Papers by Zihan Chen

Contrastive Token Learning with Similarity Decay for Repetition Suppression in Machine Translation (2024.findings-emnlp)

Challenge:	Existing approaches to search for images using single-modality are limited by representation space fragmentation.
Approach:	They propose a unified representation framework that achieves efficient query-target alignment . they introduce a multi-level Chain-of-Thought prompting strategy that guides MLMs to generate discriminative, semantically compatible captions for target images .
Outcome:	The proposed framework achieves efficient query-target alignment through synergistic components.

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Huangyu Dai, Ben Chen, Kaidi Chen, Ying Han, Zihan Liang, Wen Jiang

Multilinguality

Challenge:	Neural machine translation (NMT) is pivotal for crosslingual conversation and trade . traditional solutions that penalize text redundancy or token reoccurrence have shown limited efficacy .
Approach:	They propose an algorithm that modulates suppression of tokens dynamically, informed by attention weights and inter-token distances.
Outcome:	The proposed algorithm outperforms existing methods in precision and generalizability.

FastGAS: Fast Graph-based Annotation Selection for In-Context Learning (2024.findings-acl)

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Zihan Chen, Song Wang, Cong Shen, Jundong Li

Low-Resource NLP

Challenge:	Existing methods to select unlabeled examples for annotation require a long time due to their complexity, hindering their practical viability.
Approach:	They propose a graph-based selection method to efficiently identify high-quality instances while minimizing computational overhead.
Outcome:	The proposed method significantly reduces selection time and improves performance on different tasks.

TIE: Topological Information Enhanced Structural Reading Comprehension on Web Pages (2022.naacl-main)

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Zihan Zhao, Lu Chen, Ruisheng Cao, Hongshen Xu, Xingyu Chen, Kai Yu

Machine Reading Comprehension

Challenge:	Existing models for structural reading comprehension (SRC) only focus on comprehension of plain text, tables, tables or knowledge bases.
Approach:	They propose a topological information enhanced model which transforms a token-level task into a tag-level one by introducing a two-stage process.
Outcome:	The proposed model outperforms baselines and achieves state-of-the-art performance on the web-based SRC benchmark WebSRC at the time of writing.

Benchmarking Foundation Models with Retrieval-Augmented Generation in Olympic-Level Physics Problem Solving (2025.findings-emnlp)

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Shunfeng Zheng, Yudi Zhang, Meng Fang, Zihan Zhang, Zhitan Wu, Mykola Pechenizkiy, Ling Chen

ShadowGNN: Graph Projection Neural Network for Text-to-SQL Parser (2021.naacl-main)

Challenge:	a new study examines the potential of retrieval-augmented generation (RAG) with foundation models to enhance expert-level reasoning.
Approach:	They introduce PhoPile, a high-quality multimodal dataset specifically designed for Olympiad-level physics.
Outcome:	The proposed model can be used to solve Olympiad-level physics problems.

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Zhi Chen, Lu Chen, Yanbin Zhao, Ruisheng Cao, Zihan Xu, Su Zhu, Kai Yu

How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances (2023.emnlp-main)

Challenge:	Existing semantic parsing models struggle to adapt to unseen database schemas . a new architecture, ShadowGNN, processes schemas at abstract and semantic levels .
Approach:	They propose a new architecture which processes schemas at abstract and semantic levels.
Outcome:	The proposed architecture outperforms state-of-the-art models on a text-to-sql benchmark . it uses domain-independent representations to extract logical linking between question and schema .

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Zihan Zhang, Meng Fang, Ling Chen, Mohammad-Reza Namazi-Rad, Jun Wang

Rethinking RL Evaluation: Can Benchmarks Truly Reveal Failures of RL Methods? (2026.findings-acl)

Challenge:	Large language models (LLMs) are impressive in solving tasks, but they can quickly be outdated after deployment.
Approach:	They provide a review of recent advances in aligning deployed large language models with the ever-changing world knowledge.
Outcome:	The proposed models can be used to perform various tasks directly through in-context learning or for further fine-tuning for domain-specific uses.

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Zihan Chen, Yiming Zhang, Hengguang Zhou, Zenghui Ding, Yining Sun, Cho-Jui Hsieh

Challenge:	Existing benchmarks for reinforcement learning for large language models do not accurately assess generalization.
Approach:	They propose three core principles for designing more faithful benchmarks: sufficient difficulty, balanced evaluation, and distributional robustness.
Outcome:	The proposed benchmarks do not accurately assess generalization across distribution shifts, difficulty levels, and counterfactual scenarios.

Global Adaptive Momentum Meets Local Personalized Perturbation: Efficient Federated LLM Fine-Tuning with Zeroth-Order Gradients (2026.acl-long)

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Zihan Chen, Howard Hao Yang, Tony Quek, Kai Fong Ernest Chong

Classic4Children: Adapting Chinese Literary Classics for Children with Large Language Model (2025.findings-naacl)

Challenge:	federated fine-tuning of large language models provides privacy-preserving approach to deploying pervasive generative AI services.
Approach:	They propose a federated framework for fine-tuning large language models . they propose unified optimization and local personalized perturbation for ZO gradients .
Outcome:	The proposed framework outperforms existing methods for integrating ZO gradients in federated learning over diverse heterogeneous data settings.

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Jiali Chen, Xusen Hei, Yuqi Xue, Zihan Wu, Jiayuan Xie, Yi Cai

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

Challenge:	Recent large language models (LLMs) overlook children’s reading preferences, which poses challenges in CLA.
Approach:	They propose a method that augments large language models with children's reading preferences for adaptation by obtaining characters' personalities and narrative structure as additional information for fine-grained instruction tuning.
Outcome:	The proposed method significantly improves performance in automatic and human evaluation.

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

Alignment for Efficient Tool Calling of Large Language Models (2025.emnlp-main)

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

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Hongshen Xu, Zihan Wang, Zichen Zhu, Lei Pan, Xingyu Chen, Shuai Fan, Lu Chen, Kai Yu

PlanningArena: A Modular Benchmark for Multidimensional Evaluation of Planning and Tool Learning (2025.acl-long)

Challenge:	Recent advances in tool learning have enabled large language models to integrate external tools, enhancing their task performance by expanding their knowledge boundaries.
Approach:	They propose a framework that combines probabilistic knowledge boundary estimation with dynamic decision-making to allow LLMs to better assess when to invoke tools based on their confidence.
Outcome:	The proposed framework shows significant improvements in tool efficiency by reducing unnecessary tool usage.

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Zihan Zheng, Tianle Cui, Chuwen Xie, Jiahui Pan, Qianglong Chen, Lewei He

RetrievalQA: Assessing Adaptive Retrieval-Augmented Generation for Short-form Open-Domain Question Answering (2024.findings-acl)

Challenge:	Recent studies have shown that LLMs can be significantly improved by integrating external tools.
Approach:	They propose a framework that integrates external tools into large language models to evaluate their ability to generate action plans.
Outcome:	The proposed framework evaluates the ability of large language models to generate action plans and generate action plan templates.

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Zihan Zhang, Meng Fang, Ling Chen

Pre-training Language Model as a Multi-perspective Course Learner (2023.findings-acl)

Challenge:	Existing methods for assessing retrieval of relevant information are understudied . previous studies have neglected to evaluate ARAG methods .
Approach:	They propose a benchmark to evaluate existing ARAG methods that use threshold tuning to adjust retrieval for queries instead of indiscriminate retrieval.
Outcome:	The proposed method can be used to evaluate existing ARAG methods without calibration or training.

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Beiduo Chen, Shaohan Huang, Zihan Zhang, Wu Guo, Zhenhua Ling, Haizhen Huang, Furu Wei, Weiwei Deng, Qi Zhang

MoDULA: Mixture of Domain-Specific and Universal LoRA for Multi-Task Learning (2024.emnlp-main)

Challenge:	Experimental results show that our method significantly improves ELECTRA’s average performance by 2.8% and 3.2% absolute points respectively on GLUE and SQuAD 2.0 benchmarks.
Approach:	They propose a multi-perspective course learning method to fetch many degrees and visual angles for sample-efficient pre-training and to fully leverage the relationship between generator and discriminator.
Outcome:	The proposed method improves ELECTRA's performance on GLUE and SQuAD 2.0 benchmarks and overshadows recent advanced ELECL-style models under the same settings.

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Yufei Ma, Zihan Liang, Huangyu Dai, Ben Chen, Dehong Gao, Zhuoran Ran, Wang Zihan, Linbo Jin, Wen Jiang, Guannan Zhang, Xiaoyan Cai, Libin Yang

Responsible & Trustworthy NLP

Challenge:	Recent advances in open-source Large Language Models (LLMs) have achieved notable successes in natural language processing.
Approach:	They propose a Parameter Efficient Fine-Tuning paradigm for improved fine-tuning and parameter efficiency in multi-task learning.
Outcome:	The proposed model outperforms existing methods on multi-task learning while reducing training costs by over 80% without losing general capability.

Learning from Diverse Reasoning Paths with Routing and Collaboration (2025.emnlp-main)

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Zhenyu Lei, Zhen Tan, Song Wang, Yaochen Zhu, Zihan Chen, Yushun Dong, Jundong Li

Is Neural Topic Modelling Better than Clustering? An Empirical Study on Clustering with Contextual Embeddings for Topics (2022.naacl-main)

Challenge:	Recent studies suggest that the reasoning abilities of large language models (LLMs) grows with model size and pre-training data.
Approach:	They propose to combine quality filtering, conditional routing, and cooperative peer teaching to transfer knowledge from powerful teacher models to compact and transparent students.
Outcome:	Experiments show that QR-Distill is superior to traditional methods.

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Zihan Zhang, Meng Fang, Ling Chen, Mohammad Reza Namazi Rad

Information Extraction & Text Mining

Challenge:	Recent work incorporates pre-trained word embeddings into Neural Topic Models (NTMs), generating highly coherent topics.
Approach:	They conduct thorough experiments to investigate whether embeddings directly with an appropriate word selection method can generate more coherent and diverse topics than NTMs.
Outcome:	The proposed model generates more coherent and diverse topics than traditional NTMs, achieving higher efficiency and simplicity.

NaturalGAIA: A Verifiable Benchmark and Hierarchical Framework for Long-Horizon GUI Tasks (2026.acl-long)

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Zihan Zheng, Tianle Cui, Taoran Wang, Fengtao Wang, Jiahui Pan, Lewei He, Qianglong Chen

Challenge:	Current research faces an "Evaluation-Realism Dilemma" due to unstable MLLM judges or manual verification.
Approach:	They propose a verifiable evaluation dataset grounded in real-world human GUI intents.
Outcome:	The proposed framework outperforms the state-of-the-art framework in achieving a weighted pathway success rate of 45.6% while reducing token consumption and execution time by 76%.

CITB: A Benchmark for Continual Instruction Tuning (2023.findings-emnlp)

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Zihan Zhang, Meng Fang, Ling Chen, Mohammad-Reza Namazi-Rad

Retrieve-Plan-Generation: An Iterative Planning and Answering Framework for Knowledge-Intensive LLM Generation (2024.emnlp-main)

Challenge:	Existing methods for instruction tuning do not leverage the rich natural language instructions.
Approach:	They propose to use a benchmark to study how instruction tuning works in CL tasks.
Outcome:	The proposed method can achieve similar or better results than existing CL methods.

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Yuanjie Lyu, Zihan Niu, Zheyong Xie, Chao Zhang, Tong Xu, Yang Wang, Enhong Chen

ASCEND: A Spontaneous Chinese-English Dataset for Code-switching in Multi-turn Conversation (2022.lrec-1)

Challenge:	Large language models (LLMs) often produce factual errors due to limited internal knowledge.
Approach:	They propose a retrieval-augmented generation framework that generates plan tokens to guide subsequent generation.
Outcome:	The proposed framework improves the accuracy of large language models with external knowledge sources.

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Holy Lovenia, Samuel Cahyawijaya, Genta Winata, Peng Xu, Yan Xu, Zihan Liu, Rita Frieske, Tiezheng Yu, Wenliang Dai, Elham J. Barezi, Qifeng Chen, Xiaojuan Ma, Bertram Shi, Pascale Fung

Dialogue Systems & Conversational Agents

Challenge:	Code-switching is a speech phenomenon occurring when a speaker switches language during a conversation.
Approach:	They propose to collect Mandarin Chinese-English code-switching corpus from read speech rather than spontaneous speech to address this phenomenon.
Outcome:	ASCEND consists of 10.62 hours of clean speech, collected from 23 bilingual speakers of Chinese and English.

AceMath: Advancing Frontier Math Reasoning with Post-Training and Reward Modeling (2025.findings-acl)

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Zihan Liu, Yang Chen, Mohammad Shoeybi, Bryan Catanzaro, Wei Ping

From Cross-Task Examples to In-Task Prompts: A Graph-Based Pseudo-Labeling Framework for In-context Learning (2025.findings-emnlp)

Challenge:	In this paper, we introduce a suite of math models that excel in solving complex math problems.
Approach:	They propose a supervised fine-tuning process that achieves competitive performance across general domains, followed by targeted fine- tuning for the math domain using a carefully curated set of prompts and synthetically generated responses.
Outcome:	The proposed model outperforms Qwen2.5-Math-72B-Instruct, GPT-4o and Claude-3.5 Sonnet in the math domain.

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Zihan Chen, Song Wang, Xingbo Fu, Chengshuai Shi, Zhenyu Lei, Cong Shen, Jundong Li

Low-Resource NLP

Challenge:	In-context learning (ICL) enables large language models to perform novel tasks without parameter updates by conditioning on a few input-output examples.
Approach:	They propose a cost-efficient two-stage pipeline that reduces reliance on LLMs for data labeling.
Outcome:	The proposed pipeline reduces reliance on LLMs for data labeling . it leverages readily available cross-task examples to prompt an LLM and pseudo-label a small set of target task instances.

Multi-Defendant Legal Judgment Prediction via Hierarchical Reasoning (2023.findings-emnlp)

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Yougang Lyu, Jitai Hao, Zihan Wang, Kai Zhao, Shen Gao, Pengjie Ren, Zhumin Chen, Fang Wang, Zhaochun Ren

LLaSE-G1: Incentivizing Generalization Capability for LLaMA-based Speech Enhancement (2025.acl-long)

Challenge:	Existing methods for predicting judgment results for multiple defendants are ineffective.
Approach:	They propose a method to predict the judgment results for each defendant in multi-defendant cases . they formalize the multi-diffendant judgment process as hierarchical reasoning chains .
Outcome:	The proposed method can predict the judgment results for multiple defendants in multi-defendant cases.

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Boyi Kang, Xinfa Zhu, Zihan Zhang, Zhen Ye, Mingshuai Liu, Ziqian Wang, Yike Zhu, Guobin Ma, Jun Chen, Longshuai Xiao, Chao Weng, Wei Xue, Lei Xie

Generalizing Few-Shot Named Entity Recognizers to Unseen Domains with Type-Related Features (2023.findings-emnlp)

Challenge:	Recent advances in language models have demonstrated strong capabilities in semantic understanding and contextual modeling.
Approach:	They propose a LLaMA-based language model that incentivizes generalization capabilities for speech enhancement.
Outcome:	The proposed language model outperforms prior task-specific discriminative and generative models in acoustic enhancement tasks.

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Zihan Wang, Ziqi Zhao, Zhumin Chen, Pengjie Ren, Maarten de Rijke, Zhaochun Ren

CoRAG: Enhancing Hybrid Retrieval-Augmented Generation through a Cooperative Retriever Architecture (2025.findings-emnlp)

Challenge:	Few-shot named entity recognition methods struggle with out-of-domain (OOD) examples due to their reliance on manual labeling for the target domain.
Approach:	They propose a framework to enable generalization to an unseen target domain with only a few labeled examples.
Outcome:	The proposed framework achieves significant performance improvements on in-domain and cross-domain datasets.

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Zaiyi Zheng, Song Wang, Zihan Chen, Yaochen Zhu, Yinhan He, Liangjie Hong, Qi Guo, Jundong Li

WebSRC: A Dataset for Web-Based Structural Reading Comprehension (2021.emnlp-main)

Challenge:	Existing methods only retrieve related documents from local neighbors or subgraphs in the knowledge base, which often miss relevant information located further away from a global view.
Approach:	Hybrid-RAG combines textual documents and graph-structured relational information for RAG . existing methods only retrieve related documents from local neighbors or subgraphs in the knowledge base .
Outcome:	Hybrid-RAG combines textual documents and graph-structured relational information . existing methods only retrieve related documents from local neighbors or subgraphs in the knowledge base .

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Xingyu Chen, Zihan Zhao, Lu Chen, JiaBao Ji, Danyang Zhang, Ao Luo, Yuxuan Xiong, Kai Yu

AnyMAC: Cascading Flexible Multi-Agent Collaboration via Next-Agent Prediction (2025.emnlp-main)

Challenge:	Using a web page and a question, a machine can't understand the contents of web pages.
Approach:	They propose a novel dataset for web-based structural reading comprehension that consists of 400K question-answer pairs and a dataset of 6.4K web pages.
Outcome:	The proposed dataset consists of 400K question-answer pairs, collected from 6.4K web pages with corresponding HTML source code, screenshots, and metadata.

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Song Wang, Zhen Tan, Zihan Chen, Shuang Zhou, Tianlong Chen, Jundong Li

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

Challenge:	Existing methods for multi-agent collaboration rely on static or graph-based topologies lacking flexibility and adaptability.
Approach:	They propose a new framework that rethinks multi-agent coordination through a sequential structure rather than a graph structure.
Outcome:	The proposed method achieves superior performance while significantly reducing communication overhead.

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

Information Extraction & Text Mining

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

InfoGain-RAG: Boosting Retrieval-Augmented Generation through Document Information Gain-based Reranking and Filtering (2025.emnlp-main)

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Zihan Wang, Zihan Liang, Zhou Shao, Yufei Ma, Huangyu Dai, Ben Chen, Lingtao Mao, Chenyi Lei, Yuqing Ding, Han Li

MedINST: Meta Dataset of Biomedical Instructions (2024.findings-emnlp)

Challenge:	Retrieval-Augmented Generation (RAG) frameworks struggle with identifying whether retrieved documents meaningfully contribute to answer generation.
Approach:	They propose a document-related metric to quantify the contribution of retrieved documents to correct answer generation.
Outcome:	The proposed framework outperforms existing approaches on both single and multiple retrieval paradigms.

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Wenhan Han, Meng Fang, Zihan Zhang, Yu Yin, Zirui Song, Ling Chen, Mykola Pechenizkiy, Qingyu Chen

CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases (D19-1)

Challenge:	Medical data and tasks require extensive preprocessing and standardization for effective use in training LLMs.
Approach:	They propose to use MedINST as a meta-dataset to evaluate LLMs' generalization ability.
Outcome:	The meta-dataset of biomedical instruction measures the generalization ability of LLMs across multiple open-domain tasks.

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Tao Yu, Rui Zhang, Heyang Er, Suyi Li, Eric Xue, Bo Pang, Xi Victoria Lin, Yi Chern Tan, Tianze Shi, Zihan Li, Youxuan Jiang, Michihiro Yasunaga, Sungrok Shim, Tao Chen, Alexander Fabbri, Zifan Li, Luyao Chen, Yuwen Zhang, Shreya Dixit, Vincent Zhang, Caiming Xiong, Richard Socher, Walter Lasecki, Dragomir Radev

Programming Languages in NLP

Challenge:	CoSQL is a corpus for building cross-domain, general-purpose database querying dialogue systems.
Approach:	They present a corpus for building cross-domain, general-purpose database querying dialogue systems . they use a Wizard-of-Oz collection of 3k turns plus 10k+ annotated SQL queries .
Outcome:	The proposed corpus is based on a Wizard-of-Oz dataset of 3k dialogues querying 200 complex DBs spanning 138 domains.

Turn-Level Active Learning for Dialogue State Tracking (2023.emnlp-main)

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Zihan Zhang, Meng Fang, Fanghua Ye, Ling Chen, Mohammad-Reza Namazi-Rad

Responsible & Trustworthy NLP

Challenge:	Existing approaches to annotate dialogues require supervised training, which requires human workers to manually annotates dialogues.
Approach:	They propose a turn-level active learning framework to actively select dialogue turns to annotate . their approach can achieve comparable performance to traditional training approaches .
Outcome:	The proposed model achieves comparable performance to existing training approaches with significantly less annotated data.

Let the Expert Stick to His Last: Expert-Specialized Fine-Tuning for Sparse Architectural Large Language Models (2024.emnlp-main)

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Zihan Wang, Deli Chen, Damai Dai, Runxin Xu, Zhuoshu Li, Yu Wu

iPET: An Interactive Emotional Companion Dialogue System with LLM-Powered Virtual Pet World Simulation (2025.acl-demo)

Challenge:	Existing studies on parameter-efficient fine-tuning (PEFT) for dense-architecture LLMs are lacking.
Approach:	They propose an expert-specialized fine-tuning method that tunes the experts most relevant to downstream tasks while freezing the other experts.
Outcome:	The proposed method matches or surpasses full-parameter fine-tuning.

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Zheyong Xie, Shaosheng Cao, Zuozhu Liu, Zheyu Ye, Zihan Niu, Chonggang Lu, Tong Xu, Enhong Chen, Zhe Xu, Yao Hu, Wei Lu

Dialogue Systems & Conversational Agents

Challenge:	Existing approaches to role-playing emotional companion products lack sustained personalization and contextual adaptability, limiting their effectiveness in real-world settings.
Approach:	They propose a virtual pet agent that can enhance user engagement through rich, dynamic pet behaviors and interactions tailored to individual preferences.
Outcome:	The proposed system has been deployed in a real-world, non-commercial product for 200 days and has demonstrated its effectiveness in practical applications.

The Paradox of Outcome Optimization: A Causal Information-Theoretic Bound on Reasoning Shortcuts in LLMs (2026.acl-long)

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Zihan Chen, Yiming Zhang, Wenxiang Geng, Zenghui Ding, Yining Sun

LLM×MapReduce: Simplified Long-Sequence Processing using Large Language Models (2025.acl-long)

Challenge:	Large Language Models (LLMs) aligned via outcome-based Reinforcement Learning (RL) exhibit a critical failure mode: they exhibit brittle reasoning capabilities on out-of-distribution tasks.
Approach:	They propose a framework bridging Structural Causal Models and the Information Bottleneck principle to explain this paradox.
Outcome:	The proposed framework bridges the framework between SCM and IB principles to explain the problem.

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Zihan Zhou, Chong Li, Xinyi Chen, Shuo Wang, Yu Chao, Zhili Li, Haoyu Wang, Qi Shi, Zhixing Tan, Xu Han, Xiaodong Shi, Zhiyuan Liu, Maosong Sun

Unsupervised Speech-text word-level alignment with Dynamic Programming (2025.findings-naacl)

Challenge:	Existing studies have focused on extending the context length of large language models (LLMs) due to their quadratic computational complexity and a lack of high-quality long training examples, most LLMs are trained with a limited window size.
Approach:	They propose a training-free framework that enables large language models to effectively process long texts using a divide-and-conquer strategy for comprehensive document understanding.
Outcome:	The proposed framework outperforms open-source and commercial long-context LLMs and is compatible with several models.

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Tianshu Yu, Zihan Gong, Minghuan Tan, Guhong Chen, Min Yang