Papers by Nan Wang

End-to-End Synthetic Data Generation for Domain Adaptation of Question Answering Systems (2020.emnlp-main)

Challenge:	Adapting a model to a handful of personalized data is challenging, authors say . standard fine-tuning requires hundreds of millions of parameters for each user .
Approach:	They propose a lightweight method that clamps millions of parameters of a Transformer model and optimizes a tiny user-specific vector.
Outcome:	The proposed method improves accuracy on Yelp and IMDB datasets and reduces the number of parameters added for each user.

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Siamak Shakeri, Cicero Nogueira dos Santos, Henghui Zhu, Patrick Ng, Feng Nan, Zhiguo Wang, Ramesh Nallapati, Bing Xiang

Fusion meets Function: The Adaptive Selection-Generation Approach in Event Argument Extraction (2025.coling-main)

Challenge:	Existing approaches for synthetic QA data generation have limited or no success in improving the downstream Reading Comprehension task.
Approach:	They propose an end-to-end approach for synthetic QA data generation using a transformer-based encoder-decoder network that is trained end- to-end to generate both answers and questions.
Outcome:	The proposed model outperforms current state-of-the-art methods in the domain adaptation of QA models.

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Guoxuan Ding, Xiaobo Guo, Xin Wang, Lei Wang, Tianshu Fu, Nan Mu, Daren Zha

Two Pathways to Truthfulness: On the Intrinsic Encoding of LLM Hallucinations (2026.acl-long)

Challenge:	Event Argument Extraction is a critical subtask of Event Extraction, focused on identifying event arguments within text.
Approach:	They propose a Fusion Selection-Generation-Based Approach that merges selective and generative methods to enhance argument extraction accuracy.
Outcome:	The proposed method improves on the RAMS and WikiEvents, while preserving the unique characteristics of both methods.

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Wen Luo, Guangyue Peng, Wei Li, Shaohang Wei, Feifan Song, Liang Wang, Nan Yang, Xingxing Zhang, Jing Jin, Furu Wei, Houfeng Wang

LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing (2024.emnlp-main)

Challenge:	Previous work shows that large language models generate hallucinations, yet the origins and mechanisms of these signals remain unclear.
Approach:	They propose to validate and disentangle two different pathways for truthfulness cues . they also propose to use the same mechanism to derive self-contained evidence from the generated answer .
Outcome:	The proposed applications improve hallucination detection performance by integrating two different inputs.

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Jiangshu Du, Yibo Wang, Wenting Zhao, Zhongfen Deng, Shuaiqi Liu, Renze Lou, Henry Zou, Pranav Narayanan Venkit, Nan Zhang, Mukund Srinath, Haoran Zhang, Vipul Gupta, Yinghui Li, Tao Li, Fei Wang, Qin Liu, Tianlin Liu, Pengzhi Gao, Congying Xia, Chen Xing, Cheng Jiayang, Zhaowei Wang, Ying Su, Raj Shah, Ruohao Guo, Jing Gu, Haoran Li, Kangda Wei, Zihao Wang, Lu Cheng, Surangika Ranathunga, Meng Fang, Jie Fu, Fei Liu, Ruihong Huang, Eduardo Blanco, Yixin Cao, Rui Zhang, Philip Yu, Wenpeng Yin

Challenge:	a comparative analysis of paper (meta-)reviews by large language models (LLMs) aims to identify and distinguish LLMs from human activities .
Approach:	They present a comparative analysis to identify and distinguish LLM activities from human activities.
Outcome:	The proposed analysis aims to improve recognition of instances when someone implicitly uses LLMs for reviewing activities.

HiCLRE: A Hierarchical Contrastive Learning Framework for Distantly Supervised Relation Extraction (2022.findings-acl)

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Dongyang Li, Taolin Zhang, Nan Hu, Chengyu Wang, Xiaofeng He

ImaRA: An Imaginative Frame Augmented Method for Low-Resource Multimodal Metaphor Detection and Explanation (2025.findings-naacl)

Challenge:	Existing approaches of distantly supervised relation extraction (DSRE) focus on sentence-level or bag-level de-noising, neglecting the explicit interaction with cross levels.
Approach:	They propose a hierarchical contrastive learning framework for distantly supervised relation extraction to reduce noisy sentences.
Outcome:	The proposed framework outperforms baselines in various mainstream DSRE datasets.

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Yuan Tian, Minzheng Wang, Nan Xu, Wenji Mao

RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework (2025.acl-long)

Challenge:	Existing methods for multimodal metaphor detection neglect cross-domain and attribute similarity characteristics underlying multimodal understanding.
Approach:	They propose an Imaginative FRame Augmented method for multimodal metaphor detection and explanation . they use a cross-modal imagination dataset rich in multimodal multimodal expressions .
Outcome:	The proposed method outperforms existing methods with training data on two datasets.

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Kunlun Zhu, Yifan Luo, Dingling Xu, Yukun Yan, Zhenghao Liu, Shi Yu, Ruobing Wang, Shuo Wang, Yishan Li, Nan Zhang, Xu Han, Zhiyuan Liu, Maosong Sun

CodeExp: Explanatory Code Document Generation (2022.findings-emnlp)

Challenge:	Existing evaluation metrics for RAG systems are lacking due to high costs of data construction and lack of factual accuracy.
Approach:	They propose a framework to evaluate RAG systems in specialized scenarios . they propose three new metrics to evaluate LLM-generated responses .
Outcome:	The proposed framework outperforms zero-shot and one-shot methods in terms of clarity, safety, conformity, and richness of generated samples.

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Haotian Cui, Chenglong Wang, Junjie Huang, Jeevana Priya Inala, Todd Mytkowicz, Bo Wang, Jianfeng Gao, Nan Duan

Explainability & Interpretability in NLP

Challenge:	Existing code-to-text generation models produce only high-level code summaries that do not capture implementation-level choices essential for these scenarios.
Approach:	They propose a code explanation generation task that uses code docstrings to refine models.
Outcome:	The proposed model can generate well-structured long docstrings comparable to human-written ones.

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

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

Visual Data in NLP

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

A Survey on Efficient Large Language Model Training: From Data-centric Perspectives (2025.acl-long)

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Junyu Luo, Bohan Wu, Xiao Luo, Zhiping Xiao, Yiqiao Jin, Rong-Cheng Tu, Nan Yin, Yifan Wang, Jingyang Yuan, Wei Ju, Ming Zhang

ProQA: Structural Prompt-based Pre-training for Unified Question Answering (2022.naacl-main)

Challenge:	achieving data-efficient post-training of Large Language Models is a key research question.
Approach:	They propose a taxonomy of data-efficient LLM post-training methods from a data-centric perspective.
Outcome:	The proposed methods cover data selection, data quality enhancement, synthetic data generation, data distillation and compression, and self-evolving data ecosystems.

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Wanjun Zhong, Yifan Gao, Ning Ding, Yujia Qin, Zhiyuan Liu, Ming Zhou, Jiahai Wang, Jian Yin, Nan Duan

Title2Event: Benchmarking Open Event Extraction with a Large-scale Chinese Title Dataset (2022.emnlp-main)

Challenge:	Existing QA research on question answering is focused on specific question types, knowledge domains, or reasoning skills.
Approach:	They propose a unified QA paradigm that solves various tasks through a single model.
Outcome:	The proposed model improves QA-centric ability on 11 QA benchmarks.

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Haolin Deng, Yanan Zhang, Yangfan Zhang, Wangyang Ying, Changlong Yu, Jun Gao, Wei Wang, Xiaoling Bai, Nan Yang, Jin Ma, Xiang Chen, Tianhua Zhou

Prompt Space Optimizing Few-shot Reasoning Success with Large Language Models (2024.findings-naacl)

Challenge:	Existing EE datasets define fixed event types and design specific schemas for each of them, failing to cover diverse events emerging from the online text.
Approach:	They propose to use a sentence-level dataset to benchmark Open Event Extraction without restricting event types.
Outcome:	The proposed dataset contains more than 42,000 news titles in 34 topics collected from Chinese web pages.

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Fobo Shi, Peijun Qing, Dong Yang, Nan Wang, Youbo Lei, Haonan Lu, Xiaodong Lin, Duantengchuan Li

Cognitive Overload: Jailbreaking Large Language Models with Overloaded Logical Thinking (2024.findings-naacl)

Challenge:	Prompt engineering is an essential technique for enhancing the abilities of large language models (LLMs) by providing explicit and specific instructions.
Approach:	They propose a new approach that uses text embeddings to obtain basis vectors by matrix decomposition and constructs a space for representing all prompts.
Outcome:	The proposed approach significantly outperforms state-of-the-art prompt paradigms on ten public reasoning benchmarks.

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Nan Xu, Fei Wang, Ben Zhou, Bangzheng Li, Chaowei Xiao, Muhao Chen

Robustness in NLP

Challenge:	Large language models (LLMs) have demonstrated increasing power, but they also have vulnerabilities.
Approach:	They propose a black-box attack that targets the cognitive structure and processes of large language models (LLMs) they propose defending cognitive overload attacks from three perspectives.
Outcome:	The proposed attack is a black-box attack with no need for knowledge of model architecture or access to model weights.

RATE-Nav: Region-Aware Termination Enhancement for Zero-shot Object Navigation with Vision-Language Models (2025.findings-acl)

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Junjie Li, Nan Zhang, Xiaoyang Qu, Kai Lu, Guokuan Li, Jiguang Wan, Jianzong Wang

FIBER: Fill-in-the-Blanks as a Challenging Video Understanding Evaluation Framework (2022.acl-long)

Challenge:	Object navigation is a fundamental task in embodied artificial intelligence.
Approach:	They propose a region-aware Termination-Enhanced method that incorporates visual language models and exploration rates to enable efficient termination.
Outcome:	The proposed method achieves a success rate of 67.8% and an SPL of 31.3% on the HM3D dataset.

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Santiago Castro, Ruoyao Wang, Pingxuan Huang, Ian Stewart, Oana Ignat, Nan Liu, Jonathan Stroud, Rada Mihalcea

Is the Brain Mechanism for Hierarchical Structure Building Universal Across Languages? An fMRI Study of Chinese and English (2022.emnlp-main)

Challenge:	Existing video understanding evaluation frameworks that use fill-in-the-blanks do not reflect real-world tasks.
Approach:	They propose to use fill-in-the-blanks as a video understanding evaluation framework and introduce a novel dataset that collects multiple perspectives on the same video.
Outcome:	The proposed framework does not share the weaknesses of the current state-of-the-art language-informed video understanding tasks, namely: (1) video question answering using multiple-choice questions, where models perform relatively well because they exploit linguistic biases in the task formulation; (2) video captioning, which relies on an open-ended evaluation framework that is often inaccurate because system answers may be perceived as incorrect if they differ in form from the ground truth.

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Xiaohan Zhang, Shaonan Wang, Nan Lin, Chengqing Zong

Syntactic Text Processing

Challenge:	Existing studies have shown that the brain builds hierarchical syntactic structures, but it is unknown whether they are universal across languages.
Approach:	They analyze the working memory requirements when applying parsing strategies to two languages: Chinese and English.
Outcome:	The proposed method shows that the brain adopts parsing strategies with less memory load according to different language structures.

Federated LoRA Fine-Tuning with Pipelined Error-Mitigated Aggregation and Matrix-Wise Freezing (2026.findings-acl)

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Haoran Wang, Xiong Wang, Yuqing Li, Jing Chen, Junyi Zhang, Nan Yan, Kun He, Wei Wang

Asking Clarification Questions in Knowledge-Based Question Answering (D19-1)

Challenge:	Existing methods for fine-tuning large language models often suffer from biased model aggregation and are hindered by significant communication and computation burden.
Approach:	They propose a Federated low-rank adaptation system for large language models that leverages pipelined error-mitigated model aggregation and adaptive matrix-wise parameter freezing to mitigate aggregations.
Outcome:	The proposed system improves time-to-target by 2.17-8.48 on real-world datasets.

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Jingjing Xu, Yuechen Wang, Duyu Tang, Nan Duan, Pengcheng Yang, Qi Zeng, Ming Zhou, Xu Sun

Mitigating the Alignment Tax of RLHF (2024.emnlp-main)

Challenge:	Existing clarification datasets with limited annotated examples do not address ambiguous phenomena.
Approach:	They propose a dataset that allows users to ask clarification questions using open-domain examples.
Outcome:	The proposed model achieves better performance than strong baselines and provides new challenges.

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Yong Lin, Hangyu Lin, Wei Xiong, Shizhe Diao, Jianmeng Liu, Jipeng Zhang, Rui Pan, Haoxiang Wang, Wenbin Hu, Hanning Zhang, Hanze Dong, Renjie Pi, Han Zhao, Nan Jiang, Heng Ji, Yuan Yao, Tong Zhang

OpenT2T: An Open-Source Toolkit for Table-to-Text Generation (2024.emnlp-demo)

Challenge:	Large Language Models (LLMs) acquire a wide range of abilities during pre-training, but aligning LLMs under Reinforcement Learning with Human Feedback (RLHF) can lead to forgetting pretrained abilities, which is also known as the alignment tax.
Approach:	They propose to use a model averaging technique to find the most powerful alignment-forging Pareto front among RLHF algorithms.
Outcome:	The proposed method achieves the strongest alignment-forging Pareto front among competing methods.

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Haowei Zhang, Shengyun Si, Yilun Zhao, Lujing Xie, Zhijian Xu, Lyuhao Chen, Linyong Nan, Pengcheng Wang, Xiangru Tang, Arman Cohan

FormNet: Structural Encoding beyond Sequential Modeling in Form Document Information Extraction (2022.acl-long)

Challenge:	Existing methods for table-to-text generation are limited and benchmarked on a limited number of datasets.
Approach:	They propose to use open-source tools to reproduce existing large language models for performance comparison and expedite the development of new models.
Outcome:	The proposed toolkit compares existing large language models on 9 table-to-text generation datasets and maintains a leaderboard to provide insights for future work.

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Chen-Yu Lee, Chun-Liang Li, Timothy Dozat, Vincent Perot, Guolong Su, Nan Hua, Joshua Ainslie, Renshen Wang, Yasuhisa Fujii, Tomas Pfister

From Introspection to Best Practices: Principled Analysis of Demonstrations in Multimodal In-Context Learning (2025.naacl-long)

Challenge:	Form-like document understanding is a surging research topic due to its practical applications . form documents have unique challenges stemming from their structural characteristics .
Approach:	They propose a structure-aware sequence model that leverages spatial relationships between tokens in a form for more precise attention score calculation.
Outcome:	The proposed model outperforms existing methods with a more compact model size and less pre-training data.

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Nan Xu, Fei Wang, Sheng Zhang, Hoifung Poon, Muhao Chen

Low-Resource NLP

Challenge:	Motivated by in-context learning capabilities of Large Language Models (LLMs), multimodal LLMs with additional visual modality are also exhibited with similar ICL abilities when multiple image-text pairs are provided as demonstrations.
Approach:	They conduct systematic and principled evaluation of multimodal ICL for models of different scales on a broad spectrum of new yet critical tasks.
Outcome:	The proposed model performance improves on a broad spectrum of new yet critical tasks.

Logic-Driven Context Extension and Data Augmentation for Logical Reasoning of Text (2022.findings-acl)

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Siyuan Wang, Wanjun Zhong, Duyu Tang, Zhongyu Wei, Zhihao Fan, Daxin Jiang, Ming Zhou, Nan Duan

Answering Ambiguous Questions through Generative Evidence Fusion and Round-Trip Prediction (2021.acl-long)

Challenge:	Existing methods for logical reasoning of text focus on contextual semantics while struggling to explicitly model the logical inference process.
Approach:	They propose a logic-driven context extension framework and a data-driven augmentation algorithm that uses contrastive learning to better capture logical information.
Outcome:	The proposed framework outperforms existing methods on two benchmark datasets, ReClor and LogiQA.

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Yifan Gao, Henghui Zhu, Patrick Ng, Cicero Nogueira dos Santos, Zhiguo Wang, Feng Nan, Dejiao Zhang, Ramesh Nallapati, Andrew O. Arnold, Bing Xiang

Inspecting Unification of Encoding and Matching with Transformer: A Case Study of Machine Reading Comprehension (D19-58)

Challenge:	Open-domain question answering is a task to answer questions using passages with diverse topics.
Approach:	They propose a model that aggregates evidence from multiple passages to adaptively predict a single answer or a set of question-answer pairs for ambiguous questions.
Outcome:	The proposed model achieves state-of-the-art performance on AmbigQA dataset and shows competitive performance on NQ-Open and TriviaQA.

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Hangbo Bao, Li Dong, Furu Wei, Wenhui Wang, Nan Yang, Lei Cui, Songhao Piao, Ming Zhou

MentalGLM Series: Explainable Large Language Models for Mental Health Analysis on Chinese Social Media (2025.emnlp-main)

Challenge:	Experimental results show that unified model outperforms other models that treat encoding and matching separately.
Approach:	They evaluate a unified model with Transformer layers for machine reading comprehension . they find that the model learns different modeling strategies compared with previous models .
Outcome:	The unified model outperforms models with Transformer layers on the machine reading comprehension task.

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Wei Zhai, Nan Bai, Qing Zhao, Jianqiang Li, Fan Wang, Hongzhi Qi, Meng Jiang, Xiaoqin Wang, Bing Xiang Yang, Guanghui Fu

No Answer is Better Than Wrong Answer: A Reflection Model for Document Level Machine Reading Comprehension (2020.findings-emnlp)

Challenge:	Social media is a key platform for emotional expression, yet deep learning lacks flexibility and interpretability.
Approach:	They propose to use Chinese social media to train interpretable mental health instruction datasets to test models' ability to explain their decisions.
Outcome:	The proposed models outperform deep learning and LLMs on three mental health downstream tasks and demonstrate their potential for clinical applications.

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Xuguang Wang, Linjun Shou, Ming Gong, Nan Duan, Daxin Jiang

Examining False Positives under Inference Scaling for Mathematical Reasoning (2025.emnlp-main)

Challenge:	Natural Questions (NQ) benchmark sets new challenges for machine reading comprehension.
Approach:	They propose a novel approach to handle all answer types systematically using a two-step training procedure.
Outcome:	The proposed approach achieved the top 1 on both long and short answer leaderboards with F1 scores of 77.2 and 64.1.

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Yu Wang, Nan Yang, Liang Wang, Furu Wei, Fuli Feng

MetaScale: Test-Time Scaling with Evolving Meta-Thoughts (2026.findings-acl)

Challenge:	Recent advances in language models have led to significant improvements in mathematical reasoning across benchmarks.
Approach:	They analyze the prevalence of false positives in language models by using heuristic evaluation methods . they find that false positive models produce correct final answers but with flawed deduction paths .
Outcome:	The proposed model performance improvements are based on the proposed model and its evaluation metrics.

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Qin Liu, Wenxuan Zhou, Nan Xu, James Y. Huang, Fei Wang, Sheng Zhang, Hoifung Poon, Muhao Chen

Low-code LLM: Graphical User Interface over Large Language Models (2024.naacl-demo)

Challenge:	Existing approaches impose fixed cognitive structures that enhance performance in specific tasks but lack adaptability across diverse scenarios.
Approach:	They propose a test-time scaling framework based on meta-thoughts to improve performance . meta-thinkts are adaptive thinking strategies tailored to a given task .
Outcome:	Experimental results show that MetaScale outperforms standard inference approaches . it can scale more effectively with increasing sampling budgets and produces more structured responses .

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Yuzhe Cai, Shaoguang Mao, Wenshan Wu, Zehua Wang, Yaobo Liang, Tao Ge, Chenfei Wu, WangYou WangYou, Ting Song, Yan Xia, Nan Duan, Furu Wei

Entity-level Factual Consistency of Abstractive Text Summarization (2021.eacl-main)

Challenge:	Low-code LLM is a visual programming interface that allows users to incorporate their ideas into the process without writing trivial prompts.
Approach:	They propose a human-LLM interaction framework that incorporates low-code visual programming interactions to achieve more controllable and stable responses.
Outcome:	The proposed framework enables users to incorporate ideas into the process without writing trivial prompts.

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Feng Nan, Ramesh Nallapati, Zhiguo Wang, Cicero Nogueira dos Santos, Henghui Zhu, Dejiao Zhang, Kathleen McKeown, Bing Xiang

Summarizing Medical Conversations via Identifying Important Utterances (2020.coling-main)

Challenge:	Existing models exhibit entity hallucination, generating names of entities that are not present in the source document.
Approach:	They propose to use entity-level factual consistency to improve model quality . they propose to filter the training data to reduce entity hallucination problem .
Outcome:	The proposed model can reduce the entity hallucination problem by filtering the training data.

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Yan Song, Yuanhe Tian, Nan Wang, Fei Xia

Summarization

Challenge:	Applying natural language processing (NLP) techniques to the medical field is a prevailing trend nowadays and has great potential in many applications, such as key information extraction in medical literature.
Approach:	They propose to use a hierarchical encoder-tagger model to generate medical conversation summarization by identifying important utterances.
Outcome:	The proposed model outperforms baseline models and models and adds conversation-related features to improve performance.

Discontinuous Named Entity Recognition as Maximal Clique Discovery (2021.acl-long)

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Yucheng Wang, Bowen Yu, Hongsong Zhu, Tingwen Liu, Nan Yu, Limin Sun

Discovering Semantic Subdimensions through Disentangled Conceptual Representations (2025.findings-emnlp)

Challenge:	Existing methods for named entity recognition break the recognition process into several sequential steps.
Approach:	They propose a method that breaks the recognition process into several sequential steps . they construct a segment graph for each sentence and a grid tagging scheme to learn it .
Outcome:	Experiments show that the proposed method outperforms the state-of-the-art model and achieves 5x speedup over the SOTA model.

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Yunhao Zhang, Shaonan Wang, Nan Lin, Xinyi Dong, Chong Li, Chengqing Zong

Explainability & Interpretability in NLP

Challenge:	Existing approaches focus on predefined dimensions that overlook finer conceptual distinctions . a new framework is proposed to investigate the subdimensions underlying coarse-grained semantic dimensions .
Approach:	They propose a framework that decomposes word embeddings into multiple sub-embeddings . they propose to map these subdimensions to brain activation to assess their plausibility .
Outcome:	The proposed framework decomposes word embeddings from large language models into sub-embeddings, each encoding specific semantic information.

Joint Generator-Ranker Learning for Natural Language Generation (2023.findings-acl)

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Weizhou Shen, Yeyun Gong, Yelong Shen, Song Wang, Xiaojun Quan, Nan Duan, Weizhu Chen

Beyond Boundaries: Learning a Universal Entity Taxonomy across Datasets and Languages for Open Named Entity Recognition (2025.coling-main)

Challenge:	Existing methods for text generation train the generator and ranker individually . existing methods neglect the mutual feedback that could enhance the quality of outputs .
Approach:	They propose a joint training algorithm that integrates the generator and ranker in a single framework.
Outcome:	The proposed algorithm surpasses existing methods on four public datasets across three common generation scenarios.

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Yuming Yang, Wantong Zhao, Caishuang Huang, Junjie Ye, Xiao Wang, Huiyuan Zheng, Yang Nan, Yuran Wang, Xueying Xu, Kaixin Huang, Yunke Zhang, Tao Gui, Qi Zhang, Xuanjing Huang

Extracting Financial Events from Raw Texts via Matrix Chunking (2024.lrec-main)

Challenge:	Current Large Language Models struggle with complex entity taxonomies in open domains and lack NER capabilities.
Approach:	They propose a dataset to guide LLMs' generalization in Open NER under a universal entity taxonomy.
Outcome:	The proposed model outperforms GPT-4 in 3 out-of-domain benchmarks across 15 datasets and 6 languages.

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Yusheng Huang, Ning Hu, Kunping Li, Nan Wang, Zhouhan Lin

Chunking

Challenge:	Event Extraction (EE) is widely used in the Chinese financial field to provide valuable structured information.
Approach:	They propose a task which extracts financial events from raw texts and an efficient method called MACK.
Outcome:	The proposed method is fault-tolerant and can visualize interactions among text components.

S2R: Teaching LLMs to Self-verify and Self-correct via Reinforcement Learning (2025.acl-long)

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Ruotian Ma, Peisong Wang, Cheng Liu, Xingyan Liu, Jiaqi Chen, Bang Zhang, Xin Zhou, Nan Du, Jia Li

Measuring Data Diversity for Instruction Tuning: A Systematic Analysis and A Reliable Metric (2025.acl-long)

Challenge:	Existing approaches to incentivize LLMs’ deep thinking abilities require large-scale data or significant training efforts.
Approach:	They introduce an efficient framework that enhances LLM reasoning by teaching models to self-verify and self-correct during inference.
Outcome:	The proposed framework outperforms models trained on long-CoT distilled data with 3.1k initialization samples and achieves an accuracy improvement of 51.0% to 81.6%.

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Yuming Yang, Yang Nan, Junjie Ye, Shihan Dou, Xiao Wang, Shuo Li, Huijie Lv, Tao Gui, Qi Zhang, Xuanjing Huang

PromISe: Releasing the Capabilities of LLMs with Prompt Introspective Search (2024.lrec-main)

Challenge:	Existing studies have explored various diversity-aware data selection methods to construct high-quality datasets and enhance model performance.
Approach:	They propose to use data diversity to measure instruction tuning of large language models.
Outcome:	The proposed diversity metric outperforms existing methods on simulated and real-world data and shows that it captures diversity variations and achieves a 0.97 correlation with instruction tuning.

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Minzheng Wang, Nan Xu, Jiahao Zhao, Yin Luo, Wenji Mao

An MRC Framework for Semantic Role Labeling (2022.coling-1)

Challenge:	Existing evaluation benchmarks for large language models use uniform manual prompts, resulting in underestimation of performance.
Approach:	They propose a prompt introspective search framework that integrates self-introspect and self-refine to unlock the capabilities of LLMs.
Outcome:	The proposed framework significantly boosts the performance of 12 well-known LLMs compared to baseline methods.

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Nan Wang, Jiwei Li, Yuxian Meng, Xiaofei Sun, Han Qiu, Ziyao Wang, Guoyin Wang, Jun He

Semantic Parsing

Challenge:	Existing work on semantic role labels ignores the semantic connection between the two tasks . et al. (2010) defined two types of semantic roles: core roles and non-core roles.
Approach:	They propose to use machine reading comprehension to bridge the gap between these two tasks . they formalize predicate disambiguation as multiple-choice machine reading understanding .
Outcome:	The proposed framework achieves state-of-the-art or comparable results to previous work . it uses the descriptions of candidate senses of a given predicate as options to select the correct sense .

Mask Attention Networks: Rethinking and Strengthen Transformer (2021.naacl-main)

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Zhihao Fan, Yeyun Gong, Dayiheng Liu, Zhongyu Wei, Siyuan Wang, Jian Jiao, Nan Duan, Ruofei Zhang, Xuanjing Huang

UniXcoder: Unified Cross-Modal Pre-training for Code Representation (2022.acl-long)

Challenge:	Existing research explores to enhance the two sublayers separately to improve the capability of Transformer for text representation.
Approach:	They propose to combine SAN and Feed-Forward Networks to create a dynamic mask attention network with a learnable mask matrix which can model localness adaptively.
Outcome:	The proposed model outperforms the original Transformer on translation and text summarization tasks.

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Daya Guo, Shuai Lu, Nan Duan, Yanlin Wang, Ming Zhou, Jian Yin

On Diversified Preferences of Large Language Model Alignment (2024.findings-emnlp)

Challenge:	Pre-trained models for programming languages have demonstrated great success on code intelligence . however, such pre-tried models are sub-optimal for auto-regressive tasks .
Approach:	They propose a unified cross-modal pre-trained model for programming language that leverages cross-module contents like AST and code comment to enhance code representation.
Outcome:	The proposed model achieves state-of-the-art on most code-related tasks and compares with existing models on zero-shot code-to-code search.

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Dun Zeng, Yong Dai, Pengyu Cheng, Longyue Wang, Tianhao Hu, Wanshun Chen, Nan Du, Zenglin Xu

Reasoning Over Semantic-Level Graph for Fact Checking (2020.acl-main)

Challenge:	Large language models (LLMs) can be fine tuned with human feedback, but human preferences can be diversified due to annotators’ different tastes, which hinders the effectiveness of LLM alignment methods.
Approach:	They propose a calibration error metric to evaluate large language models (LLMs) and a multi-objective reward learning method to enhance the calibration performance of RMs on shared preferences.
Outcome:	The proposed model can be adopted as a key calibration error and MORE can achieve superior alignment performance.

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Wanjun Zhong, Jingjing Xu, Duyu Tang, Zenan Xu, Nan Duan, Ming Zhou, Jiahai Wang, Jian Yin

DEMO: Reframing Dialogue Interaction with Fine-grained Element Modeling (2025.findings-acl)

Challenge:	Existing methods for fact checking use string concatenation or fusing features of isolated evidence sentences.
Approach:	They propose a method suitable for reasoning about the semantic-level structure of evidence . they use graph convolutional network and graph attention network to exploit the structure .
Outcome:	The proposed method improves claim verification accuracy and FEVER score on a benchmark dataset.

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Minzheng Wang, Xinghua Zhang, Kun Chen, Nan Xu, Haiyang Yu, Fei Huang, Wenji Mao, Yongbin Li

MMAU: A Holistic Benchmark of Agent Capabilities Across Diverse Domains (2025.findings-naacl)

Challenge:	Large language models (LLMs) enabled dialogue systems are one of the central modes in human-machine interaction.
Approach:	They propose a benchmark task for dialogue element MOdeling and Element Awareness and a new benchmark for dialogue agent interaction that allows the agent to model dialogue elements via imitation learning.
Outcome:	The proposed agent performs well in both dialogue element modeling and out-of-domain tasks.

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Guoli Yin, Haoping Bai, Shuang Ma, Feng Nan, Yanchao Sun, Zhaoyang Xu, Shen Ma, Jiarui Lu, Xiang Kong, Aonan Zhang, Dian Ang Yap, Yizhe Zhang, Karsten Ahnert, Vik Kamath, Mathias Berglund, Dominic Walsh, Tobias Gindele, Juergen Wiest, Zhengfeng Lai, Xiaoming Simon Wang, Jiulong Shan, Meng Cao, Ruoming Pang, Zirui Wang

Challenge:	Existing benchmarks focus on specific application scenarios, emphasizing task completion but failing to dissect the underlying skills that drive these outcomes.
Approach:	They propose a Massive Multitask Agent Understanding benchmark that evaluates LLMs across five domains and offline tasks.
Outcome:	The Massive Multitask Agent Understanding (MMAU) benchmark evaluates models across five domains including Tool-use, Directed Acyclic Graph (DAG) QA, Data Science and Machine Learning coding, Contest-level programming and Mathematics.

mDPO: Conditional Preference Optimization for Multimodal Large Language Models (2024.emnlp-main)

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Fei Wang, Wenxuan Zhou, James Y. Huang, Nan Xu, Sheng Zhang, Hoifung Poon, Muhao Chen

Monotonic Paraphrasing Improves Generalization of Language Model Prompting (2024.findings-emnlp)

Challenge:	Recent studies have attempted to apply DPO to multimodal scenarios but have found it challenging to achieve consistent improvement.
Approach:	They propose a multimodal DPO objective that prevents the over-prioritization of language-only preferences by also optimizing image preference.
Outcome:	The proposed method significantly improves performance on two multimodal LLMs of different sizes and three widely used benchmarks.

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Qin Liu, Fei Wang, Nan Xu, Tianyi Yan, Tao Meng, Muhao Chen

K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters (2021.findings-acl)

Challenge:	Large language models (LLMs) have demonstrated remarkable proficiency in zero-shot decision making and instruction following.
Approach:	They propose an end-to-end decoding strategy that paraphrases given prompts or instructions into their lower perplexity counterparts based on an ensemble of a paraphrase LM for prompt rewriting, and a target LM that constrains the generation for lower perxity.
Outcome:	The proposed method can efficiently paraphrase the original prompt without altering its semantic meaning while decreasing the perplexity of each generation as calculated by the target LM.

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Ruize Wang, Duyu Tang, Nan Duan, Zhongyu Wei, Xuanjing Huang, Jianshu Ji, Guihong Cao, Daxin Jiang, Ming Zhou

DUET: Joint Exploration of User–Item Profiles in Recommendation System (2026.findings-acl)

Challenge:	Existing methods for injecting knowledge into pre-trained models are inconsistent and can flush out knowledge when multiple kinds of knowledge are injected.
Approach:	They propose a framework that retains the original parameters of pre-trained models fixed and supports the development of versatile knowledge-infused models.
Outcome:	The proposed framework retains the original parameters of the pre-trained model fixed and supports the development of versatile knowledge-infused models.

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Yue Chen, Yifei Sun, Lu Wang, Fangkai Yang, Pu Zhao, Minjie Hong, Yifei Dong, Minghua He, Nan Hu, Jianjin Zhang, Zhiwei Dai, Yuefeng Zhan, Weihao Han, Hao Sun, Qingwei Lin, Weiwei Deng, Feng Sun, Qi Zhang, Saravan Rajmohan, Dongmei Zhang

Challenge:	Existing LLMs are opaque and difficult to interpret, resulting in limited interpretability.
Approach:	They propose an interaction-aware profile generator that jointly produces user and item profiles conditioned on both user history and item evidence.
Outcome:	The proposed model outperforms baselines on three real-world datasets.

A Contrastive Framework for Learning Sentence Representations from Pairwise and Triple-wise Perspective in Angular Space (2022.acl-long)

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Yuhao Zhang, Hongji Zhu, Yongliang Wang, Nan Xu, Xiaobo Li, Binqiang Zhao

Is Graph Structure Necessary for Multi-hop Question Answering? (2020.emnlp-main)

Challenge:	Existing methods for learning sentence representations focus on constitution of positive and negative representation pairs and do not focus on training objective.
Approach:	They propose a new method to learn sentence representations using BERT-like pre-trained models . they use a pairwise discriminating power and a model to model the entailment relation of triplet sentences .
Outcome:	The proposed method outperforms the previous state-of-the-art on diverse sentence related tasks.

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Nan Shao, Yiming Cui, Ting Liu, Shijin Wang, Guoping Hu

A2ATS: Retrieval-Based KV Cache Reduction via Windowed Rotary Position Embedding and Query-Aware Vector Quantization (2025.findings-acl)

Challenge:	Existing studies focus on multi-hop question answering across multiple documents or paragraphs.
Approach:	They propose a graph neural network to deal with graph structure in textual multi-hop reasoning . they propose 'self-attention' and propose removing entire graph structure may not hurt the final results .
Outcome:	The proposed model shows that graph-attention or the entire graph structure can be replaced by self-attention . hotpotQA is a widely used benchmark for multi-hop question answering .

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Junhui He, Junna Xing, Nan Wang, Rui Xu, Shangyu Wu, Peng Zhou, Qiang Liu, Chun Jason Xue, Qingan Li

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

Challenge:	Long context large language models (LLMs) pose significant challenges for efficient serving due to the large memory footprint and high access overhead of KV cache.
Approach:	They propose a retrieval-based method to reduce the memory footprint of LLMs . they propose Windowed Rotary Position Embedding and query-aware vector quantization .
Outcome:	The proposed method can achieve lower performance degradation with lower overhead compared to existing methods . it can reduce the memory footprint and access overhead of long context large language models .

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

More Than Spoken Words: Nonverbal Message Extraction and Generation (2023.emnlp-main)

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

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Dian Yu, Xiaoyang Wang, Wanshun Chen, Nan Du, Longyue Wang, Haitao Mi, Dong Yu

ToolSandbox: A Stateful, Conversational, Interactive Evaluation Benchmark for LLM Tool Use Capabilities (2025.findings-naacl)

Challenge:	Existing studies focus on extracting NMs from small-scale well-structured corpora such as movie scripts wherein NM is enclosed in parentheses by scriptwriters, which greatly decreases the difficulty of extraction.
Approach:	They propose to extract nonverbal messages (NMs) from written text and NMs from spoken text by using a semi-supervised learning algorithm.
Outcome:	The extracted NMs can generate more relevant, valid, and factually consistent NM than the purely supervised generator.

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Jiarui Lu, Thomas Holleis, Yizhe Zhang, Bernhard Aumayer, Feng Nan, Haoping Bai, Shuang Ma, Shen Ma, Mengyu Li, Guoli Yin, Zirui Wang, Ruoming Pang

Streaming Hallucination Detection in Long Chain-of-Thought Reasoning (2026.findings-acl)

Challenge:	Recent advances in large language models have led to a growing interest in tool assisted LLMs . toolSandbox includes stateful tool execution, implicit state dependencies between tools .
Approach:	a new tool-based evaluation tool is released to help LLMs evaluate their tool-use capabilities. a tool-driven evaluation tool includes stateful tool execution, implicit state dependencies between tools and a built-in user simulator.
Outcome:	the toolSandbox evaluation benchmark shows that open source and proprietary models have a performance gap . the benchmarks show that even the most capable LLMs are challenged by state dependent tasks .

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Haolang Lu, Minghui Pan, Ripeng LI, Guoshun Nan, Jialin Zhuang, Zijie Zhao, Zhongxiang Sun, Kun Wang, Yang Liu

AIR-Bench: Automated Heterogeneous Information Retrieval Benchmark (2025.acl-long)

Challenge:	Long chain-of-thought reasoning improves performance of large language models, yet hallucinations in such settings often emerge subtly and propagate across reasoning steps.
Approach:	They propose to treat step-level hallucination judgments as local observations and introduce a cumulative prefix-level signal that tracks the global evolution of the reasoning state over the entire trajectory.
Outcome:	The proposed method enables streaming hallucination detection in long CoT reasoning, providing real-time, interpretable evidence.

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Jianlyu Chen, Nan Wang, Chaofan Li, Bo Wang, Shitao Xiao, Han Xiao, Hao Liao, Defu Lian, Zheng Liu

Information Retrieval

Challenge:	Evaluation benchmarks based on predefined domains and human-labeled data face limitations in addressing evaluation needs for emerging domains.
Approach:	They propose an automated information retrieval benchmark based on predefined domains and human-labeled data . AIR-Bench is automated and Heterogeneous with three key features .
Outcome:	The proposed benchmarks are based on predefined domains and human-labeled data.

COFFEE: Counterfactual Fairness for Personalized Text Generation in Explainable Recommendation (2023.emnlp-main)

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Nan Wang, Qifan Wang, Yi-Chia Wang, Maziar Sanjabi, Jingzhou Liu, Hamed Firooz, Hongning Wang, Shaoliang Nie

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

Challenge:	Personalized text generation (PTG) is a key component of our digital lives but can inadvertently associate different levels of linguistic quality with users’ protected attributes.
Approach:	They propose a framework to achieve measure-specific counterfactual fairness in explanation generation by focusing on one of the most studied settings: generating natural language explanations for recommendations.
Outcome:	The proposed framework achieves measure-specific counterfactual fairness in explanation generation.

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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

MMLU-ProX: A Multilingual Benchmark for Advanced Large Language Model Evaluation (2025.emnlp-main)

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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Weihao Xuan, Rui Yang, Heli Qi, Qingcheng Zeng, Yunze Xiao, Aosong Feng, Dairui Liu, Yun Xing, Junjue Wang, Fan Gao, Jinghui Lu, Yuang Jiang, Huitao Li, Xin Li, Kunyu Yu, Ruihai Dong, Shangding Gu, Yuekang Li, Xiaofei Xie, Felix Juefei-Xu, Foutse Khomh, Osamu Yoshie, Qingyu Chen, Douglas Teodoro, Nan Liu, Randy Goebel, Lei Ma, Edison Marrese-Taylor, Shijian Lu, Yusuke Iwasawa, Yutaka Matsuo, Irene Li

Multilinguality

Challenge:	Existing large language model evaluation benchmarks focus on English, while current multilingual tasks lack parallel questions that specifically assess cross-lingual reasoning abilities.
Approach:	They propose a comprehensive benchmark covering 29 languages, built on an English benchmark.
Outcome:	The MMLU-ProX is a comprehensive benchmark covering 29 languages, built on an English benchmark.

An Enhanced Knowledge Injection Model for Commonsense Generation (2020.coling-main)

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Zhihao Fan, Yeyun Gong, Zhongyu Wei, Siyuan Wang, Yameng Huang, Jian Jiao, Xuanjing Huang, Nan Duan, Ruofei Zhang

Little Giants: Synthesizing High-Quality Embedding Data at Scale (2025.naacl-long)

Challenge:	a recent study shows that digging the relationship of concepts from scratch is non-trivial for commonsense generation tasks.
Approach:	They use a retrieve-and-edit framework to retrieve a prototype with these concepts . they use qt and qq to generate commonsense questions at scale .
Outcome:	The proposed method significantly improves the performance on commonsense generation tasks.

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Haonan Chen, Liang Wang, Nan Yang, Yutao Zhu, Ziliang Zhao, Furu Wei, Zhicheng Dou

MedEureka: A Medical Domain Benchmark for Multi-Granularity and Multi-Data-Type Embedding-Based Retrieval (2025.findings-naacl)

Challenge:	Synthetic data generation is an increasingly popular way of training models without the need for large, manually labeled datasets.
Approach:	They propose a framework that aligns open-source small models to efficiently generate large-scale embedding data.
Outcome:	The proposed framework outperforms state-of-the-art embedding models by using only 1/10 of the GPT API calls.

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Yongqi Fan, Nan Wang, Kui Xue, Jingping Liu, Tong Ruan

MIND: Towards Immersive Psychological Healing with Multi-Agent Inner Dialogue (2025.findings-emnlp)

Challenge:	Embedding-based retrieval (EBR) is a mainstream approach in information retrieval.
Approach:	They propose an enriched benchmark to evaluate retrieval capabilities of embedding models . they use four levels of granularity and six types of medical texts to prompt instruction-fine-tuned embeddable models.
Outcome:	The proposed benchmark evaluates the retrieval capabilities of embedding models with multi-granularity and multi-data types.

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Yujia Chen, Changsong Li, Yiming Wang, Tianjie Ju, Qingqing Xiao, Nan Zhang, Zifan Kong, Peng Wang, Binyu Yan

ChartInsights: Evaluating Multimodal Large Language Models for Low-Level Chart Question Answering (2024.findings-emnlp)

Challenge:	Mental health issues are worsening in today’s competitive society, such as depression and anxiety.
Approach:	They propose a multi-agent inner dialogue paradigm that provides more immersive psychological healing environments.
Outcome:	The proposed paradigm provides more immersive psychological healing environments.

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Yifan Wu, Lutao Yan, Leixian Shen, Yunhai Wang, Nan Tang, Yuyu Luo

Improving Text Embeddings with Large Language Models (2024.acl-long)

Challenge:	Chart question answering (ChartQA) tasks are a critical part of visualization charts.
Approach:	They propose a chart question answering task that uses MLLMs to analyze charts . they propose 'Chain-of-Charts' textual prompt strategy that directs attention to visual elements .
Outcome:	The proposed model improves performance by 14.41% and 80% in low-level ChartQA tasks.

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Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, Furu Wei

Does Your Model Classify Entities Reasonably? Diagnosing and Mitigating Spurious Correlations in Entity Typing (2022.emnlp-main)

Challenge:	Existing methods for obtaining text embeddings require complex training pipelines . authors leverage proprietary LLMs to generate diverse synthetic data for text embeds based on 93 languages .
Approach:	They propose a method for obtaining high-quality text embeddings using only synthetic data and less than 1k training steps.
Outcome:	The proposed method achieves strong performance on competitive text embedding benchmarks without using any labeled data.

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Nan Xu, Fei Wang, Bangzheng Li, Mingtao Dong, Muhao Chen

LSDSCC: a Large Scale Domain-Specific Conversational Corpus for Response Generation with Diversity Oriented Evaluation Metrics (N18-1)

Challenge:	Existing entity typing models are subject to spurious correlations due to shortcuts and biased training.
Approach:	They propose a method to augment existing model biases by combining spurious correlations with debiasedcounterparts to improve generalization.
Outcome:	The proposed method improves generalization of different entity typing models on the original and debiased test sets.

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Zhen Xu, Nan Jiang, Bingquan Liu, Wenge Rong, Bowen Wu, Baoxun Wang, Zhuoran Wang, Xiaolong Wang

Analytical Reasoning of Text (2022.findings-naacl)

Challenge:	Existing evaluation metrics for NRG models can't measure semantic relevance and diversity of generated results.
Approach:	They propose a large-scale domain-specific conversational corpus with preprocessing and cleansing procedures for model training and a testing set for measuring the diversity of generated results.
Outcome:	The proposed corpus can be taken as a new benchmark dataset for the NRG task.

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Wanjun Zhong, Siyuan Wang, Duyu Tang, Zenan Xu, Daya Guo, Yining Chen, Jiahai Wang, Jian Yin, Ming Zhou, Nan Duan

When Compression Meets Model Compression: Memory-Efficient Double Compression for Large Language Models (2024.findings-emnlp)

Challenge:	Existing models with implicit reasoning ability struggle to solve analytical reasoning of text.
Approach:	They propose an approach to analyze text and use it to perform reasoning over it.
Outcome:	The proposed approach outperforms pre-trained models on an analysis of the Law School Admission Test dataset.

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Weilan Wang, Yu Mao, Tang Dongdong, Du Hongchao, Nan Guan, Chun Jason Xue

Neural Deepfake Detection with Factual Structure of Text (2020.emnlp-main)

Challenge:	Large language models (LLMs) exhibit excellent performance in various tasks, but memory requirements present a challenge when deploying on memory-limited devices.
Approach:	They propose a framework to compress LLM after quantization further, achieving about 2.2x compression ratio.
Outcome:	The proposed model can achieve 40% reduction in memory size with negligible loss in accuracy and inference speed.

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Wanjun Zhong, Duyu Tang, Zenan Xu, Ruize Wang, Nan Duan, Ming Zhou, Jiahai Wang, Jian Yin

Dense Retrieval as Indirect Supervision for Large-space Decision Making (2023.findings-emnlp)

Challenge:	Existing approaches to deepfake detection typically represent documents with coarse-grained representations, but they struggle to capture factual structures of documents.
Approach:	They propose a graph-based model that captures factual structures of documents for deepfake detection.
Outcome:	The proposed model improves strong base models built with RoBERTa on two public deepfake datasets.

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Nan Xu, Fei Wang, Mingtao Dong, Muhao Chen

Information Retrieval

Challenge:	Dense Decision Retrieval (DDR) is a learning-to-retrieve task for discriminative natural language understanding (NLU) tasks with large label spaces.
Approach:	They propose a novel approach to learning large-space discriminative NLU tasks as a learning-to-retrieve task by adopting a dual-encoder architecture that learns to predict by retrieving from a decision thesaurus.
Outcome:	The proposed approach outperforms baselines greatly on multi-label classification tasks, 1.17% in F1 score ultra-fine entity typing, and 1.26% in accuracy on three few-shot intent classification tasks on average.

LogicalFactChecker: Leveraging Logical Operations for Fact Checking with Graph Module Network (2020.acl-main)

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Wanjun Zhong, Duyu Tang, Zhangyin Feng, Nan Duan, Ming Zhou, Ming Gong, Linjun Shou, Daxin Jiang, Jiahai Wang, Jian Yin

Improving Factual Consistency of Abstractive Summarization via Question Answering (2021.acl-long)

Challenge:	Existing methods for fact checking textual statements are not yet available.
Approach:	They propose a neural network approach capable of leveraging logical operations for fact checking . they use a textual statement and semi-structured tables to generate a program from it .
Outcome:	The proposed approach achieves state-of-the-art performance on TABFACT dataset . it derives a program (a.k.a. logical form) of the statement in semantic parsing manner .

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Feng Nan, Cicero Nogueira dos Santos, Henghui Zhu, Patrick Ng, Kathleen McKeown, Ramesh Nallapati, Dejiao Zhang, Zhiguo Wang, Andrew O. Arnold, Bing Xiang

Summarization

Challenge:	Recent studies show that about 30% of summaries generated by neural text summarization suffer from fact fabrication.
Approach:	They propose an automatic evaluation metric to measure factual consistency and a learning algorithm that maximizes the metric during model training.
Outcome:	The proposed method improves factual consistency and overall quality of summarization models.

SimLM: Pre-training with Representation Bottleneck for Dense Passage Retrieval (2023.acl-long)

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Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei

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

Challenge:	SimLM uses a simple bottleneck architecture that learns to compress the passage information into a dense vector through self-supervised pre-training.
Approach:	They propose a simple yet effective pre-training method for dense passage retrieval that learns to compress the passage information into a dense vector through self-supervised pre-tuning.
Outcome:	The proposed method outperforms multi-vector approaches on large-scale passage retrieval datasets and shows significant improvements over baselines.

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

CIA: Inferring the Communication Topology from LLM-based Multi-Agent Systems (2026.acl-long)

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.

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Yongxuan Wu, Xixun Lin, He Zhang, Nan Sun, Kun Wang, Chuan Zhou, Shirui Pan, Yanan Cao

SWING: Balancing Coverage and Faithfulness for Dialogue Summarization (2023.findings-eacl)

Challenge:	LLM-based multi-agent systems (MAS) have demonstrated remarkable capabilities in solving complex tasks.
Approach:	They propose a communication inference attack that constructs new adversarial queries to induce intermediate agents’ reasoning outputs and models their semantic correlations through the global bias disentanglement and LLM-guided weak supervision.
Outcome:	The proposed attack achieves an average AUC of 0.87 and a peak AUC up to 0.99, revealing the privacy risk in MAS.

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Kung-Hsiang Huang, Siffi Singh, Xiaofei Ma, Wei Xiao, Feng Nan, Nicholas Dingwall, William Yang Wang, Kathleen McKeown

AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models (2024.findings-naacl)

Challenge:	Existing approaches to dialogue summarization rely on features of conversation data.
Approach:	They propose to use natural language inference models to improve coverage and faithfulness . they use fine-grained training signals to encourage model to generate missing content .
Outcome:	The proposed model achieves higher faithfulness and coverage while maintaining conciseness compared to prior methods.

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Wanjun Zhong, Ruixiang Cui, Yiduo Guo, Yaobo Liang, Shuai Lu, Yanlin Wang, Amin Saied, Weizhu Chen, Nan Duan

ContraCLM: Contrastive Learning For Causal Language Model (2023.acl-long)

Challenge:	Traditional benchmarks for evaluating foundation models often fail to accurately represent their general abilities for human-centric tasks.
Approach:	They propose a bilingual benchmark to assess foundation models in the context of human-centric standardized exams such as college entrance exams, law school admission tests, and math competitions.
Outcome:	The proposed benchmark exceeds the average human performance on SAT, LSAT, and math competitions with 95% accuracy and 92.5% on the Chinese college entrance English exam.

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Nihal Jain, Dejiao Zhang, Wasi Uddin Ahmad, Zijian Wang, Feng Nan, Xiaopeng Li, Ming Tan, Ramesh Nallapati, Baishakhi Ray, Parminder Bhatia, Xiaofei Ma, Bing Xiang

Noise Robust Named Entity Understanding for Voice Assistants (2021.naacl-industry)

Challenge:	Existing studies show that causal language models lack expressiveness due to poor discrimination ability.
Approach:	They propose a contrastive learning framework that enhances discrimination of representations and bridges the gap with encoder-only models.
Outcome:	The proposed framework improves discrimination and source code generation capabilities on a variety of downstream tasks.

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Deepak Muralidharan, Joel Ruben Antony Moniz, Sida Gao, Xiao Yang, Justine Kao, Stephen Pulman, Atish Kothari, Ray Shen, Yinying Pan, Vivek Kaul, Mubarak Seyed Ibrahim, Gang Xiang, Nan Dun, Yidan Zhou, Andy O, Yuan Zhang, Pooja Chitkara, Xuan Wang, Alkesh Patel, Kushal Tayal, Roger Zheng, Peter Grasch, Jason D Williams, Lin Li

Robustness in NLP

Challenge:	Named Entity Recognition and Entity Linking are challenging for voice assistants . utterances are relatively short, so there is not much context to help disambiguate .
Approach:	They propose a Named Entity Understanding system that combines NER and EL in a joint reranking module.
Outcome:	The proposed framework improves NER accuracy by up to 3.13% and EL accuracy by 3.6% in F1 score . it also leads to better accuracies in other natural language understanding tasks .

Learning to Infer Entities, Properties and their Relations from Clinical Conversations (D19-1)

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Nan Du, Mingqiu Wang, Linh Tran, Gang Lee, Izhak Shafran

Multiview Identifiers Enhanced Generative Retrieval (2023.acl-long)

Challenge:	Existing relation extraction models restrict inferring relations between tokens within a few neighboring sentences to avoid high computational complexity.
Approach:	They propose a Span Attribute Tagging (SAT) model to infer clinical entities and their properties using a hierarchical two-stage approach.
Outcome:	The proposed model outperforms baseline models in identifying relations between symptoms and properties by about 32% and 50% on medications and their properties.

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Yongqi Li, Nan Yang, Liang Wang, Furu Wei, Wenjie Li

Information Retrieval

Challenge:	Current approaches use a numeric ID or text piece as the identifier, but these identifieres cannot cover a passage’s content well.
Approach:	They propose a new type of identifier that is generated based on the content of a passage and could integrate contextualized information that text pieces lack.
Outcome:	The proposed approach performs the best in generative retrieval on three public datasets.

GPS: Genetic Prompt Search for Efficient Few-Shot Learning (2022.emnlp-main)

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Hanwei Xu, Yujun Chen, Yulun Du, Nan Shao, Wang Yanggang, Haiyu Li, Zhilin Yang

Rethinking Stateful Tool Use in Multi-Turn Dialogues: Benchmarks and Challenges (2025.findings-acl)

Challenge:	Pretrained language models are often finetuned for downstream tasks, which has been shown to improve performance over non-pretrained models.
Approach:	They propose a genetic algorithm to automatically search for the best prompt for few-shot learning with pretrained language models by gradient-free algorithm.
Outcome:	Experiments on diverse datasets show that the proposed method outperforms manual prompts by 2.6 points.

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Hongru Wang, Wenyu Huang, Yufei Wang, Yuanhao Xi, Jianqiao Lu, Huan Zhang, Nan Hu, Zeming Liu, Jeff Z. Pan, Kam-Fai Wong

FormNetV2: Multimodal Graph Contrastive Learning for Form Document Information Extraction (2023.acl-long)

Challenge:	Existing benchmarks that assess Language Models (LMs) as Language Agents (LAs) for tool use focus on stateless, single-turn interactions or partial evaluations, overlooking the inherent stateful nature of interactions in multi-turn applications.
Approach:	They propose a multi-turn dialogue dataset with stateful tool interactions considering the whole life cycle of tool use across six key tasks in three stages . they also build VirtualMobile – an embodied virtual mobile evaluation environment to simulate API calls and assess the robustness of the created APIs.
Outcome:	The proposed dataset evaluates 13 open- and closed-source LLMs and provides detailed analysis at each stage.

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Chen-Yu Lee, Chun-Liang Li, Hao Zhang, Timothy Dozat, Vincent Perot, Guolong Su, Xiang Zhang, Kihyuk Sohn, Nikolay Glushnev, Renshen Wang, Joshua Ainslie, Shangbang Long, Siyang Qin, Yasuhisa Fujii, Nan Hua, Tomas Pfister

mmE5: Improving Multimodal Multilingual Embeddings via High-quality Synthetic Data (2025.findings-acl)

Challenge:	Existing approaches that extend the mask language modeling to other modalities require careful multi-task tuning, complex reconstruction target designs, or additional pre-training data.
Approach:	They propose a centralized multimodal graph contrastive learning strategy to unify self-supervised pre-training for all modalities in one loss.
Outcome:	The proposed model achieves state-of-the-art performance on FUNSD, CORD, SROIE and Payment benchmarks with a more compact model size.

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Haonan Chen, Liang Wang, Nan Yang, Yutao Zhu, Ziliang Zhao, Furu Wei, Zhicheng Dou

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

Challenge:	Multimodal embedding models encode multimedia inputs into latent vector representations.
Approach:	They propose to synthesize multimodal multilingual data using a multimodal large language model . they identify three criteria for high-quality synthetic multimodal data .
Outcome:	The proposed model outperforms existing models on the MMEB Benchmark and the XTD benchmark.

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

The Medical Scribe: Corpus Development and Model Performance Analyses (2020.lrec-1)

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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Izhak Shafran, Nan Du, Linh Tran, Amanda Perry, Lauren Keyes, Mark Knichel, Ashley Domin, Lei Huang, Yu-hui Chen, Gang Li, Mingqiu Wang, Laurent El Shafey, Hagen Soltau, Justin Stuart Paul

Challenge:	Existing tools to assist in clinical note generation using audio of provider-patient encounters are lacking.
Approach:	They develop an annotation scheme to extract relevant clinical concepts from audio of provider-patient encounters and train a state-of-the-art tagging model.
Outcome:	The proposed model is more useful than the F-scores reflect and can be used in clinical notes.

NUWA-XL: Diffusion over Diffusion for eXtremely Long Video Generation (2023.acl-long)

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Shengming Yin, Chenfei Wu, Huan Yang, Jianfeng Wang, Xiaodong Wang, Minheng Ni, Zhengyuan Yang, Linjie Li, Shuguang Liu, Fan Yang, Jianlong Fu, Ming Gong, Lijuan Wang, Zicheng Liu, Houqiang Li, Nan Duan

Can LLMs Evaluate Complex Attribution in QA? Automatic Benchmarking using Knowledge Graphs (2025.acl-long)

Challenge:	Existing work generates long videos segment by segment sequentially, which is inefficient.
Approach:	They propose a Diffusion over Difference architecture for eXtremely Long video generation.
Outcome:	The proposed architecture reduces the average inference time from 7.55min to 26s (94.26%) and generates high-quality long videos with both global and local coherence.

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Nan Hu, Jiaoyan Chen, Yike Wu, Guilin Qi, Hongru Wang, Sheng Bi, Yongrui Chen, Tongtong Wu, Jeff Z. Pan

Constructing a Chinese Medical Conversation Corpus Annotated with Conversational Structures and Actions (L18-1)

Challenge:	Attributed Question Answering (AQA) has attracted wide attention, but there are several limitations in evaluating the attributions.
Approach:	They propose a large-scale benchmark containing comprehensive attribution categories . they compare 25 automatic evaluators with human evaluers and tested LLM evalators .
Outcome:	The proposed method can compare attributions with subtle differences and provide feedback to improve them.

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Nan Wang, Yan Song, Fei Xia

Analyzing the Effects of Supervised Fine-Tuning on Model Knowledge from Token and Parameter Levels (2025.emnlp-main)

Challenge:	Recent studies have found that patients' advocacy for antibiotic treatment is consequential on antibiotic over-prescribing.
Approach:	They propose to analyze a manually transcribed corpus of medical dialogue in Chinese pediatric consultations with annotation of conversational structures and actions.
Outcome:	The proposed corpus can shed light on ways to improve physician-patient communication in order to reduce antibiotic over-prescribing.

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Junjie Ye, Yuming Yang, Yang Nan, Shuo Li, Qi Zhang, Tao Gui, Xuanjing Huang, Peng Wang, Zhongchao Shi, Jianping Fan

LongEmbed: Extending Embedding Models for Long Context Retrieval (2024.emnlp-main)

Challenge:	Large language models (LLMs) acquire substantial world knowledge during pretraining, which is further shaped by post-training techniques such as supervised fine-tuning (SFT).
Approach:	They evaluate closed-book question answering (CBQA) performance across five LLMs from the LLaMA-2 and LLama-3 families and examine the impact of supervised fine-tuning on model knowledge.
Outcome:	The proposed model performance is 14% worse than models fine-tuned on 1,920 samples and 12% worse on 240 samples.

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Dawei Zhu, Liang Wang, Nan Yang, Yifan Song, Wenhao Wu, Furu Wei, Sujian Li

Representation Learning

Challenge:	Existing embedding models support only 512 input tokens, hindering their application in scenarios requiring long inputs.
Approach:	They evaluate the performance of existing embedding models by using a new benchmark and a training-free context window extension strategy.
Outcome:	The proposed model extends the input window of existing models by several folds.

Query2doc: Query Expansion with Large Language Models (2023.emnlp-main)

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Liang Wang, Nan Yang, Furu Wei

Leveraging Declarative Knowledge in Text and First-Order Logic for Fine-Grained Propaganda Detection (2020.emnlp-main)

Challenge:	Existing methods for sparse and dense retrieval have limited success on popular datasets.
Approach:	They propose a query expansion approach that generates pseudo-documents by few-shot prompting large language models and then expands the query with generated pseudo-docs.
Outcome:	The proposed method boosts the performance of BM25 on ad-hoc IR datasets by 3% to 15% without any model fine-tuning.

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Ruize Wang, Duyu Tang, Nan Duan, Wanjun Zhong, Zhongyu Wei, Xuanjing Huang, Daxin Jiang, Ming Zhou

Challenge:	Existing methods for fine-grained propaganda detection are not based on input-output data, but instead use declarative knowledge to detect propagandistic text fragments.
Approach:	They propose a method to inject declarative knowledge of fine-grained propaganda techniques into training data to get better representations of propagandistic texts.
Outcome:	The proposed method achieves superior performance on a large dataset for propaganda detection.

Learning to Retrieve In-Context Examples for Large Language Models (2024.eacl-long)

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Liang Wang, Nan Yang, Furu Wei

MoCa: Modality-aware Continual Pre-training Makes Better Bidirectional Multimodal Embeddings (2026.acl-long)

Challenge:	Existing approaches to improve in-context learning performance are highly sensitive to the quality of the incontext examples provided.
Approach:	They propose a framework to iteratively train dense retrievers that can identify high-quality in-context examples for large language models.
Outcome:	The proposed model improves performance by retrieving examples with similar patterns, and the gains are consistent across LLMs of varying sizes.

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Haonan Chen, Hong Liu, Yuping Luo, Liang Wang, Nan Yang, Furu Wei, Zhicheng Dou

TRACE: An Experiential Framework for Coherent Multi-hop Knowledge Graph Question Answering (2026.findings-acl)

Challenge:	Existing approaches to embed multimodal models face limitations such as suboptimal causal attention in VLMs and limited diversity in training objectives and data.
Approach:	They propose a framework for transforming pre-trained VLMs into bidirectional multimodal embedding models.
Outcome:	The proposed model improves performance across MMEB and ViDoRe-v2 benchmarks and exhibits strong scalability with both model size and training data on MMEF.

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Yingxu Wang, Jiaxin Huang, Mengzhu Wang, Nan Yin

Improving Fake News Detection of Influential Domain via Domain- and Instance-Level Transfer (2022.coling-1)

Challenge:	Existing methods for multihop Knowledge Graph Question Answering (KGQA) treat each reasoning step independently and fail to leverage experience from prior explorations, leading to fragmented reasoning and redundant exploration.
Approach:	They propose a framework that unifies LLM-driven contextual reasoning with exploration prior integration to enhance coherence and robustness of multihop KGQA.
Outcome:	Extensive experiments on multiple KGQA benchmarks show that TRACE outperforms state-of-the-art methods.

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Qiong Nan, Danding Wang, Yongchun Zhu, Qiang Sheng, Yuhui Shi, Juan Cao, Jintao Li

TED-TTS: Training-Free Intra-Utterance Emotion and Duration Control for Text-to-Speech Synthesis (2026.acl-long)

Challenge:	Social media spreads both real news and fake news in various domains including politics, health, entertainment, etc.
Approach:	They propose a Domain- and Instance-level Transfer Framework for Fake News Detection which could improve the performance of specific target domains.
Outcome:	The proposed framework improves performance of target domains by hurting other domains, resulting in unsatisfactory performance in the target domain.

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Qifan Liang, Yuansen Liu, Ruixin Wei, Nan Lu, Junchuan Zhao, Ye Wang

LMDX: Language Model-based Document Information Extraction and Localization (2024.findings-acl)

Challenge:	Existing controllable Text-to-Speech methods limited to inter-utterance-level control . utterance expressiveness remains a challenge in building human-like TTS synthesis systems .
Approach:	They propose a training-free controllable framework for pretrained zero-shot TTS to enable intra-utterance emotion and duration expression.
Outcome:	The proposed framework achieves state-of-the-art intra-utterance consistency while maintaining baseline-level speech quality.

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Vincent Perot, Kai Kang, Florian Luisier, Guolong Su, Xiaoyu Sun, Ramya Sree Boppana, Zilong Wang, Zifeng Wang, Jiaqi Mu, Hao Zhang, Chen-Yu Lee, Nan Hua

iTAG: Inverse Design for Natural Text Generation with Accurate Causal Graph Annotations (2026.acl-long)

Challenge:	Large Language Models have revolutionized Natural Language Processing but their application in extracting information from visually rich documents has not been successful.
Approach:	They propose a language model-based document information extraction and localization methodology to reframe the document information extract task for a LLM.
Outcome:	The proposed method enables extraction of singular, repeated, and hierarchical entities with and without training data.

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Wenshuo Wang, Boyu Cao, Nan Zhuang, Wei Li

Logic Jailbreak: Efficiently Unlocking LLM Safety Restrictions Through Formal Logical Expression (2026.findings-acl)

Challenge:	Lack of causally annotated text data for use as ground truth hinders causal discovery . early template-based generation methods sacrifice text naturalness in exchange for high annotation costs .
Approach:	They propose a method which performs real-world concept assignment to nodes before converting causal graphs into text.
Outcome:	The proposed method shows high annotation accuracy and naturalness across extensive tests.

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Jingyu Peng, Maolin Wang, Nan Wang, Jiatong Li, Yuchen Li, Yuyang Ye, Wanyu Wang, Pengyue Jia, Kai Zhang, Xiangyu Zhao

Breaking the Impasse: Dual-Scale Evolutionary Policy Training for Social Language Agents (2026.acl-long)

Challenge:	Despite advances in aligning LLMs with human values, current safety mechanisms remain vulnerable to jailbreak attacks.
Approach:	They propose a black-box jailbreak method that uses logical expression translation to bypass LLM safety mechanisms.
Outcome:	The proposed method exploits the distributional gap between alignment data and logic-expressed inputs while preserving the underlying semantic intent and readability while evading safety constraints.

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Minzheng Wang, Run Luo, Yanbo Wang, Zichen Liu, Yuqiao Tan, Tao Tan, Nan Xu, Lu Wang, Wenji Mao

DialogVED: A Pre-trained Latent Variable Encoder-Decoder Model for Dialog Response Generation (2022.acl-long)

Challenge:	Reinforcement Learning with Verifiable Rewards (RLVR) is effective for closed-ended tasks, but it is not applicable to open-ended social language games.
Approach:	They propose a method that uses a time-scaled evolutionary perception mechanism to detect impasse by quantifying dual-scale value baseline divergence alongside match entropy.
Outcome:	Experiments on multiple social language games show that the proposed method outperforms baselines and avoids policy degeneration.

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Wei Chen, Yeyun Gong, Song Wang, Bolun Yao, Weizhen Qi, Zhongyu Wei, Xiaowu Hu, Bartuer Zhou, Yi Mao, Weizhu Chen, Biao Cheng, Nan Duan

ZeroPrompt: Scaling Prompt-Based Pretraining to 1,000 Tasks Improves Zero-Shot Generalization (2022.findings-emnlp)

Challenge:	Existing pre-trained dialog models shed light on various downstream tasks in natural language processing (NLP).
Approach:	They propose a dialog pre-training framework that introduces latent variables into the enhanced encoder-decoder pre-train framework to increase relevance and diversity of responses.
Outcome:	The proposed model achieves state-of-the-art on personaChat, DailyDialog, and DSTC7-AVSD datasets.

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Hanwei Xu, Yujun Chen, Yulun Du, Nan Shao, Wang Yanggang, Haiyu Li, Zhilin Yang

InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training (2021.naacl-main)

Challenge:	a recent study shows that task scaling can be an efficient alternative to model scaling.
Approach:	They propose a multitask pretraining approach ZeroPrompt for zero-shot generalization . they focus on task scaling and zero-shooting to improve model performance .
Outcome:	The proposed approach improves zero-shot generalization efficiency by 30 times with task scaling.

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Zewen Chi, Li Dong, Furu Wei, Nan Yang, Saksham Singhal, Wenhui Wang, Xia Song, Xian-Ling Mao, Heyan Huang, Ming Zhou