Papers by Yixin Zhang

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

Challenge:	Recent advances in Large Language Models (LLMs) have transformed the paradigm in ocean science.
Approach:	They propose a framework to automatically obtain large volume of ocean domain instruction data, which generates instructions based on multi-agent collaboration.
Outcome:	The proposed framework shows a higher level of knowledge expertise for ocean science tasks and gains preliminary embodied intelligence capabilities in ocean technology.

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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.

FOLIO: Natural Language Reasoning with First-Order Logic (2024.emnlp-main)

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Simeng Han, Hailey Schoelkopf, Yilun Zhao, Zhenting Qi, Martin Riddell, Wenfei Zhou, James Coady, David Peng, Yujie Qiao, Luke Benson, Lucy Sun, Alexander Wardle-Solano, Hannah Szabó, Ekaterina Zubova, Matthew Burtell, Jonathan Fan, Yixin Liu, Brian Wong, Malcolm Sailor, Ansong Ni, Linyong Nan, Jungo Kasai, Tao Yu, Rui Zhang, Alexander Fabbri, Wojciech Kryscinski, Semih Yavuz, Ye Liu, Xi Lin, Shafiq Joty, Yingbo Zhou, Caiming Xiong, Rex Ying, Arman Cohan, Dragomir Radev

Retrieval and Reasoning on KGs: Integrate Knowledge Graphs into Large Language Models for Complex Question Answering (2024.findings-emnlp)

Challenge:	Existing benchmarks for logical reasoning in large language models lack language naturalness or limited complexity.
Approach:	They propose to use first-order logic annotations to evaluate logical reasoning capabilities of large language models.
Outcome:	The proposed dataset evaluates the FOL reasoning ability of supervised fine-tuning on medium-sized language models.

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Yixin Ji, Kaixin Wu, Juntao Li, Wei Chen, Mingjie Zhong, Xu Jia, Min Zhang

The Death and Life of Great Prompts: Analyzing the Evolution of LLM Prompts from the Structural Perspective (2024.emnlp-main)

Challenge:	Large Language Models (LLMs) have performed impressively in various NLP tasks, but their inherent hallucination phenomena severely challenge their credibility in complex reasoning.
Approach:	They propose to integrate explainable Knowledge Graphs (KGs) with LLMs to alleviate hallucinations . they construct subgraphs to enhance the retrieval capabilities of KGs via CoT reasoning.
Outcome:	Extensive experiments on two KGQA datasets show that the proposed model achieves convincing performance compared to strong baselines.

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Yihan Ma, Xinyue Shen, Yixin Wu, Boyang Zhang, Michael Backes, Yang Zhang

PRISM: Probabilistic Reward Model with Inherent Structural Modeling (2026.acl-long)

Challenge:	Recent research has shown that high-quality prompts are essential for LLMs to produce accurate and relevant responses.
Approach:	They analyze 10,538 in-the-wild prompts collected from various platforms and develop a framework that decomposes the prompts into eight key components.
Outcome:	The proposed framework decomposes 10,538 in-the-wild prompts into eight components.

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Yuhang Zhou, Yixin Cao, Yuchen Ni, Shihan Dou, Xutian Chen, Ge Zhang, Xiang Liu, Guangnan Ye

Cognitive Modeling

Challenge:	Existing evaluators compress diverse human judgments into a single scalar, leading to brittle alignment and reward hacking.
Approach:	They propose a Gaussian-based reinterpretation of reward evaluation as a conditional distribution and a mixture of Gaussians to capture conflicting preference dimensions.
Outcome:	The proposed model outperforms scalar baselines in accuracy and generalization.

Quantifying and Mitigating Unimodal Biases in Multimodal Large Language Models: A Causal Perspective (2024.findings-emnlp)

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Meiqi Chen, Yixin Cao, Yan Zhang, Chaochao Lu

A Needle in a Haystack: An Analysis of High-Agreement Workers on MTurk for Summarization (2023.acl-long)

Challenge:	Recent advances in Large Language Models have facilitated the development of Multimodal LLMs.
Approach:	They propose a causal framework to interpret unimodal biases in visual question answering problems and a framework to integrate information from different modalities and mitigate biase.
Outcome:	The proposed framework analyzes visual question answering (VQA) problems to assess their impact on predictions.

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Lining Zhang, Simon Mille, Yufang Hou, Daniel Deutsch, Elizabeth Clark, Yixin Liu, Saad Mahamood, Sebastian Gehrmann, Miruna Clinciu, Khyathi Raghavi Chandu, João Sedoc

Early Exit with Disentangled Representation and Equiangular Tight Frame (2023.findings-acl)

Challenge:	Using crowdsourcing, it is difficult to obtain high-quality annotations for difficult tasks.
Approach:	They propose a recruitment pipeline to recruit high-quality Amazon Mechanical Turk workers . they filter out subpar workers before they carry out the evaluations .
Outcome:	The proposed method can filter out subpar workers before they carry out evaluations and obtain high-agreement annotations with similar constraints on resources.

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Yixin Ji, Jikai Wang, Juntao Li, Qiang Chen, Wenliang Chen, Min Zhang

Representation Learning

Challenge:	Existing early exit paradigm relies on training parametrical internal classifiers to complete specific tasks.
Approach:	They propose a method to decouple two distinct types of representation and introduce a non-parametric tight frame classifier for improvement.
Outcome:	Experiments on monolingual and multilingual tasks show that the proposed method improves over existing methods.

MAB-DQA: Addressing Query Aspect Importance in Document Question Answering with Multi-Armed Bandits (2026.acl-long)

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Yixin Xiang, Yunshan Ma, Xiaoyu Du, Yibing Chen, Yanxin Zhang, Jinhui Tang

CaMML: Context-Aware Multimodal Learner for Large Models (2024.acl-long)

Challenge:	Document Question Answering (DQA) requires interpreting visual layouts, which has prompted recent studies to adopt multimodal Retrieval-Augmented Generation (RAG) that processes page images for answer generation.
Approach:	They propose a multi-armed bandit-based DQA framework that explicitly models the varying importance of multiple implicit aspects in a query.
Outcome:	The proposed framework shows an improvement of 5%-18% over the state-of-the-art method on four benchmarks.

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Yixin Chen, Shuai Zhang, Boran Han, Tong He, Bo Li

Multi-Programming Language Sandbox for LLMs (2025.acl-demo)

Challenge:	a lightweight module for tuning large multimodal models is introduced . CaMML integrates contextual samples into large models, enabling them to make inferences .
Approach:	They introduce a lightweight module for tuning large multimodal models . they have developed two models that have shown exceptional performance .
Outcome:	The proposed model outperforms LLaVA-1.5 on ten widely recognized datasets with a noticeable margin.

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Shihan Dou, Jiazheng Zhang, Jianxiang Zang, Yunbo Tao, Weikang Zhou, Haoxiang Jia, Shichun Liu, Yuming Yang, Shenxi Wu, Zhiheng Xi, Muling Wu, Rui Zheng, Changze Lv, Limao Xiong, Shaoqing Zhang, Lin Zhang, Wenyu Zhan, Rongxiang Weng, Jingang Wang, Xunliang Cai, Yueming Wu, Ming Wen, Yixin Cao, Tao Gui, Xipeng Qiu, Qi Zhang, Xuanjing Huang

DUAL RM: Beyond Rule-based Preference Reward Modeling via Meta-Reward (2026.acl-long)

Challenge:	MPLSandbox is an out-of-the-box multi-programming language sandbox designed to provide unified and comprehensive feedback from compiler and analysis tools for Large Language Models (LLMs).
Approach:	They propose a multi-programming language sandbox that provides unified feedback from compilers and analysis tools for Large Language Models.
Outcome:	The proposed multi-language sandbox can provide comprehensive feedback from compilers and analysis tools for large language models (LLMs).

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Xiaobo Liang, Wanfu Wang, Qipeng Huang, Yuyang Ding, Zecheng Tang, Yixin Ji, Qianben Chen, Zhe Zhao, Kehai Chen, Juntao Li, Min Zhang

Challenge:	Existing preference-based reward modeling methods face a recursive dependency where each verifier requires a meta-verifier, leading to continuous and costly dependence on human annotation.
Approach:	They propose a dual RM that couples discriminative and generative reward models under a non-parametric meta-reward.
Outcome:	The proposed model achieves strong performance across major preference benchmarks and even when trained exclusively on language modality, it exhibits robust cross-modal transfer on Omni-RewardBench.

Investigating Transfer Learning in Multilingual Pre-trained Language Models through Chinese Natural Language Inference (2021.findings-acl)

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Hai Hu, He Zhou, Zuoyu Tian, Yiwen Zhang, Yina Patterson, Yanting Li, Yixin Nie, Kyle Richardson

Multilinguality

Challenge:	Multilingual transformers have been shown to have remarkable transfer skills in zero-shot settings.
Approach:	They investigate cross-lingual transfer abilities of XLM-R for Chinese and English natural language inference using a large scale Chinese dataset.
Outcome:	The proposed model trains on Chinese and English natural language inference datasets.

DialCoT Meets PPO: Decomposing and Exploring Reasoning Paths in Smaller Language Models (2023.emnlp-main)

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Chengcheng Han, Xiaowei Du, Che Zhang, Yixin Lian, Xiang Li, Ming Gao, Baoyuan Wang

Dialogue Systems & Conversational Agents

Challenge:	Chain-of-Thought prompting has improved the reasoning capabilities of Large Language Models (LLMs) but it is ineffective or detrimental to the performance on reasoning tasks in Smaller Language Model (SLMs) with less than 10 billion parameters.
Approach:	They propose a Dialogue-guided Chain-of-Thought method to improve the reasoning capabilities of Large Language Models (LLMs) by generating intermediate reasoning steps in a dialogue format to guide the model to the final answer.
Outcome:	The proposed method can achieve significant performance gains over state-of-the-art competitors on four arithmetic reasoning datasets.

Are Missing Links Predictable? An Inferential Benchmark for Knowledge Graph Completion (2021.acl-long)

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Yixin Cao, Xiang Ji, Xin Lv, Juanzi Li, Yonggang Wen, Hanwang Zhang

Knowledge Representation

Challenge:	Existing benchmarks for Knowledge Graph Completion (KGC) are unsatisfactory .
Approach:	They propose to use rule-guided train/test generation instead of conventional random split to ensure that each testing sample is predictable with supportive data in the training set.
Outcome:	The proposed model improves on existing benchmarks in inferential ability, assumptions, and patterns.

LogicPro: Improving Complex Logical Reasoning via Program-Guided Learning (2025.acl-long)

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Jin Jiang, Yuchen Yan, Yang Liu, Jianing Wang, Shuai Peng, Xunliang Cai, Yixin Cao, Mengdi Zhang, Liangcai Gao

Searching for Best Practices in Retrieval-Augmented Generation (2024.emnlp-main)

Challenge:	LogicPro is a data synthesis method that uses LeetCode-style algorithm problems and their corresponding Program solutions to generate complex logic data.
Approach:	They propose a new method which leverages LeetCode-style algorithm Problems and their corresponding Program solutions to synthesize complex logic data in text format.
Outcome:	The proposed method outperforms existing models for BBH27, LogicBench, DROP, AR-LSAT, and GSM8K, and a wide range of reasoning datasets.

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Xiaohua Wang, Zhenghua Wang, Xuan Gao, Feiran Zhang, Yixin Wu, Zhibo Xu, Tianyuan Shi, Zhengyuan Wang, Shizheng Li, Qi Qian, Ruicheng Yin, Changze Lv, Xiaoqing Zheng, Xuanjing Huang

Information Retrieval

Challenge:	Retrieval-augmented generation (RAG) techniques have proven to be effective in integrating up-to-date information, mitigating hallucinations, and enhancing response quality, especially in specialized domains.
Approach:	They propose several strategies for deploying RAG that balance performance and efficiency.
Outcome:	The proposed approaches can significantly enhance question-answering capabilities and accelerate the generation of multimodal content using a “retrieval as generation” strategy.

Code over Words: Overcoming Semantic Inertia via Code-Grounded Reasoning (2026.findings-acl)

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Manjie Xu, Isabella Yin, Xinyi Tu, Chi Zhang, Yixin Zhu

Adaptive Feature-based Low-Rank Compression of Large Language Models via Bayesian Optimization (2024.findings-emnlp)

Challenge:	Large Language Models struggle with semantic inertia, a problem that is often attributed to natural language encoding, which entangles descriptive semantics and logical rules, leading to persistent hallucinations of familiar physics despite explicit contradictory rules.
Approach:	They propose a framework that decouples logical dynamics from visual priors via amortized theory induction and counterfactual contrastive alignment.
Outcome:	The proposed framework outperforms expensive inference-time search methods in both efficiency and accuracy.

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Yixin Ji, Yang Xiang, Juntao Li, Qingrong Xia, Zi Ye, Xinyu Duan, Zhefeng Wang, Kehai Chen, Min Zhang

When Is Thinking Enough? Early Exit via Sufficiency Assessment for Efficient Reasoning (2026.acl-long)

Challenge:	Large language models require a balance between efficiency and performance.
Approach:	They propose a low-rank compression technique that reduces non-essential parameters by decomposing weight matrices into products of two low-ranked matrici.
Outcome:	The proposed method outperforms existing pruning and low-rank compression techniques in maintaining model performance at the same compression ratio.

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Yang Xiang, Yixin Ji, Ruotao Xu, Dan Qiao, Zheming Yang, Juntao Li, Min Zhang

Reasoning with Language Model Prompting: A Survey (2023.acl-long)

Challenge:	Existing approaches to early exit reasoning often rely on handcrafted or empirical indicators that are unreliable and impractical.
Approach:	They propose a framework that allows LRMs to assess the sufficiency of its chain-of-thought and determine the optimal point for early exit.
Outcome:	The proposed framework reduces reasoning length by 28.9%–34.9% with minimal performance loss, effectively mitigating overthinking.

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Shuofei Qiao, Yixin Ou, Ningyu Zhang, Xiang Chen, Yunzhi Yao, Shumin Deng, Chuanqi Tan, Fei Huang, Huajun Chen

Demonstration Augmentation for Zero-shot In-context Learning (2024.findings-acl)

Challenge:	Reasoning is an essential ability for complex problem-solving and can provide back-end support for various real-world applications.
Approach:	They present cutting-edge research on reasoning with language model prompting and provide systematic resources to help beginners.
Outcome:	The proposed approaches have not been systematically reviewed and analyzed.

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Yi Su, Yunpeng Tai, Yixin Ji, Juntao Li, Yan Bowen, Min Zhang

Low-Resource NLP

Challenge:	Large Language Models (LLMs) have demonstrated an impressive capability known as In-context Learning (ICL), which enables them to acquire knowledge from textual demonstrations without the need for parameter updates.
Approach:	They propose to use model’s previously predicted historical samples as demonstrations for subsequent ones to improve model’ s performance.
Outcome:	The proposed method significantly outperforms the previous method and its predecessors in terms of inference cost and time.

Disentangling Language and Culture for Evaluating Multilingual Large Language Models (2025.acl-long)

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Jiahao Ying, Wei Tang, Yiran Zhao, Yixin Cao, Yu Rong, Wenxuan Zhang

Beware of Model Collapse! Fast and Stable Test-time Adaptation for Robust Question Answering (2023.emnlp-main)

Challenge:	Extensive evaluations of large language models (LLMs) are conducted on a wide range of models, revealing a notable cultural-linguistic synergy phenomenon, where models exhibit better performance when questions are culturally aligned with the language.
Approach:	They propose a Dual Evaluation Framework to comprehensively assess the multilingual capabilities of large language models by decomposing evaluation along dimensions of linguistic medium and cultural context.
Outcome:	The proposed framework allows for a nuanced analysis of LLMs’ ability to process questions within both native and cross-cultural contexts cross-lingually.

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Yi Su, Yixin Ji, Juntao Li, Hai Ye, Min Zhang

Robustness in NLP

Challenge:	Pre-trained language models (PLMs) have achieved great success in question answering, but their robustness is insufficient to support their practical applications.
Approach:	They propose a method which regularizes the model's output and an efficient side block to reduce its inference time.
Outcome:	The proposed method achieves comparable or better results than previous TTA methods at a speed close to vanilla forward propagation, which is 1.8 to 4.4 speedup compared to previous methods.

InferPilot: Autonomous Inference Attacks Against ML Services With LLM-Based Agents (2026.findings-acl)

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Yixin Wu, Rui Wen, Chi Cui, Michael Backes, Yang Zhang

Robustness in NLP

Challenge:	Inference attacks are important for assessing model's robustness, but their implementation and parameters are challenging for non-experts.
Approach:	They propose an autonomous agent capable of conducting inference attacks without human intervention.
Outcome:	The proposed agent achieves a 100.0% task completion rate and near-expert attack performance with an average token cost of only 0.627 per run.

Fair Abstractive Summarization of Diverse Perspectives (2024.naacl-long)

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Yusen Zhang, Nan Zhang, Yixin Liu, Alexander Fabbri, Junru Liu, Ryo Kamoi, Xiaoxin Lu, Caiming Xiong, Jieyu Zhao, Dragomir Radev, Kathleen McKeown, Rui Zhang

G-HiRel: Enhancing the Adaption to Knowledge Updating for Large Language Model Reasoning (2026.findings-acl)

Challenge:	Existing work on summarization metrics and large language models has not explored fair abstractive summarizing.
Approach:	They propose four reference-free automatic metrics to measure the differences between target and source perspectives.
Outcome:	The proposed methods alleviate fair abstractive summarization on user-generated data.

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Yudai Pan, Jiajie Hong, Tianzhe Zhao, Lingyun Song, Lingling Zhang, Yixin Chen, Xuequn Shang

How Do LLMs Acquire New Knowledge? A Knowledge Circuits Perspective on Continual Pre-Training (2025.findings-acl)

Challenge:	Large language models (LLMs) have good performance in multiple reasoning tasks, but are limited to adapt the rapid knowledge updates in the real-world scenario.
Approach:	They propose an LLM reasoning framework with hierarchical relational retrieval for large-scale knowledge updating, named G-HiRel.
Outcome:	The proposed framework achieves superiority in terms of accuracy and interpretability on three benchmarks.

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Yixin Ou, Yunzhi Yao, Ningyu Zhang, Hui Jin, Jiacheng Sun, Shumin Deng, Zhenguo Li, Huajun Chen

CriticLean: Critic-Guided Reinforcement Learning for Mathematical Formalization (2026.acl-long)

Challenge:	Large Language Models (LLMs) have exceptional capabilities in knowledge-intensive tasks . however, they struggle with knowledge updates due to dynamic nature of world knowledge .
Approach:	They propose to identify computational subgraphs that facilitate knowledge storage and processing . they also identify a phase shift from formation to optimization in LLMs .
Outcome:	The proposed model can capture factual knowledge from pre-training corpus and encapsulate it as extensive parametric knowledge.

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Zhongyuan Peng, Yifan Yao, Kaijing Ma, Shuyue Guo, Yizhe Li, Yichi Zhang, Chenchen Zhang, Yifan Zhang, Zhouliang Yu, Luming Li, Minghao Liu, Yihang Xia, Jiawei Shen, Yuchen Wu, Yixin Cao, Zhaoxiang Zhang, Wenhao Huang, Jiaheng Liu, Ge Zhang

ERGO: Event Relational Graph Transformer for Document-level Event Causality Identification (2022.coling-1)

Challenge:	Existing approaches to formalizing mathematical statements face limitations in accuracy, especially in the context of complex, highlevel problems that involve sophisticated mathematical reasoning.
Approach:	They propose a CriticLean framework that elevates the role of the critic from a passive validator to an active learning component and introduce a benchmark to measure models’ ability to distinguish semantically correct from incorrect formalizations.
Outcome:	The proposed framework outperforms open- and closed-source benchmarks and shows that it significantly outperformed existing models.

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Meiqi Chen, Yixin Cao, Kunquan Deng, Mukai Li, Kun Wang, Jing Shao, Yan Zhang

Information Extraction & Text Mining

Challenge:	Existing methods to identify event-event causal relations in a document are noisy and require heuristic rules or external tools.
Approach:	They propose a document-level event-event causality identification framework that uses heuristic rules to design edges between events.
Outcome:	The proposed framework outperforms existing state-of-the-art methods on two benchmark datasets.

On the Role of Summary Content Units in Text Summarization Evaluation (2024.naacl-short)

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Marcel Nawrath, Agnieszka Nowak, Tristan Ratz, Danilo Walenta, Juri Opitz, Leonardo Ribeiro, João Sedoc, Daniel Deutsch, Simon Mille, Yixin Liu, Sebastian Gehrmann, Lining Zhang, Saad Mahamood, Miruna Clinciu, Khyathi Chandu, Yufang Hou

Summarization

Challenge:	a human written summary content unit (SCU) is used to judge the quality of a summary . a pyramid evaluation method is based on SCUs that decompose a reference summary into concise sentences .
Approach:	They propose to use automated SCUs to evaluate the quality of a candidate summary . they propose to generate SCU approximations from AMR meaning representations and large language models .
Outcome:	The proposed method can be fully automated, but lacks the human effort to validate it.

GEMv2: Multilingual NLG Benchmarking in a Single Line of Code (2022.emnlp-demos)

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Sebastian Gehrmann, Abhik Bhattacharjee, Abinaya Mahendiran, Alex Wang, Alexandros Papangelis, Aman Madaan, Angelina Mcmillan-major, Anna Shvets, Ashish Upadhyay, Bernd Bohnet, Bingsheng Yao, Bryan Wilie, Chandra Bhagavatula, Chaobin You, Craig Thomson, Cristina Garbacea, Dakuo Wang, Daniel Deutsch, Deyi Xiong, Di Jin, Dimitra Gkatzia, Dragomir Radev, Elizabeth Clark, Esin Durmus, Faisal Ladhak, Filip Ginter, Genta Indra Winata, Hendrik Strobelt, Hiroaki Hayashi, Jekaterina Novikova, Jenna Kanerva, Jenny Chim, Jiawei Zhou, Jordan Clive, Joshua Maynez, João Sedoc, Juraj Juraska, Kaustubh Dhole, Khyathi Raghavi Chandu, Laura Perez Beltrachini, Leonardo F . R. Ribeiro, Lewis Tunstall, Li Zhang, Mahim Pushkarna, Mathias Creutz, Michael White, Mihir Sanjay Kale, Moussa Kamal Eddine, Nico Daheim, Nishant Subramani, Ondrej Dusek, Paul Pu Liang, Pawan Sasanka Ammanamanchi, Qi Zhu, Ratish Puduppully, Reno Kriz, Rifat Shahriyar, Ronald Cardenas, Saad Mahamood, Salomey Osei, Samuel Cahyawijaya, Sanja Štajner, Sebastien Montella, Shailza Jolly, Simon Mille, Tahmid Hasan, Tianhao Shen, Tosin Adewumi, Vikas Raunak, Vipul Raheja, Vitaly Nikolaev, Vivian Tsai, Yacine Jernite, Ying Xu, Yisi Sang, Yixin Liu, Yufang Hou

Recognizing Everything from All Modalities at Once: Grounded Multimodal Universal Information Extraction (2024.findings-acl)

Challenge:	Evaluations in machine learning rarely use the latest metrics, datasets, or human evaluation in favor of remaining compatible with prior work.
Approach:	They propose to use the Generation, Evaluation, and Metrics Benchmark to integrate new evaluation methods into existing evaluations.
Outcome:	The proposed evaluation infrastructure bridges the gap between the advantages of leaderboards and in-depth and evolving evaluations by allowing model developers to benefit from each other's work.

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Meishan Zhang, Hao Fei, Bin Wang, Shengqiong Wu, Yixin Cao, Fei Li, Min Zhang

Information Extraction & Text Mining

Challenge:	Existing studies on IE tasks have focused on recognizing and analyzing cross-modal information . a multimodal large language model (MLLM) is developed to analyze IE across modalities .
Approach:	They propose a multimodal large language model (MLLM) capable of grounding information from all modalities.
Outcome:	The proposed framework provides a framework to analyze IE tasks over various modalities and their fine-grained groundings.

Is Multi-Hop Reasoning Really Explainable? Towards Benchmarking Reasoning Interpretability (2021.emnlp-main)

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Xin Lv, Yixin Cao, Lei Hou, Juanzi Li, Zhiyuan Liu, Yichi Zhang, Zelin Dai

Peering Behind the Shield: Guardrail Identification in Large Language Models (2026.findings-acl)

Challenge:	Existing models for multi-hop reasoning are not able to evaluate their interpretability . a recent study found that many paths are unreasonable .
Approach:	They propose a framework to evaluate the interpretability of multi-hop reasoning models . they annotate all possible rules and establish a benchmark .
Outcome:	The proposed framework outperforms existing models in terms of performance and interpretability.

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Ziqing Yang, Yixin Wu, Rui Wen, Michael Backes, Yang Zhang

CHEER: Centrality-aware High-order Event Reasoning Network for Document-level Event Causality Identification (2023.acl-long)

Challenge:	Identifying guardrails in conversational AI agents is critical for identifying malicious content . identifying guardrail components in black-box AI agents poses security challenges .
Approach:	They propose a method that leverages guard-specific adversarial prompts to detect guardrails in black-box AI agents.
Outcome:	The proposed method achieves perfect classification accuracy in multiple scenarios.

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Meiqi Chen, Yixin Cao, Yan Zhang, Zhiwei Liu

LLM-as-a-Coauthor: Can Mixed Human-Written and Machine-Generated Text Be Detected? (2024.findings-naacl)

Challenge:	Recent studies focus on building a document-level graph for cross-sentence reasoning, but ignore important causal structures.
Approach:	They propose a document-level event causality identification model which annotates central events and incorporates event centrality information into the reasoning network.
Outcome:	The proposed model performs high-order reasoning while considering event centrality.

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Qihui Zhang, Chujie Gao, Dongping Chen, Yue Huang, Yixin Huang, Zhenyang Sun, Shilin Zhang, Weiye Li, Zhengyan Fu, Yao Wan, Lichao Sun

Isotropic Representation Can Improve Zero-Shot Cross-Lingual Transfer on Multilingual Language Models (2023.findings-emnlp)

Challenge:	Current research focuses on purely MGT detection without adequately addressing mixed scenarios including AI-revised Human-Written Text (HWT) and human-revealed MGT.
Approach:	They define mixtext, a form of mixed text involving both AI and human-generated content, and then use a MixSet dataset to assess their effectiveness.
Outcome:	The proposed detectors struggle to identify mixtext, particularly in dealing with subtle modifications and style adaptability.

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Yixin Ji, Jikai Wang, Juntao Li, Hai Ye, Min Zhang

Improving the Adversarial Robustness of NLP Models by Information Bottleneck (2022.findings-acl)

Challenge:	Existing mPLMs can align representations well for myriads of cross-lingual transfer tasks.
Approach:	They propose enhanced isotropy and constrained code-switching for zero-shot cross-lingual transfer to alleviate the problem of misalignment caused by anisotropic representations.
Outcome:	The proposed method improves on three zero-shot cross-lingual transfer tasks and over existing methods.

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Cenyuan Zhang, Xiang Zhou, Yixin Wan, Xiaoqing Zheng, Kai-Wei Chang, Cho-Jui Hsieh

Robustness in NLP

Challenge:	Existing studies have shown that adversarial examples can be directly attributed to the presence of non-robust features.
Approach:	They propose to capture task-specific robust features while eliminating non-robust ones . they show that models can achieve significant improvement in robust accuracy .
Outcome:	The proposed method outperforms all defense methods on SST-2, AGNEWS and IMDB datasets while achieving no performance drop.

EasyInstruct: An Easy-to-use Instruction Processing Framework for Large Language Models (2024.acl-demos)

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Yixin Ou, Ningyu Zhang, Honghao Gui, Ziwen Xu, Shuofei Qiao, Runnan Fang, Lei Li, Zhen Bi, Guozhou Zheng, Huajun Chen

Rethinking Assessments of Prompt Injection Attacks (2026.findings-acl)

Challenge:	Large Language Models (LLMs) have improved performance across tasks and domains . instruction tuning is a crucial technique to enhance the capabilities of LLMs - but there is no standard open-source instruction processing framework available for the community .
Approach:	They propose an open-source instruction tuning framework for Large Language Models that modularizes instruction generation, selection, prompting and their combination and interaction.
Outcome:	The proposed framework is open-source and available on Github.

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Chi Cui, Yixin Wu, Michael Backes, Yang Zhang

IPL: Leveraging Multimodal Large Language Models for Intelligent Product Listing (2024.emnlp-industry)

Challenge:	Prompt injection attacks are recognized as one of the primary risks faced by LLM-integrated applications in recent years.
Approach:	They evaluate prompt injection attacks on LLM-integrated applications across 37 target tasks, 185 injected tasks, 21 attack instructions, and 143,745 queries.
Outcome:	The proposed framework provides a solid foundation for assessing vulnerabilities in LLM-integrated applications and evaluating the efficacy of defensive strategies.

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Kang Chen, Qing Zhang, Chengbao Lian, Yixin Ji, Xuwei Liu, Shuguang Han, Guoqiang Wu, Fei Huang, Jufeng Chen

Robust Prompt Optimization for Large Language Models Against Distribution Shifts (2023.emnlp-main)

Challenge:	Unlike professional Business-to-Consumer (B2C) e-commerce platforms, consumer-to consumer (C2C), is mainly targeting individual sellers.
Approach:	They develop an intelligent product listing tool that generates product descriptions using various product attributes such as category, brand, color, condition, etc.
Outcome:	The proposed tool outperforms the base model in domain-specific tasks while producing less hallucination.

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Moxin Li, Wenjie Wang, Fuli Feng, Yixin Cao, Jizhi Zhang, Tat-Seng Chua

Com2 : A Causal-Guided Benchmark for Exploring Complex Commonsense Reasoning in Large Language Models (2025.acl-long)

Challenge:	Existing research has explored automatic prompt optimization methods to eliminate manual effort in identifying effective prompts for a given task.
Approach:	They propose a framework for prompt optimization that can be generalized to an unlabeled target group.
Outcome:	The proposed framework improves on target group and source group while generalizing to unlabeled target group.

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Kai Xiong, Xiao Ding, Yixin Cao, Yuxiong Yan, Li Du, Yufei Zhang, Jinglong Gao, Jiaqian Liu, Bing Qin, Ting Liu

Analyzing Temporal Complex Events with Large Language Models? A Benchmark towards Temporal, Long Context Understanding (2024.acl-long)

Challenge:	Existing works focus on complex tasks like math and code, while complex commonsense reasoning remains underexplored due to its uncertainty and lack of structure.
Approach:	They propose to build a benchmark for large language models based on complex commonsense reasoning based upon causal event graphs and causal theory.
Outcome:	The proposed benchmark combines a complex commonsense reasoning benchmark with a detective story to achieve a more challenging subset.

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Zhihan Zhang, Yixin Cao, Chenchen Ye, Yunshan Ma, Lizi Liao, Tat-Seng Chua

VIB-Probe: Detecting and Mitigating Hallucinations in Vision-Language Models via Variational Information Bottleneck (2026.acl-long)

Challenge:	Existing research in complex event analysis has made significant strides but is constrained by inadequate natural language processing techniques.
Approach:	They propose a novel approach using Large Language Models to extract and analyze the event chain within TCE, characterized by their key points and timestamps.
Outcome:	The proposed model performs comparable to models with long context window and retrieval-augmented generation method in three distinct tasks .

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Feiran Zhang, Yixin Wu, Zhenghua Wang, Xiaohua Wang, Changze Lv, Xuanjing Huang, Xiaoqing Zheng

MoPrune: Scene-Guided Motion-Aware Token Pruning for Efficient Video Large Language Models (2026.findings-acl)

Challenge:	Existing hallucination detection methods rely on external verification tools . however, entanglement of visual-linguistic syntax and noise makes it difficult to detect hallucis .
Approach:	They propose a hallucination detection framework that leverages the Variational Information Bottleneck theory to detect hallucinic heads and to infer hallucication mitigation strategies.
Outcome:	The proposed framework outperforms baselines in hallucinations and noise detection environments.

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Wenhao Hong, Ziyang Wang, Yixin Zhang, Zilei Wang

Improving Large Language Models in Event Relation Logical Prediction (2024.acl-long)

Challenge:	Prior approaches to token pruning ignore video dynamics and the fact that different scenes exhibit different redundancy patterns.
Approach:	They propose a token pruning framework that is train-free and scene-guided to accelerate VideoLLMs by removing redundant visual information from video frames.
Outcome:	MoPrune is a training-free, scene-guided and motion-centric token pruning framework for accelerating VideoLLMs.

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Meiqi Chen, Yubo Ma, Kaitao Song, Yixin Cao, Yan Zhang, Dongsheng Li

Mitigating Hallucinations in VLMs: Enhancing Visual Attention via Head-Wise Perturbation (2026.findings-acl)

Challenge:	Event relation extraction tasks require rigorous logical reasoning and semantic comprehension, a challenge for narrative understanding and reasoning.
Approach:	They propose three approaches to endow LLMs with event relation logic to generate more coherent answers across different scenarios.
Outcome:	The proposed approach improves on a set of ERE tasks and provides insights for future work.

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Zhenghua Wang, Yixin Wu, Feiran Zhang, Qi Qian, Changze Lv, Xuanjing Huang, Xiaoqing Zheng

Isotropy-Enhanced Conditional Masked Language Models (2023.findings-emnlp)

Challenge:	Vision–Language Models (VLMs) have demonstrated strong capabilities in tasks that require joint understanding of text and images.
Approach:	They propose a strategy that incorporates head-wise attention perturbation via continuous multiplicative noise coupled with a visual-guided loss focused on vision-sensitive text tokens to promote a more balanced attention distribution.
Outcome:	The proposed approach outperforms baseline models on three benchmarks and consistently outperformed the baseline model.

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Pei Guo, Yisheng Xiao, Juntao Li, Yixin Ji, Min Zhang

Causal Inference for Human-Language Model Collaboration (2024.naacl-long)

Challenge:	Existing non-autoregressive models with auto-regressing decoding paradigms have been used for various text generation tasks to accelerate inference but at the cost of generation quality to some extent.
Approach:	They propose to use Look Neighbors strategy to enhance learning of target token representations during training to achieve a good balance between inference speedup and generation quality.
Outcome:	The proposed models outperform current models on 4 WMT datasets and outperformed the current SoTA results.

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Bohan Zhang, Yixin Wang, Paramveer Dhillon

Are LLMs Rational Investors? A Study on the Financial Bias in LLMs (2025.findings-acl)

Challenge:	In this paper, we examine the collaborative dynamics between humans and language models where the interaction involves LMs proposing text segments and humans editing or responding to these segments.
Approach:	They propose a causal estimand to estimate the incremental stylistic effect (ISE) of various interaction strategies in dynamic human-LM collaborations.
Outcome:	The proposed estimand reduces confounding and significantly improves counterfactual estimation over a set of competitive baselines.

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Yuhang Zhou, Yuchen Ni, Zhiheng Xi, Zhangyue Yin, Yu He, Gan Yunhui, Xiang Liu, Zhang Jian, Sen Liu, Xipeng Qiu, Yixin Cao, Guangnan Ye, Hongfeng Chai

EvoWiki: Evaluating LLMs on Evolving Knowledge (2025.acl-long)

Challenge:	Existing studies on biases within specific domains, such as finance, remain limited.
Approach:	They propose a framework to detect, detect, analyze and mitigate financial biases in large language models.
Outcome:	The proposed framework reduces bias by 68% for the most biased model, according to key metrics.

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Wei Tang, Yixin Cao, Yang Deng, Jiahao Ying, Bo Wang, Yizhe Yang, Yuyue Zhao, Qi Zhang, Xuanjing Huang, Yu-Gang Jiang, Yong Liao

Challenge:	Existing knowledge evolution benchmarks are static and fail to capture the evolving nature of LLMs and knowledge.
Approach:	They propose an evolving dataset that categorizes information into stable, evolved, and uncharted states.
Outcome:	The proposed dataset is auto-updatable and enables evaluation of continuously changing knowledge and newly released LLMs.

XFinBench: Benchmarking LLMs in Complex Financial Problem Solving and Reasoning (2025.findings-acl)

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Zhihan Zhang, Yixin Cao, Lizi Liao

KnowAgent: Knowledge-Augmented Planning for LLM-Based Agents (2025.findings-naacl)

Challenge:	Existing large language models (LLMs) lack advanced capabilities such as temporal reasoning, future forecasting, and numerical modeling.
Approach:	They propose a benchmarking tool to evaluate LLMs' ability to solve complex financial problems across diverse graduate-level finance topics with multi-modal context.
Outcome:	The proposed model improves on the o1 model but still lags behind human experts in temporal reasoning and scenario planning capabilities.

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Yuqi Zhu, Shuofei Qiao, Yixin Ou, Shumin Deng, Shiwei Lyu, Yue Shen, Lei Liang, Jinjie Gu, Huajun Chen, Ningyu Zhang

Exploring and Mitigating Shortcut Learning for Generative Large Language Models (2024.lrec-main)

Challenge:	Large Language Models (LLMs) fail to effectively guide the planning trajectories during task solving and result in planning hallucinations.
Approach:	They propose a novel approach to enhance the planning capabilities of large language models by incorporating explicit action knowledge.
Outcome:	The proposed approach can achieve comparable or superior performance to existing baselines on HotpotQA and ALFWorld.

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Zechen Sun, Yisheng Xiao, Juntao Li, Yixin Ji, Wenliang Chen, Min Zhang