Papers by Hua Li

Disentangling Logic: The Role of Context in Large Language Model Reasoning Capabilities (2025.findings-acl)

Challenge:	Complex reasoning ability is one of the most important features of Large Language Models.
Approach:	They propose a new benchmark that measures the reasoning ability of Large Language Models . it contains 900 algorithmic questions belonging to the NP-Hard complexity class .
Outcome:	The proposed benchmark contains 900 questions belonging to the NP-Hard complexity class and is updated on a monthly basis.

Copied to clipboard

Wenyue Hua, Kaijie Zhu, Lingyao Li, Lizhou Fan, Mingyu Jin, Shuhang Lin, Haochen Xue, Zelong Li, Jindong Wang, Yongfeng Zhang

DuReader_robust: A Chinese Dataset Towards Evaluating Robustness and Generalization of Machine Reading Comprehension in Real-World Applications (2021.acl-short)

Challenge:	Using large language models, large language model models can be used to evaluate reasoning abilities in context-rich scenarios.
Approach:	They construct datasets for both propositional logic and abductive logic reasoning with four difficulty levels across 12 distinct domains based on Wikipedia categorization and those with purely abstract variables.
Outcome:	The proposed model can be used to benchmark LLMs in real-world scenarios, but not in context-rich scenarios.

Copied to clipboard

Hongxuan Tang, Hongyu Li, Jing Liu, Yu Hong, Hua Wu, Haifeng Wang

Is Your Language Model Ready for Monetization Decisions? (2026.findings-acl)

Challenge:	In order to comprehensively verify the robustness and generalization of MRC models, we construct a real-world Chinese dataset - DuReader_robust .
Approach:	They introduce a real-world Chinese dataset to evaluate the robustness and generalization of MRC models from three aspects: over-sensitivity, over-stability and generalisation.
Outcome:	The proposed model fails to perform well on the challenge test set and may provide suggestions for future model development.

Copied to clipboard

Jialu Gao, Hua Li, Tracy Ortman, Yeyun Gong, Jian Jiao

Gentopia.AI: A Collaborative Platform for Tool-Augmented LLMs (2023.emnlp-demo)

Challenge:	Existing benchmarks focus on shopping-centric scenarios and user-facing data, overlooking intermediate decision stages and robustness considerations.
Approach:	They propose a multi-task benchmark to evaluate large language models in real-world monetization contexts.
Outcome:	The proposed benchmark covers intent understanding, commercial matching, and user behavior modeling.

Copied to clipboard

Binfeng Xu, Xukun Liu, Hua Shen, Zeyu Han, Yuhan Li, Murong Yue, Zhiyuan Peng, Yuchen Liu, Ziyu Yao, Dongkuan Xu

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

Challenge:	Existing frameworks for Augmented Language Models lack flexibility, democratization, and holistic evaluation.
Approach:	They propose a lightweight and extensible framework for Augmented Language Models called Gentopia.
Outcome:	The proposed framework integrates language models, task formats, prompting modules, and plugins into a unified paradigm.

Copied to clipboard

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

Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network (P18-1)

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

Copied to clipboard

Xiangyang Zhou, Lu Li, Daxiang Dong, Yi Liu, Ying Chen, Wayne Xin Zhao, Dianhai Yu, Hua Wu

UNIMO: Towards Unified-Modal Understanding and Generation via Cross-Modal Contrastive Learning (2021.acl-long)

Challenge:	Existing models for matching dialogue responses rely on semantic and functional dependencies . a recent study only uses the last utterance in context for matching a reply .
Approach:	They propose a model that matches a response with its multi-turn context using attention.
Outcome:	The proposed model outperforms the state-of-the-art models on two large-scale multi-turn response selection tasks.

Copied to clipboard

Wei Li, Can Gao, Guocheng Niu, Xinyan Xiao, Hao Liu, Jiachen Liu, Hua Wu, Haifeng Wang

VaeDiff-DocRE: End-to-end Data Augmentation Framework for Document-level Relation Extraction (2025.coling-main)

Challenge:	Existing pre-training methods focus on single-modal tasks or multi-modal ones . large-scale pre- training has drawn much attention in both the community of Compute Vision (CV) and Natural Language Processing (NLP).
Approach:	They propose a UNIfied-MOdal pre-training architecture which can adapt to both single-modal and multi-modal understanding and generation tasks.
Outcome:	The proposed model can learn more generalizable representations with rich non-paired single-modal data.

Copied to clipboard

Khai Phan Tran, Wen Hua, Xue Li

D-Artemis: A Deliberative Cognitive Framework for Mobile GUI Multi-Agents (2026.findings-acl)

Challenge:	Existing methods for Document-level Relation Extraction assume a uniform label distribution, resulting in suboptimal performance on real-world, imbalanced datasets.
Approach:	They propose a method that leverages the Variational Autoencoder architecture to capture all relation-wise distributions formed by entity pair representations and augment data for underrepresented relations.
Outcome:	The proposed method outperforms state-of-the-art models on two benchmark datasets and is available on github.

Copied to clipboard

Hongze Mi, Yibo Feng, WenJie Lu, Yuqi Wang, Jinyuan Li, Song Cao, He Cui, Tengfei Tian, Xuelin Zhang, Haotian Luo, Di Sun, Jun Fang, Hua Chai, Naiqiang Tan, Gang Pan

Challenge:	Graphical User Interface (GUI) agents aim to automate a wide spectrum of human tasks by emulating user interaction.
Approach:	They propose a deliberative framework that leverages a fine-grained tip retrieval mechanism to inform its decision-making process.
Outcome:	The proposed framework achieves SOTA among open-source general models on AndroidWorld and ScreenSpot-V2 . it leverages a fine-grained, app-specific tip retrieval mechanism to inform its decision-making process .

How does Attention Affect the Model? (2021.findings-acl)

Copied to clipboard

Cheng Zhang, Qiuchi Li, Lingyu Hua, Dawei Song

Explainability & Interpretability in NLP

Challenge:	Existing studies on the effectiveness of attention in NLP do not consider changes in semantic capability of different components.
Approach:	They propose a framework that exploits a convex hull representation of sequence semantics in an n-dimensional Semantic Euclidean Space and defines indicators to capture the impact of attention on sequence semantic.
Outcome:	The proposed framework exploits a convex hull representation of sequence semantics in an n-dimensional Semantic Euclidean Space and defines indicators to capture the impact of attention on sequence semantic.

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

Copied to clipboard

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

Is “My Favorite New Movie” My Favorite Movie? Probing the Understanding of Recursive Noun Phrases (2022.naacl-main)

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.

Copied to clipboard

Qing Lyu, Zheng Hua, Daoxin Li, Li Zhang, Marianna Apidianaki, Chris Callison-Burch

Challenge:	Recursive noun phrases have interesting semantic properties, yet it is unknown whether language models have such knowledge.
Approach:	They propose a dataset of three textual inference tasks targeting recursive noun phrases . they show that such knowledge is learnable with appropriate data .
Outcome:	The proposed model achieves strong zero-shot performance on an extrinsic Harm Detection task.

Planning-Guided Tutoring with Assessment-Driven Memory for Pedagogical LLM Tutors (2026.acl-long)

Copied to clipboard

Zechen Li, Qiannan Zhu, Mei Wang, Jia Li, Hua Huang

Rethinking Jailbreak Detection of Large Vision Language Models with Representational Contrastive Scoring (2026.acl-long)

Challenge:	Existing approaches to simulate tutor behaviors or preferences fail to sustain high-quality pedagogical conversations that provide explicit stepwise scaffolding and adapt to learners’ evolving cognitive states.
Approach:	They propose a planning-guided tutoring framework with an assessment-driven memory for multi-turn math dialogue tutoring.
Outcome:	Experiments on multi-turn math tutoring benchmarks show that ScaffoldLM significantly improves pedagogical tutoring quality over strong baselines.

Copied to clipboard

Peichun Hua, Hao Li, Shanghao Shi, Zhiyuan Yu, Ning Zhang

DuReadervis: A Chinese Dataset for Open-domain Document Visual Question Answering (2022.findings-acl)

Challenge:	Large Vision-Language Models (LVLMs) are vulnerable to a growing array of multimodal jailbreak attacks, necessitating a generalizable defense that is efficient for practical deployment.
Approach:	They propose a framework that uses a lightweight projection to separate benign and malicious inputs in safety-critical layers.
Outcome:	The proposed framework enables a simple yet powerful contrastive score that differentiates true malicious intent from mere distribution shift.

Copied to clipboard

Le Qi, Shangwen Lv, Hongyu Li, Jing Liu, Yu Zhang, Qiaoqiao She, Hua Wu, Haifeng Wang, Ting Liu

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

Challenge:	Open-domain question answering is a task that requires answering questions based on a collection of document images.
Approach:	They propose to use document images to answer questions using layouts and visual features instead of text.
Outcome:	The proposed approach reduces human cost and improves scalability of QA systems by incorporating layouts and visual features.

Copied to clipboard

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

STACL: Simultaneous Translation with Implicit Anticipation and Controllable Latency using Prefix-to-Prefix Framework (P19-1)

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

Copied to clipboard

Mingbo Ma, Liang Huang, Hao Xiong, Renjie Zheng, Kaibo Liu, Baigong Zheng, Chuanqiang Zhang, Zhongjun He, Hairong Liu, Xing Li, Hua Wu, Haifeng Wang

Multilinguality

Challenge:	Simultaneous translation is notoriously dif- ficult due to word-order differences.
Approach:	They propose a prefix-to-prefix framework that implicitly learns to anticipate in a single translation model.
Outcome:	The proposed framework achieves low latency and reasonable qual- ity on 4 directions.

UP5: Unbiased Foundation Model for Fairness-aware Recommendation (2024.eacl-long)

Copied to clipboard

Wenyue Hua, Yingqiang Ge, Shuyuan Xu, Jianchao Ji, Zelong Li, Yongfeng Zhang

HyperEdit: Unlocking Instruction-based Text Editing in LLMs via Hypernetworks (2026.findings-acl)

Challenge:	Large Language Models (LLMs) are gaining a foothold in Recommender Systems (RS) but there is growing concern that LLMs perpetuate stereotypes and may result in unfair recommendations.
Approach:	They propose a counterfactually-fair-prompt method for LLM-based recommendation that is based on unbiased foundation mOdels.
Outcome:	The proposed method achieves better recommendation performance with a high level of fairness on two real-world datasets.

Copied to clipboard

Yiming Zeng, Jinghan Cao, Zexin Li, Wanhao Yu, Zhankai Ye, Dawei Xiang, Ting Hua, Xin Liu, Shangqian Gao, Tingting Yu

Text Generation

Challenge:	Existing approaches treat instruction-based text editing as a generic text generation problem. Existing methods either over-edit or fail to apply modifications consistently.
Approach:	They propose a framework that processes each editing request to best align with it.
Outcome:	The proposed framework achieves 9% improvement over the state-of-the-art model.

Agentic Episodic Control (2026.findings-acl)

Copied to clipboard

Xidong Yang, Wenhao Li, Junjie Sheng, Yun Hua, Haosheng Chen, Chuyun Shen, Xiangfeng Wang

Green & Sustainable NLP

Challenge:	Existing methods for reinforcement learning (RL) are limited by poor data efficiency and weak generalization.
Approach:	They propose a novel architecture that integrates large language models into episodic RL.
Outcome:	The proposed architecture achieves 2–6 higher data efficiency than baselines and is the only method to solve complex tasks like UnlockLocal with over 90% success.

DuReader-Retrieval: A Large-scale Chinese Benchmark for Passage Retrieval from Web Search Engine (2022.emnlp-main)

Copied to clipboard

Yifu Qiu, Hongyu Li, Yingqi Qu, Ying Chen, QiaoQiao She, Jing Liu, Hua Wu, Haifeng Wang

Information Retrieval

Challenge:	Existing datasets for non-English passage retrieval are lacking in quality and accuracy.
Approach:	They present a large-scale Chinese dataset for passage retrieval . they reduce false negatives by manually annotating results pooled from multiple retrievers .
Outcome:	The proposed dataset reduces false negatives in development and testing sets and removes similar training queries.

SudokuFill: A Multi-Agent Progressive Filling Framework for Document-Level Scientific Information Extraction (2026.findings-acl)

Copied to clipboard

Yang Li, Yajiao Wang, Yu Zhang, Yuanzhe Zhang, Maodi Hu, Mengting Zhang, Xi Sun, Hua Yue, Zhixiong Zhang

FinTextQA: A Dataset for Long-form Financial Question Answering (2024.acl-long)

Challenge:	Scientific information extraction (SciIE) is a key bottleneck for turning unstructured papers into computable knowledge bases.
Approach:	They propose a scientific information extraction framework that solves a Sudoku problem as a progressive filling problem.
Outcome:	The proposed framework outperforms the GPT-4o model on a document-level adjuvant dataset.

Copied to clipboard

Jian Chen, Peilin Zhou, Yining Hua, Loh Xin, Kehui Chen, Ziyuan Li, Bing Zhu, Junwei Liang

FRSUM: Towards Faithful Abstractive Summarization via Enhancing Factual Robustness (2022.findings-emnlp)

Challenge:	Existing financial question answering datasets lack scope diversity and question complexity.
Approach:	They propose to use a dataset for long-form question answering in finance to evaluate QA systems.
Outcome:	The proposed dataset includes 1,262 high-quality, source-attributed QA pairs extracted and selected from finance textbooks and government agency websites.

Copied to clipboard

Wenhao Wu, Wei Li, Jiachen Liu, Xinyan Xiao, Ziqiang Cao, Sujian Li, Hua Wu

Robustness in NLP

Challenge:	Existing models of abstractive summarization are able to generate fluent and coherent summaries, but they still suffer from the unfaithful generation problem.
Approach:	They propose to improve the faithfulness of existing models by enhancing their factual robustness by using a novel training strategy, namely FRSUM, which teaches the model to defend against both explicit adversarial samples and implicit factual adversarials.
Outcome:	The proposed training strategy improves faithfulness of various models, such as T5, BART, and T5 .

SCAR: Data Selection via Style Consistency-Aware Response Ranking for Efficient Instruction-Tuning of Large Language Models (2025.acl-long)

Copied to clipboard

Zhuang Li, Yuncheng Hua, Thuy-Trang Vu, Haolan Zhan, Lizhen Qu, Gholamreza Haffari

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

Challenge:	Recent studies show that manually ensuring a consistent response style and maintaining high data quality can significantly improve the performance of fine-tuned Large Language Models (LLMs).
Approach:	They introduce a style-aware response ranking system that prioritizes instruction-response pairs based on their stylistic consistency.
Outcome:	The proposed model matches or surpasses models trained on the entire dataset in coding and open-ended question-answering benchmarks.

Copied to clipboard

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

Leveraging Graph to Improve Abstractive Multi-Document Summarization (2020.acl-main)

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

Copied to clipboard

Wei Li, Xinyan Xiao, Jiachen Liu, Hua Wu, Haifeng Wang, Junping Du

CMD: a framework for Context-aware Model self-Detoxification (2024.emnlp-main)

Challenge:	Empirical results show that our model brings substantial improvements over several strong baselines.
Approach:	They propose a neural abstractive multi-document summarization model which captures cross-document relations and can guide the summary generation process.
Outcome:	The proposed model improves on the WikiSum and MultiNews datasets and can be easily combined with pre-trained language models.

Copied to clipboard

Zecheng Tang, Keyan Zhou, Juntao Li, Yuyang Ding, Pinzheng Wang, Yan Bowen, Renjie Hua, Min Zhang

CrochetBench: Can Vision-Language Models Move from Describing to Doing in Crochet Domain? (2026.acl-long)

Challenge:	Existing methods of text detoxification fail to achieve a decent balance between effectiveness and generation quality.
Approach:	They propose a text detoxification framework that pays attention to both context and detoxification process.
Outcome:	Experiments on various LLMs show that the proposed framework can yield the best performance compared to baselines.

Copied to clipboard

Peiyu Li, Xiaobao Huang, Ting Hua, Nitesh V Chawla

Leveraging Word-Formation Knowledge for Chinese Word Sense Disambiguation (2021.findings-emnlp)

Challenge:	Existing multimodal large language models cannot generate executable procedures . authors propose a new benchmark to assess procedural competence in multimodal models .
Approach:	They propose a new benchmark to assess procedural competence in multimodal large language models . they use a CrochetPARADE DSL representation to enable structural validation and functional evaluation .
Outcome:	The proposed model enables structural validation and functional evaluation via execution.

Copied to clipboard

Hua Zheng, Lei Li, Damai Dai, Deli Chen, Tianyu Liu, Xu Sun, Yang Liu

Cross-Lingual Leveled Reading Based on Language-Invariant Features (2021.findings-emnlp)

Challenge:	Word sense disambiguation (WSD) methods have not explored word-formations in parataxis languages like Chinese.
Approach:	They propose to leverage word-formation knowledge to enhance Chinese WSD by incorporating word-forms into sense disambiguation models.
Outcome:	The proposed model improves on baselines in Chinese word sense disambiguation (WSD) with word-formation knowledge, the results show.

Copied to clipboard

Simin Rao, Hua Zheng, Sujian Li

Multilinguality

Challenge:	Leveled reading (LR) aims to automatically classify texts by the cognitive levels of readers.
Approach:	They propose to use adversarial training and cross-lingual pre-training methods to transfer LR knowledge from annotated data in resource-rich English to Chinese.
Outcome:	The proposed method captures language-invariant features between English and Chinese.

Judge and Improve: Towards a Better Reasoning of Knowledge Graphs with Large Language Models (2025.emnlp-main)

Copied to clipboard

Mo Zhiqiang, Yang Hua, Jiahui Li, Yuan Liu, Shawn Wong, Jianmin Huang

ADO: Automatic Data Optimization for Inputs in LLM Prompts (2025.findings-acl)

Challenge:	Existing approaches to integrating graph and language models face two key limitations: achieving robust semantic alignment and ensuring interpretability in outputs.
Approach:	They propose a framework to integrate graph and language modalities while enhancing transparency.
Outcome:	Extensive experiments on three benchmark datasets show that the proposed framework surpasses existing methods in efficiency and generates outputs that are significantly more interpretable.

Copied to clipboard

Sam Lin, Wenyue Hua, Lingyao Li, Zhenting Wang, Yongfeng Zhang

Unveiling Privacy Risks in Multi-modal Large Language Models: Task-specific Vulnerabilities and Mitigation Challenges (2025.findings-acl)

Challenge:	Recent research has focused on refining instruction components and augmenting input data with in-context examples, but this study explores the potential benefits of optimizing the input data itself.
Approach:	They propose a content engineering and structural reformulation strategy to optimize input data within prompts to improve performance of Large Language Models.
Outcome:	The proposed approach improves performance of Large Language Models (LLMs) in various tasks, offering a promising avenue for future research in prompt engineering.

Copied to clipboard

Tiejin Chen, Pingzhi Li, Kaixiong Zhou, Tianlong Chen, Hua Wei

TrustAgent: Towards Safe and Trustworthy LLM-based Agents (2024.findings-emnlp)

Challenge:	Privacy risks in text-only Large Language Models are well-documented, especially their tendency to memorize and leak sensitive information.
Approach:	They propose a dataset to assess privacy risks across multi-modal tasks and scenarios . they demonstrate how models leak sensitive data across various tasks .
Outcome:	The proposed model can leak sensitive data embedded in images or stored in memory, exposing privacy risks.

Copied to clipboard

Wenyue Hua, Xianjun Yang, Mingyu Jin, Zelong Li, Wei Cheng, Ruixiang Tang, Yongfeng Zhang

RENOVI: A Benchmark Towards Remediating Norm Violations in Socio-Cultural Conversations (2024.findings-naacl)

Challenge:	Existing LLMs are primarily used for simple text-related tasks, but LLM-based agents can undertake more complex tasks that require planning and interaction with the physical world and humans.
Approach:	They propose an Agent-Constitution-based agent framework with a particular focus on improving the LLM-based agents' safety.
Outcome:	The proposed framework can enhance an LLM agent’s safety across multiple domains by identifying and mitigating potential dangers during the planning process.

Copied to clipboard

Haolan Zhan, Zhuang Li, Xiaoxi Kang, Tao Feng, Yuncheng Hua, Lizhen Qu, Yi Ying, Mei Rianto Chandra, Kelly Rosalin, Jureynolds Jureynolds, Suraj Sharma, Shilin Qu, Linhao Luo, Ingrid Zukerman, Lay-Ki Soon, Zhaleh Semnani Azad, Reza Haf

Dialogue Systems & Conversational Agents

Challenge:	Norm violations occur when individuals fail to conform to culturally accepted behaviors, which may lead to potential conflicts.
Approach:	They propose to use a large corpus of 9,258 multi-turn dialogues annotated with social norms to equip AI systems with a remediation ability.
Outcome:	The proposed system can understand and remediate norm violations step by step.

IMO: Greedy Layer-Wise Sparse Representation Learning for Out-of-Distribution Text Classification with Pre-trained Models (2024.acl-long)

Copied to clipboard

Tao Feng, Lizhen Qu, Zhuang Li, Haolan Zhan, Yuncheng Hua, Reza Haf

SgSum:Transforming Multi-document Summarization into Sub-graph Selection (2021.emnlp-main)

Challenge:	IMO is a machine learning model that learns invariant features from unseen domains.
Approach:	They propose IMO: Invariant features Masks for Out-of-Distribution text classification to achieve OOD generalization by learning invariant feature masks.
Outcome:	The proposed model outperforms baseline models in various evaluation metrics and settings.

Copied to clipboard

Moye Chen, Wei Li, Jiachen Liu, Xinyan Xiao, Hua Wu, Haifeng Wang

Large Language Models Are Poor Clinical Decision-Makers: A Comprehensive Benchmark (2024.emnlp-main)

Challenge:	Existing extractive multi-document summarization methods score each sentence individually and extract salient sentences one by one.
Approach:	They propose a novel framework for extractive multi-document summarization that selects a sub-graph as the summary instead of selecting salient sentences.
Outcome:	The proposed framework improves on existing methods on multi-document datasets and human evaluations show it produces more coherent and informative summaries.

Copied to clipboard

Fenglin Liu, Zheng Li, Hongjian Zhou, Qingyu Yin, Jingfeng Yang, Xianfeng Tang, Chen Luo, Ming Zeng, Haoming Jiang, Yifan Gao, Priyanka Nigam, Sreyashi Nag, Bing Yin, Yining Hua, Xuan Zhou, Omid Rohanian, Anshul Thakur, Lei Clifton, David Clifton

Beyond Completion: A Foundation Model for General Knowledge Graph Reasoning (2025.findings-acl)

Challenge:	Existing studies focus on evaluating large language models in close-ended QA tasks, but many clinical decisions involve answering open-ended questions without pre-set options.
Approach:	They construct a benchmark to better understand large language models in the clinic . they use existing datasets to evaluate LLMs in clinical situations .
Outcome:	The proposed model outperforms human experts in multiple medical tasks.

Copied to clipboard

Yin Hua, Zhiqiang Liu, Mingyang Chen, Zheng Fang, Chi Man Wong, Lingxiao Li, Chi Man Vong, Huajun Chen, Wen Zhang

Structured Data in NLP

Challenge:	Existing foundation models for general knowledge graph reasoning have focused on their structural aspects, with most efforts restricted to in-KG tasks.
Approach:	They propose a conditional encoding architecture that bridges the gap between textual and structural modalities, enabling seamless integration.
Outcome:	The proposed model outperforms baseline models on 28 datasets and is generalized to out-of-KG tasks.

Vision Language Model Helps Private Information De-Identification in Vision Data (2025.findings-acl)

Copied to clipboard

Tiejin Chen, Pingzhi Li, Kaixiong Zhou, Tianlong Chen, Hua Wei

Visual Data in NLP

Challenge:	Visual Language Models (VLMs) have gained popularity due to their ability to solve imagerelated tasks.
Approach:	They propose a framework to enhance privacy awareness of visual language models . they use a specialized instruction-tuning dataset and a tailored training methodology .
Outcome:	The proposed framework outperforms existing approaches in handling private information.

Multi-Passage Machine Reading Comprehension with Cross-Passage Answer Verification (P18-1)

Copied to clipboard

Yizhong Wang, Kai Liu, Jing Liu, Wei He, Yajuan Lyu, Hua Wu, Sujian Li, Haifeng Wang

Reasoning

Challenge:	Recent years have seen rapid growth in the MRC community . MRC is believed to be a crucial step in building a general intelligent agent .
Approach:	They propose an end-to-end neural model that enables multiple passages to verify each other based on their content representations.
Outcome:	The proposed model outperforms the baseline on the English MS-MARCO dataset and the Chinese DuReader dataset, and achieves state-of-the-art performance on both datasets.

Few-Shot Complex Knowledge Base Question Answering via Meta Reinforcement Learning (2020.emnlp-main)

Copied to clipboard

Yuncheng Hua, Yuan-Fang Li, Gholamreza Haffari, Guilin Qi, Tongtong Wu

D-NET: A Pre-Training and Fine-Tuning Framework for Improving the Generalization of Machine Reading Comprehension (D19-58)

Challenge:	Existing approaches to complex question-answering (CQA) exhibit uneven performance when questions have different types, harboring inherently different characteristics, e.g., difficulty level.
Approach:	They propose a meta-reinforcement learning approach to program induction in CQA to tackle the potential distributional bias in questions.
Outcome:	The proposed method achieves state-of-the-art performance on the CQA dataset while using only five trial trajectories for the top-5 retrieved questions in each support set.

Copied to clipboard

Hongyu Li, Xiyuan Zhang, Yibing Liu, Yiming Zhang, Quan Wang, Xiangyang Zhou, Jing Liu, Hua Wu, Haifeng Wang

The Strength of the Weakest Supervision: Topic Classification Using Class Labels (N19-3)

Challenge:	MRC requires machines to understand text and answer questions about the text.
Approach:	They propose a simple system Baidu submitted for MRQA 2019 Shared Task that focused on generalization of machine reading comprehension (MRC) models.
Outcome:	The proposed system is ranked at top 1 of all participants in terms of averaged F1 score.

Copied to clipboard

Jiatong Li, Kai Zheng, Hua Xu, Qiaozhu Mei, Yue Wang

UNIMO-2: End-to-End Unified Vision-Language Grounded Learning (2022.findings-acl)

Challenge:	a topic classifier can understand only class labels when training for tasks that require a large amount of labeled documents.
Approach:	They propose an algorithm that can initialize a topic classifier using only class labels . they propose a method that combines word embedding and naive Bayes classification .
Outcome:	The proposed approach saves significant initial labeling effort by providing a "warm start" the proposed approach can be fine-tuned with more labeled documents to reach a certain performance level.

Copied to clipboard

Wei Li, Can Gao, Guocheng Niu, Xinyan Xiao, Hao Liu, Jiachen Liu, Hua Wu, Haifeng Wang

RCTEA: Richness-guided Co-training for Temporal Entity Alignment (2026.findings-acl)

Challenge:	Existing methods for vision-language pre-training can only learn from aligned image-caption data and rely heavily on expensive regional features.
Approach:	They propose an end-to-end unified-modal pre-training framework for joint learning . they propose to conduct grounded learning on both images and texts via a sharing grounded space .
Outcome:	The proposed model improves visual and visual semantic alignment on images and texts.

Copied to clipboard

Jiayun Li, Wen Hua, Shiqi Fan, Fengmei Jin, Haiyang Jiang, Xue Li

Mixup Decoding for Diverse Machine Translation (2021.findings-emnlp)

Challenge:	Existing TEA models fail to capture the orthogonal yet complementary effect between structural and temporal features.
Approach:	They propose a framework that jointly models structural and temporal aspects of Temporal Knowledge Graphs for entity alignment.
Outcome:	The proposed framework achieves state-of-the-art on public TEA benchmarks.

Copied to clipboard

Jicheng Li, Pengzhi Gao, Xuanfu Wu, Yang Feng, Zhongjun He, Hua Wu, Haifeng Wang

Machine Translation

Challenge:	Existing methods for generating multiple translations for source and target languages neglect the one-to-many mapping between the source and the target languages.
Approach:	They propose a method to generate different translations for the input sentence by linearly interpolating it with different sentence pairs sampled from the training corpus during decoding.
Outcome:	Experiments on WMT’16 en-ro, WMT'14 en de, and WMT ‘17 zh-en show that the proposed method outperforms all previous diverse machine translation methods.

BASS: Boosting Abstractive Summarization with Unified Semantic Graph (2021.acl-long)

Copied to clipboard

Wenhao Wu, Wei Li, Xinyan Xiao, Jiachen Liu, Ziqiang Cao, Sujian Li, Hua Wu, Haifeng Wang

DuSQL: A Large-Scale and Pragmatic Chinese Text-to-SQL Dataset (2020.emnlp-main)

Challenge:	Abstractive summarization for long-document or multi-document remains challenging for Seq2Seq as it does not analyze long-distance relations in text.
Approach:	They propose a framework for Boosting Abstractive Summarization based on a unified Semantic graph which aggregates co-referent phrases distributing across a long range of context and conveys rich relations between phrases.
Outcome:	The proposed framework improves document representation and summary generation process by leveraging the graph structure.

Copied to clipboard

Lijie Wang, Ao Zhang, Kun Wu, Ke Sun, Zhenghua Li, Hua Wu, Min Zhang, Haifeng Wang

Programming Languages in NLP

Challenge:	Existing text-to-SQL parsing methods mainly focus on English, but there is no labeled data available for the language . a larges-scale and pragmatic Chinese dataset is used for cross-domain text- to-Sql task .
Approach:	They propose a larges-scale Chinese dataset for a cross-domain text-to-SQL task . they analyze questions from several representative applications and use an effective data construction framework .
Outcome:	The proposed dataset contains 200 databases, 813 tables, and 23,797 question/SQL pairs.

Data Augmentation with Hierarchical SQL-to-Question Generation for Cross-domain Text-to-SQL Parsing (2021.emnlp-main)

Copied to clipboard

Kun Wu, Lijie Wang, Zhenghua Li, Ao Zhang, Xinyan Xiao, Hua Wu, Min Zhang, Haifeng Wang

Programming Languages in NLP

Challenge:	Existing approaches to parse text-to-SQL data are lacking labeled data for unseen evaluation databases.
Approach:	They propose a framework for enhancing SQL queries by automatically producing large numbers of SQL queries based on an abstract syntax tree grammar.
Outcome:	The proposed framework can produce high-quality natural language questions over strong baselines.

Dynamic Low-rank Estimation for Transformer-based Language Models (2023.findings-emnlp)

Copied to clipboard

Ting Hua, Xiao Li, Shangqian Gao, Yen-Chang Hsu, Yilin Shen, Hongxia Jin

Cross-Utterance Conditioned VAE for Non-Autoregressive Text-to-Speech (2022.acl-long)

Challenge:	RankDyna is a matrix decomposition method that can be used to compress Transformer-based language models.
Approach:	They propose a matrix decomposition method that enables dynamic rank resource allocation . they say it can outperform current SOTA methods under various parameter budget levels .
Outcome:	The proposed method outperforms current SOTA methods under various budget levels . the proposed method is more efficient with higher compression rates .

Copied to clipboard

Yang Li, Cheng Yu, Guangzhi Sun, Hua Jiang, Fanglei Sun, Weiqin Zu, Ying Wen, Yang Yang, Jun Wang

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

Challenge:	Experimental results show that the proposed model improves naturalness and prosody diversity with clear margins.
Approach:	They propose a cross-utterance conditional VAE to estimate posterior probability distribution of latent prosody features for each phoneme by conditioning on acoustic features, speaker information, and text features from past and future sentences.
Outcome:	The proposed model improves naturalness and prosody diversity with clear margins.

Copied to clipboard

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

Exploring Chain-of-Thought for Multi-modal Metaphor Detection (2024.acl-long)

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.

Copied to clipboard

Yanzhi Xu, Yueying Hua, Shichen Li, Zhongqing Wang

Sentiment Analysis

Challenge:	Metaphors are commonly found in advertising and internet memes, but lack of high-quality textual data is a challenge for language models . a new framework for multi-modal metaphor detection is being developed to address these challenges .
Approach:	They propose a framework that extracts and integrates knowledge from Large Language Models into smaller ones to improve model performance.
Outcome:	The proposed framework outperforms existing models on the MET-MEME dataset.

Let’s Negotiate! A Survey of Negotiation Dialogue Systems (2024.findings-eacl)

Copied to clipboard

Haolan Zhan, Yufei Wang, Zhuang Li, Tao Feng, Yuncheng Hua, Suraj Sharma, Lizhen Qu, Zhaleh Semnani Azad, Ingrid Zukerman, Reza Haf

ERNIE-Gram: Pre-Training with Explicitly N-Gram Masked Language Modeling for Natural Language Understanding (2021.naacl-main)

Challenge:	Recent research has focused on negotiation dialogue systems, but no systematic review of this task has been conducted.
Approach:	They propose to provide a systematic review of negotiation dialogue systems and to provide an overview of current research.
Outcome:	The proposed systems are based on the literature and are compared against existing systems.

Copied to clipboard

Dongling Xiao, Yu-Kun Li, Han Zhang, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang

TEXTOIR: An Integrated and Visualized Platform for Text Open Intent Recognition (2021.acl-demo)

Challenge:	Existing methods to model coarse-grained linguistic information do not integrate coarse-gram information into pre-training.
Approach:	They propose an explicitly n-gram masking method to enhance integration of coarse-grained linguistic information into pre-training.
Outcome:	The proposed method outperforms existing models on English and Chinese text corpora and fine-tunes on 19 downstream tasks.

Copied to clipboard

Hanlei Zhang, Xiaoteng Li, Hua Xu, Panpan Zhang, Kang Zhao, Kai Gao

Sentiment Analysis

Challenge:	TEXTOIR is the first integrated platform for text open intent recognition . currently, many dialogue systems are limited to handle the uncertain open intents .
Approach:	TEXTOIR is the first integrated platform for text open intent recognition . it is composed of two main modules: open intent detection and open intent discovery . authors propose a framework to implement a complete process to identify known intents and discover open intents .
Outcome:	TEXTOIR is the first integrated and visualized platform for text open intent recognition . it integrates state-of-the-art algorithms and benchmark intent datasets . however, there are still some issues, which bring difficulties for future research .

Golden Touchstone: A Comprehensive Bilingual Benchmark for Evaluating Financial Large Language Models (2025.findings-emnlp)

Copied to clipboard

Xiaojun Wu, Junxi Liu, Huan-Yi Su, Zhouchi Lin, Yiyan Qi, Chengjin Xu, Jiajun Su, Jiajie Zhong, Fuwei Wang, Saizhuo Wang, Fengrui Hua, Jia Li, Jian Guo

Decompose, Fuse and Generate: A Formation-Informed Method for Chinese Definition Generation (2021.naacl-main)

Challenge:	Existing financial benchmarks suffer from limited language and task coverage, low-quality datasets, and inadequate adaptability for LLM evaluation.
Approach:	They propose a bilingual benchmark for financial LLMs that assesses models’ language understanding and generation capabilities.
Outcome:	The proposed bilingual benchmark assesses models’ language understanding and generation capabilities.

Copied to clipboard

Hua Zheng, Damai Dai, Lei Li, Tianyu Liu, Zhifang Sui, Baobao Chang, Yang Liu

Challenge:	Existing definition generation methods take the source word as an indecomposable semantic unit, but in parataxis languages like Chinese, word meanings can be composed using the word formation process.
Approach:	They propose to use word formation features to enhance Definition Generation (DG) in Chinese to generate an explanatory text.
Outcome:	The proposed model enhances Definition Generation (DG) in Chinese by decomposing the word meaning into different semantic components.

A Predicate-Function-Argument Annotation of Natural Language for Open-Domain Information eXpression (2020.emnlp-main)

Copied to clipboard

Mingming Sun, Wenyue Hua, Zoey Liu, Xin Wang, Kangjie Zheng, Ping Li

Open Information Extraction

Challenge:	Existing OIE (Open Information Extraction) algorithms are redundant and not reusable.
Approach:	They propose a pipeline where an Open-domain Information eXpression task provides a platform for all OIE strategies.
Outcome:	The proposed pipeline provides a platform for all OIE strategies.

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

Copied to clipboard

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

Towards a Design Guideline for RPA Evaluation: A Survey of Large Language Model-Based Role-Playing Agents (2025.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.

Copied to clipboard

Chaoran Chen, Bingsheng Yao, Ruishi Zou, Wenyue Hua, Weimin Lyu, Toby Jia-Jun Li, Dakuo Wang