucinations while maintaining a competitive ability to follow instructions.

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Aoqiang Zhu, Min Hu, Xiaohua Wang, Jiaoyun Yang, Yiming Tang, Ning An

Robustness in NLP

Challenge:	Existing studies focus on optimizing model structures to handle uncertain missingness, but models still face challenges when dealing with uncertain missing data.
Approach:	They propose a data-centric robust multimodal sentiment analysis method, Proxy-Driven Robust Multimodal Fusion, which maps unimodal data to the latent space of Gaussian distributions to capture core features and structure.
Outcome:	The proposed method outperforms existing models in noise resistance and achieves state-of-the-art performance on multiple benchmark datasets.

The Price of Format: Diversity Collapse in LLMs (2025.findings-emnlp)

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Longfei Yun, Chenyang An, Zilong Wang, Letian Peng, Jingbo Shang

Interventional Rationalization (2023.emnlp-main)

Challenge:	Instruction-tuned large language models employ structured templates to enforce format consistency during inference.
Approach:	They fine-tune instruction-tuning large language models with structured templates and evaluate their results across three axes: downstream task performance, alignment behavior, and output diversity.
Outcome:	The proposed model generates semantically similar outputs even under high temperature sampling and structural tokens in templates significantly constrain the model’s output space.

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Linan Yue, Qi Liu, Li Wang, Yanqing An, Yichao Du, Zhenya Huang

Responsible & Trustworthy NLP

Challenge:	Existing methods for rationalization use spurious correlations in data to compose rationales and make predictions.
Approach:	They propose a method to discover the causal rationales by using a structural causal model.
Outcome:	The proposed method is based on the causal theory and validates on three real-world datasets.

SeDev: Structured Semantic Exploration for LLM-Driven Code Generation (2026.acl-long)

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Ronghui Yang, Jie Liu, Jiajie Zeng, Jiexin Wang, Jiuchuan Jiang, Bo An, Yi Cai, Mengchen Zhao

Programming Languages in NLP

Challenge:	Large Language Models (LLMs) have shown remarkable capabilities in automating code generation, but they suffer from insufficient exploration of the vast solution space.
Approach:	They propose a large-scale LLM-driven code generation framework that efficiently finds high-quality solutions in only a few iterations.
Outcome:	The proposed framework outperforms baselines while maintaining reasonable time and computational costs.

Prompt Tuning for Unified Multimodal Pretrained Models (2023.findings-acl)

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Hao Yang, Junyang Lin, An Yang, Peng Wang, Chang Zhou

Do Large Language Models Discriminate in Hiring Decisions on the Basis of Race, Ethnicity, and Gender? (2024.acl-short)

Challenge:	Prompt tuning has demonstrated success in natural language pretraining and even vision pretraining.
Approach:	They propose to apply prompt tuning to a unified sequence-to-sequence pretrained model by adding a sequence of learnable embeddings to each layer and finetuning the pretrained models on downstream tasks.
Outcome:	The proposed method outperforms other parameter-efficient tuning methods on multimodal models and is robust against adversarial attacks.

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Haozhe An, Christabel Acquaye, Colin Wang, Zongxia Li, Rachel Rudinger

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

Challenge:	We study whether large language models exhibit race- and gender-based name discrimination in hiring decisions .
Approach:	They propose templatic prompts to LLMs to write an email to a named job applicant informing them of a hiring decision.
Outcome:	The proposed model generates an acceptance or rejection email based on the applicant's first name .

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

GBV-SQL: Guided Generation and SQL2Text Back-Translation Validation for Multi-Agent Text2SQL (2026.acl-long)

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

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Daojun Chen, Xi Wang, Shenyuan Ren, Qingzhi Ma, Pengpeng Zhao, An Liu

Text Generation

Challenge:	Existing frameworks for Text2SQL generation still have a critical semantic gap . a dedicated validator translates generated SQL back into natural language and checks whether its logic is aligned with the original question.
Approach:	They propose a framework that introduces Guided Generation with SQL2Text Back-translation Validation . dedicated validator translates generated SQL back into natural language and checks whether logic is aligned with original question .
Outcome:	The proposed framework achieves 63.23% execution accuracy on the BIRD benchmark and 90.42% on repaired BIDR dev.

DaNet: Dual-Aware Enhanced Alignment Network for Multimodal Aspect-Based Sentiment Analysis (2025.findings-acl)

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Aoqiang Zhu, Min Hu, Xiaohua Wang, Jiaoyun Yang, Yiming Tang, Ning An

Sentiment Analysis

Challenge:	Existing methods assume a direct alignment between images and aspects, matching the entire image with a corresponding aspect. Existing algorithms assume 'direct alignment' between images, introducing noise.
Approach:	They propose a Dual-Aware Enhanced Alignment Network (DaNet) that can enhance fine-grained multimodal aspect-image alignment and denoising.
Outcome:	The proposed system outperforms existing methods in three subtasks and is available on https://github.com/***/DaNet.

FLUID QA: A Multilingual Benchmark for Figurative Language Usage in Dialogue across English, Chinese, and Korean (2025.emnlp-main)

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Seoyoon Park, Hyeji Choi, Minseon Kim, Subin An, Xiaonan Wang, Gyuri Choi, Hansaem Kim

Visualize Before You Write: Imagination-Guided Open-Ended Text Generation (2023.findings-eacl)

Challenge:	Figurative language is a core component of everyday communication . existing benchmarks focus on sentence-level classification or inference tasks .
Approach:	They propose a multilingual benchmark that evaluates figurative usage in dialogue . they use a sentence-level diagnostic task to embed figurativ choices into multi-turn contexts .
Outcome:	The benchmark evaluates large language models' ability to use figurative expressions coherently in conversation.

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Wanrong Zhu, An Yan, Yujie Lu, Wenda Xu, Xin Wang, Miguel Eckstein, William Yang Wang

Generalized Category Discovery with Large Language Models in the Loop (2024.findings-acl)

Challenge:	Existing tools for text-to-image synthesis can visualize machine imaginations for a given context.
Approach:	They propose a framework that uses machine-generated images to guide language models in open-ended text generation.
Outcome:	The proposed framework is effective on open-ended text generation tasks while showing minor degeneration.

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Wenbin An, Wenkai Shi, Feng Tian, Haonan Lin, QianYing Wang, Yaqiang Wu, Mingxiang Cai, Luyan Wang, Yan Chen, Haiping Zhu, Ping Chen

AceGPT, Localizing Large Language Models in Arabic (2024.naacl-long)

Challenge:	Generalized Category Discovery (GCD) is a crucial task that aims to recognize both known and novel categories from a set of unlabeled data.
Approach:	They propose a framework that introduces Large Language Models into the training loop to generate category names without human effort.
Outcome:	The proposed framework outperforms SOTA models on three benchmark datasets and generates accurate category names for the discovered clusters.

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Huang Huang, Fei Yu, Jianqing Zhu, Xuening Sun, Hao Cheng, Song Dingjie, Zhihong Chen, Mosen Alharthi, Bang An, Juncai He, Ziche Liu, Junying Chen, Jianquan Li, Benyou Wang, Lian Zhang, Ruoyu Sun, Xiang Wan, Haizhou Li, Jinchao Xu

MusTQ: A Temporal Knowledge Graph Question Answering Dataset for Multi-Step Temporal Reasoning (2024.findings-acl)

Challenge:	Significant concerns emerge when addressing cultural sensitivity and local values.
Approach:	They propose a localized Large Language Model (LLM) specifically for Arabic, a language imbued with unique cultural characteristics inadequately addressed by current mainstream models.
Outcome:	The proposed model sets the state-of-the-art standard for open Arabic LLMs across various benchmarks.

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Tingyi Zhang, Jiaan Wang, Zhixu Li, Jianfeng Qu, An Liu, Zhigang Chen, Hongping Zhi

A Diffusion Weighted Graph Framework for New Intent Discovery (2023.emnlp-main)

Challenge:	Existing studies focus on fact-centered reasoning with limited attention to temporal reasoning.
Approach:	They propose a new TKGQA dataset, MusTQ, which contains 666K multi-step temporal reasoning questions and a TKG.
Outcome:	The proposed model achieves state-of-the-art multi-step temporal reasoning ability with entity-time attention mechanism and optimized temporal knowledge graph representation.

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Wenkai Shi, Wenbin An, Feng Tian, Qinghua Zheng, QianYing Wang, Ping Chen

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

Challenge:	Existing methods to learn from unlabeled data generate noisy supervisory signals . current methods only rely on semantic similarities to generate supervisory signal .
Approach:	They propose a weighted DWGF framework to capture semantic similarities and structure relationships in data.
Outcome:	The proposed method outperforms state-of-the-art models on evaluation metrics across multiple benchmark datasets.

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

Multimodal Text Style Transfer for Outdoor Vision-and-Language Navigation (2021.eacl-main)

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

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Wanrong Zhu, Xin Wang, Tsu-Jui Fu, An Yan, Pradyumna Narayana, Kazoo Sone, Sugato Basu, William Yang Wang

Enhancing Pre-Trained Language Representations with Rich Knowledge for Machine Reading Comprehension (P19-1)

Challenge:	Outdoor vision-and-language navigation (VLN) tasks require visual grounding to generate correct actions.
Approach:	They propose a multimodal text style transfer learning approach to mitigate data scarcity in outdoor vision-and-language navigation tasks.
Outcome:	The proposed approach outperforms baseline models on the outdoor vision-and-language navigation task, improving task completion rate by 8.7% relative to the baseline models.

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An Yang, Quan Wang, Jing Liu, Kai Liu, Yajuan Lyu, Hua Wu, Qiaoqiao She, Sujian Li

Transferring General Multimodal Pretrained Models to Text Recognition (2023.findings-acl)

Challenge:	Recent results show pre-trained language models (LMs) can improve machine reading comprehension (MRC) Experimental results indicate that KT-NET offers significant and consistent improvements over BERT .
Approach:	They propose a method that leverages external knowledge bases to improve machine reading comprehension (MRC) KT-NET employs an attention mechanism to select desired knowledge from KBs and fuses selected knowledge with BERT to enable context- and knowledge-aware predictions.
Outcome:	The proposed model outperforms baseline models on ReCoRD and SQuAD1.1 benchmarks and ranks 1st on the ReCoDR and SQUAD1.1 leaderboards.

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Junyang Lin, Xuancheng Ren, Yichang Zhang, Gao Liu, Peng Wang, An Yang, Chang Zhou

DREEAM: Guiding Attention with Evidence for Improving Document-Level Relation Extraction (2023.eacl-main)

Challenge:	Existing methods for text recognition rely on large-scale pretraining on human-annotated or synthetic data.
Approach:	They propose a method to transfer multimodal pretrained models to text recognition using image captioning.
Outcome:	The proposed method outperforms the baselines and achieves state-of-the-art performance in the Chinese text recognition benchmark.

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Youmi Ma, An Wang, Naoaki Okazaki

Information Extraction & Text Mining

Challenge:	Document-level relation extraction (DocRE) is a task of identifying relations between entities in a document. evidence retrieval (ER) in DocRE faces two major issues: high memory consumption and limited availability of annotations.
Approach:	They propose a memory-efficient approach that uses evidence as the supervisory signal . they propose er self-training to learn ER from automatically-generated evidence .
Outcome:	The proposed method exhibits state-of-the-art performance on the DocRED benchmark . it uses evidence as the supervisory signal and self-trains on massive data without annotations .

Neuron-Level Sequential Editing for Large Language Models (2025.acl-long)

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Houcheng Jiang, Junfeng Fang, Tianyu Zhang, Baolong Bi, An Zhang, Ruipeng Wang, Tao Liang, Xiang Wang

LEGENT: Open Platform for Embodied Agents (2024.acl-demos)

Challenge:	Existing model editing methods focus on single-round editing and often face significant challenges in sequential model editing.
Approach:	They propose a model editing method that optimizes the target layer’s hidden states using the model’s original weights to prevent model failure.
Outcome:	The proposed method outperforms existing model editing methods and is available on the open-source platform 4open.science.

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Zhili Cheng, Zhitong Wang, Jinyi Hu, Shengding Hu, An Liu, Yuge Tu, Pengkai Li, Lei Shi, Zhiyuan Liu, Maosong Sun