Papers by Yuxuan Feng

Dual-Channel Evidence Fusion for Fact Verification over Texts and Tables (2022.naacl-main)

Challenge:	Existing high-quality xMRC datasets can be further utilized to fine-tune our model.
Approach:	They propose a cross-lingual question answering over knowledge base approach that converts KB subgraphs into passages to narrow the gap between KB schemas and questions.
Outcome:	The proposed approach outperforms baselines and achieves strong few-shot and zero-shot performance on two xKBQA datasets in 12 languages.

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Nan Hu, Zirui Wu, Yuxuan Lai, Xiao Liu, Yansong Feng

Fact & Claim Verification

Challenge:	Existing fact extraction and verification tasks only consider evidence of a single format . Existing models convert evidence into either sentences or tables, thus losing context information .
Approach:	They propose a Dual Channel Unified Format fact verification model which unifies various evidence into parallel streams, i.e., natural language sentences and a global evidence table, simultaneously.
Outcome:	The proposed model outperforms existing models in two formats by a large margin . it makes the most of existing tables and tables to absorb evidence of two formats .

SURE: Mutually Visible Objects and Self-generated Candidate Labels For Relation Extraction (2025.coling-main)

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Yuxuan Feng, Qian Chen, Qianyou Wu, Xin Guo, Suge Wang

Information Extraction & Text Mining

Challenge:	Joint relation extraction models face high computational complexity, complex network architectures, difficult parameter tuning and limited interpretability.
Approach:	They develop a candidate label marker mechanism that prioritizes strategic label selection over simple label generation.
Outcome:	The proposed candidate label marks improve the SOTA methods by 2.5%, 1.9%, 1.2% . the proposed candidate labels improve the performance of the proposed methods .

Improving Controllable Text Generation with Position-Aware Weighted Decoding (2022.findings-acl)

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Yuxuan Gu, Xiaocheng Feng, Sicheng Ma, Jiaming Wu, Heng Gong, Bing Qin

Adaptive Backtracking for Privacy Protection in Large Language Models (2026.findings-acl)

Challenge:	Controllable text generation is a challenging task in natural language generation, which aims to generate diverse text related to specified attributes.
Approach:	They propose a framework that uses a lightweight controller to adjust bias signals from the controller at different decoding positions.
Outcome:	Experiments on positive sentiment control, topic control, and language detoxification show the proposed framework works on 4 SOTA models.

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Zhihao Yao, Yuxuan Gu, Xiachong Feng, Weitao Ma, Bo Li, Xiaocheng Feng, Bing Qin

Responsible & Trustworthy NLP

Challenge:	Existing privacy protection methods are prone to privacy leakage, but they are not effective in ensuring the privacy of users.
Approach:	They propose to capture latent leakage tendency of large language models during generation process and to construct a new benchmark for personal information.
Outcome:	The proposed method improves privacy by up to 14% over strong baselines against adversarial attacks, avoiding the degradation of response utility.

HD-Eval: Aligning Large Language Model Evaluators Through Hierarchical Criteria Decomposition (2024.acl-long)

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Yuxuan Liu, Tianchi Yang, Shaohan Huang, Zihan Zhang, Haizhen Huang, Furu Wei, Weiwei Deng, Feng Sun, Qi Zhang

How Many Answers Should I Give? An Empirical Study of Multi-Answer Reading Comprehension (2023.findings-acl)

Challenge:	Large language models (LLMs) are a promising alternative to expensive human evaluations.
Approach:	They propose a framework that iteratively aligns LLM-based evaluators with human preference . they decompose a given evaluation task into finer-grained criteria .
Outcome:	The proposed framework iteratively aligns LLM-based evaluators with human preference . it decomposes a given evaluation task into finer-grained criteria . the framework is efficient to train and more explainable than relying solely on prompts .

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Chen Zhang, Jiuheng Lin, Xiao Liu, Yuxuan Lai, Yansong Feng, Dongyan Zhao

UnifEE: Unified Evidence Extraction for Fact Verification (2023.eacl-main)

Challenge:	Despite recent progress in multi-answer MRC, there is no systematic analysis of how this phenomenon arises and how to better address it.
Approach:	They develop a taxonomy to categorize commonly-seen multi-answer MRC instances and examine how well different paradigms deal with different types of multi-announced questions.
Outcome:	The proposed taxonomy categorizes commonly-seen multi-answer instances and analyzes how well different paradigms deal with different types of multi-announced instances.

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Nan Hu, Zirui Wu, Yuxuan Lai, Chen Zhang, Yansong Feng

Information Extraction & Text Mining

Challenge:	Existing models extract evidence in both sentences and table cells from Wikipedia dumps, ignoring potential connections between them.
Approach:	They propose a model which uses a mixed evidence graph to extract the evidence in both formats without manually designed conversion rules.
Outcome:	The proposed model outperforms existing models and improves the verification step.

From Hypothesis to Publication: A Comprehensive Survey of AI-Driven Research Support Systems (2025.findings-emnlp)

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Zekun Zhou, Xiaocheng Feng, Lei Huang, Xiachong Feng, Ziyun Song, Ruihan Chen, Liang Zhao, Weitao Ma, Yuxuan Gu, Baoxin Wang, Dayong Wu, Guoping Hu, Ting Liu, Bing Qin

Three Sentences Are All You Need: Local Path Enhanced Document Relation Extraction (2021.acl-short)

Challenge:	rapid development of artificial intelligence (AI) technologies has inspired researchers to explore how AI can accelerate and enhance research.
Approach:	They organize the relevant studies into three main categories: hypothesis formulation, hypothesis validation, and manuscript publication.
Outcome:	The authors summarize the current state of research in three main areas: hypothesis formulation, hypothesis validation, and manuscript publication.

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Quzhe Huang, Shengqi Zhu, Yansong Feng, Yuan Ye, Yuxuan Lai, Dongyan Zhao

Information Extraction & Text Mining

Challenge:	Document-level relation extraction (RE) is more challenging than sentence RE as it often requires reasoning over multiple sentences.
Approach:	They propose a method to heuristically select evidence sentences for document-level relation extraction.
Outcome:	The proposed method can be easily combined with BiLSTM to achieve good performance on benchmark datasets even better than fancy graph neural network based methods.

Alleviating Hallucinations from Knowledge Misalignment in Large Language Models via Selective Abstention Learning (2025.acl-long)

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Lei Huang, Xiaocheng Feng, Weitao Ma, Yuchun Fan, Xiachong Feng, Yuxuan Gu, Yangfan Ye, Liang Zhao, Weihong Zhong, Baoxin Wang, Dayong Wu, Guoping Hu, Lingpeng Kong, Tong Xiao, Ting Liu, Bing Qin

Democratizing Reasoning Ability: Tailored Learning from Large Language Model (2023.emnlp-main)

Challenge:	Large language models (LLMs) suffer from severe hallucination issues due to the knowledge misalignment between the pre-training stage and the supervised fine-tuning stage.
Approach:	They propose a training objective with an abstention mechanism that selectively rejects tokens that misalign with the desired knowledge distribution via a special [REJ] token.
Outcome:	The proposed model selectively rejects tokens that misalign with the desired knowledge distribution via a special [REJ] token.

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Zhaoyang Wang, Shaohan Huang, Yuxuan Liu, Jiahai Wang, Minghui Song, Zihan Zhang, Haizhen Huang, Furu Wei, Weiwei Deng, Feng Sun, Qi Zhang

SocialEval: Evaluating Social Intelligence of Large Language Models (2025.acl-long)

Challenge:	Large language models (LLMs) exhibit impressive emergent abilities in natural language processing, but their democratization is hindered due to huge computation requirements and closed-source nature.
Approach:	They propose a tailored learning approach to distill the exclusive reasoning ability to smaller LMs to facilitate democratization.
Outcome:	The proposed approach enables the democratization of the exclusive reasoning ability by leveraging the black-box model as a reasoning teacher.

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Jinfeng Zhou, Yuxuan Chen, Yihan Shi, Xuanming Zhang, Leqi Lei, Yi Feng, Zexuan Xiong, Miao Yan, Xunzhi Wang, Yaru Cao, Jianing Yin, Shuai Wang, Quanyu Dai, Zhenhua Dong, Hongning Wang, Minlie Huang

Attribution-Based Analysis and Optimization of Modular Agentic Workflows (2026.findings-acl)

Challenge:	Existing work on LLMs does not address their social intelligence (SI) and their discrepancy with humans.
Approach:	They propose a script-based bilingual SI benchmark that integrates outcome-oriented goal achievement evaluation and process-oriented interpersonal ability evaluation by manually crafting narrative scripts.
Outcome:	The proposed model is based on a script-based bilingual evaluation paradigm that integrates outcome- and process-oriented evaluation by manually crafting narrative scripts.

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Yingxuan Yang, Bo Huang, Siyuan Qi, Chao Feng, Haoyi Hu, Yuxuan Zhu, Jinbo Hu, Haoran Zhao, Ziyi He, Xiao Liu, ZongYu Wang, Muning Wen, Lin Qiu, Xuezhi Cao, Xunliang Cai, Yong Yu, Weinan Zhang

Challenge:	Large Language Models (LLMs) have driven the rise of agentic workflows . yet, how can we attribute performance gains to individual upgrades and their interactions?
Approach:	They propose a game-theoretic framework that models component upgrades as players and evaluates component coalitions to compute Shapley values.
Outcome:	The proposed framework provides interaction-aware attribution and recommendation for model allocation under a fixed workflow structure.

Context-Aware Hierarchical Taxonomy Generation for Scientific Papers via LLM-Guided Multi-Aspect Clustering (2025.emnlp-main)

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Kun Zhu, Lizi Liao, Yuxuan Gu, Lei Huang, Xiaocheng Feng, Bing Qin

Text Clustering

Challenge:	Existing taxonomy construction methods lack coherence and granularity . Existing approaches rely on manual or narrowly defined schemes .
Approach:	They propose a context-aware hierarchical taxonomy generation framework that integrates LLMs with dynamic clustering.
Outcome:	The proposed method outperforms existing methods in taxonomy coherence, granularity, and interpretability.

Chain of Condition: Construct, Verify and Solve Conditions for Conditional Question Answering (2024.findings-emnlp)

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Jiuheng Lin, Yuxuan Lai, Yansong Feng

Investigating and Mitigating the Multimodal Hallucination Snowballing in Large Vision-Language Models (2024.acl-long)

Challenge:	Existing methods for conditional question answering struggle with finding probable answers and identifying missing conditions.
Approach:	They propose a conditional question answering prompting approach that first identifies all conditions and constructs their logical relationships explicitly according to the document, then verifyes whether these conditions are satisfied and finally solves the logical expression to indicate any missing conditions.
Outcome:	The proposed method outperforms existing prompting baselines on two CQA benchmark datasets and can facilitate GPT-3.5-Turbo or GPT-4 to outperFORM all existing supervised models.

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Weihong Zhong, Xiaocheng Feng, Liang Zhao, Qiming Li, Lei Huang, Yuxuan Gu, Weitao Ma, Yuan Xu, Bing Qin

Multimodality

Challenge:	Large Vision-Language Models (LVLMs) suffer from multimodal hallucinations . however, the generated hallucines could influence the models’ subsequent generation .
Approach:	They propose a framework to evaluate LVLMs' behaviors when encountering generated hallucinations and a method to revise the output distribution of LVLs with the one derived from the residual visual input.
Outcome:	The proposed framework reduces the performance of open-source LVLMs by 31%, indicating that they are prone to accept the generated hallucinations and make false claims that they would not have supported without distractions.

Modeling discourse cohesion for discourse parsing via memory network (P18-2)

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Yanyan Jia, Yuan Ye, Yansong Feng, Yuxuan Lai, Rui Yan, Dongyan Zhao

Why Machine Reading Comprehension Models Learn Shortcuts? (2021.findings-acl)

Challenge:	Existing approaches to discourse parsing focus on studying the semantic and syntactic aspects of EDU pairs, but they do not address long span dependencies.
Approach:	They propose a new transition-based discourse parser that takes discourse cohesion into account by using memory networks.
Outcome:	The proposed method outperforms traditional features and improves performance on the RST discourse treebank.

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Yuxuan Lai, Chen Zhang, Yansong Feng, Quzhe Huang, Dongyan Zhao

Machine Reading Comprehension

Challenge:	Existing studies show that many MRC models learn shortcuts to outwit benchmarks, but the performance is unsatisfactory in real-world applications.
Approach:	They propose to use shortcut questions to analyze learning difficulty of MRC models . they propose to analyze the learning difficulty regarding shortcut and challenging questions .
Outcome:	The proposed methods show that a large proportion of shortcut questions in training data make models rely on shortcut tricks excessively.

Extract, Integrate, Compete: Towards Verification Style Reading Comprehension (2021.findings-emnlp)

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Chen Zhang, Yuxuan Lai, Yansong Feng, Dongyan Zhao

Enhancing Key-Value Memory Neural Networks for Knowledge Based Question Answering (N19-1)

Challenge:	VGaokao is a verification style reading comprehension dataset for Chinese language tests requiring advanced language understanding skills.
Approach:	They propose a new extract-integration-compete approach to extract complementary evidence from Chinese Language tests of Gaokao and a pairwise competition to push models to learn the subtle difference between similar text pieces.
Outcome:	The proposed approach outperforms baselines on VGaokao with retrieved complementary evidence while having the merits of efficiency and explainability.

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Kun Xu, Yuxuan Lai, Yansong Feng, Zhiguo Wang

Natural Language Interfaces

Challenge:	Existing Key-value Memory Neural Networks are effective for shallow reasoning over documents . but extending them to Knowledge Based Question Answering is not trivial .
Approach:	They propose a mechanism to enable conventional KV-MemNNs models to perform interpretable reasoning for complex questions.
Outcome:	The proposed solution provides better reasoning abilities on complex questions and achieves state-of-the-art performance.

Length Controlled Generation for Black-box LLMs (2025.acl-long)

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Yuxuan Gu, Wenjie Wang, Xiaocheng Feng, Weihong Zhong, Kun Zhu, Lei Huang, Ting Liu, Bing Qin, Tat-Seng Chua

Responsible & Trustworthy NLP

Challenge:	Existing length control methods involve fine-tuning the parameters of LLMs, which is inefficient and suboptimal for practical use.
Approach:	They propose an iterative sampling framework that regulates LLMs to generate length-constrained text without modifying the underlying parameters.
Outcome:	The proposed method achieves 100% success rates on Llama3.1 tasks with minimal additional computational overhead.

Controllable Text Generation via Probability Density Estimation in the Latent Space (2023.acl-long)

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Yuxuan Gu, Xiaocheng Feng, Sicheng Ma, Lingyuan Zhang, Heng Gong, Weihong Zhong, Bing Qin

Text Generation

Challenge:	Existing control approaches cannot effectively model complex space with diverse attributes, high dimensionality, and asymmetric structure, leaving subsequent controls unsatisfactory.
Approach:	They propose a control framework using probability density estimation in the latent space and an invertible transformation function that maps the complex distributions to simple Gaussian distributions in the prior space.
Outcome:	The proposed method outperforms baselines on attribute relevance and text quality, achieving a new SOTA.

MeasHalu: Mitigation of Scientific Measurement Hallucinations for Large Language Models with Enhanced Reasoning (2026.findings-acl)

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Ruijun Huang, Zhiqiao Kang, Yuxuan Zhu, Junxiong Li, Jiahao Zhao, Minghuan Tan, Feng Jiang, Min Yang

A Distributional Lens for Multi-Aspect Controllable Text Generation (2022.emnlp-main)

Challenge:	Large Language Models (LLMs) exhibit severe hallucinations, which undermine reliability of automated scientific document understanding systems.
Approach:	They propose a framework for mitigating scientific measurement hallucinations through enhanced reasoning and targeted optimization.
Outcome:	The proposed framework significantly reduces hallucination rates and improves overall accuracy on the MeasEval benchmark.

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Yuxuan Gu, Xiaocheng Feng, Sicheng Ma, Lingyuan Zhang, Heng Gong, Bing Qin

Text Generation

Challenge:	Existing methods for multi-aspect control suffer from attribute degeneration due to mutual interference of these controllers.
Approach:	They propose to use attribute fusion to find the intersections of multiple attributes as their combination for generation.
Outcome:	The proposed method outperforms baselines on attribute relevance and text quality and achieves the SOTA.

Stratagem: Learning Transferable Reasoning via Trajectory-Modulated Game Self-Play (2026.acl-long)

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Xiachong Feng, Deyi Yin, Xiaocheng Feng, Yi Jiang, Libo Qin, Yangfan Ye, Lei Huang, Weitao Ma, Qiming Li, Yuxuan Gu, Bing Qin, Lingpeng Kong

LLaMA-Rider: Spurring Large Language Models to Explore the Open World (2024.findings-naacl)

Challenge:	Existing self-play approaches to developing general reasoning in language models rely on terminal game outcomes.
Approach:	They propose a game-based reasoning transfer model that addresses two barriers to reasoning transfer.
Outcome:	The proposed model improves mathematical reasoning, general reasoning, and code generation benchmarks.

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Yicheng Feng, Yuxuan Wang, Jiazheng Liu, Sipeng Zheng, Zongqing Lu

NiuTrans.LMT: Toward Inclusive and Scalable Multilingual Machine Translation with LLMs (2026.acl-long)

Challenge:	Recent studies have used Large Language Models to help decision-making and planning in environments, but their capacity to acquire environmental knowledge and adapt in an open world remains uncertain.
Approach:	They propose an approach to spur LLMs to explore the open world, gather experiences, and learn to improve their task-solving capabilities by using a feedback-revision mechanism.
Outcome:	The proposed model enhances the efficiency of the LLM in exploring the open world and improves its ability to accomplish more tasks through fine-tuning with merely 1.3k instances of collected data, showing minimal training costs compared to baseline using reinforcement learning.

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Yingfeng Luo, Ziqiang Xu, Yuxuan Ouyang, MuRun Yang, DingYang Lin, Kaiyan Chang, Tong Zheng, Bei Li, Peinan Feng, Quan Du, Tong Xiao, JingBo Zhu

Multilinguality

Challenge:	Large language models have significantly advanced Multilingual Machine Translation (MMT) yet scaling to many languages while maintaining robust performance across directions remains challenging.
Approach:	They propose a strategy to reduce the number of translations in one direction . they propose auxiliary parallel sentences to promote cross-lingual transfer .
Outcome:	The proposed model performs on par with or better than substantially larger baselines.

Calibrating LLM-Based Evaluator (2024.lrec-main)

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Yuxuan Liu, Tianchi Yang, Shaohan Huang, Zihan Zhang, Haizhen Huang, Furu Wei, Weiwei Deng, Feng Sun, Qi Zhang

Extending Context Window of Large Language Models from a Distributional Perspective (2024.emnlp-main)

Challenge:	Existing models for large language models lack the ability to calibrate their outputs towards human preference.
Approach:	They propose a multi-stage, gradient-free approach to calibrate an LLM-based evaluator toward human preference.
Outcome:	The proposed approach improves correlation with expert evaluation on multiple text quality evaluation datasets.

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Yingsheng Wu, Yuxuan Gu, Xiaocheng Feng, Weihong Zhong, Dongliang Xu, Qing Yang, Hongtao Liu, Bing Qin

Learning Fine-Grained Grounded Citations for Attributed Large Language Models (2024.findings-acl)

Challenge:	Existing scaling methods for extending context window rely on empirical approaches and lack understanding of the internal distribution within RoPE resulting in suboptimal performance.
Approach:	They propose to optimize the context window extending task from the view of rotary angle distribution by minimizing disturbance between rotary angles to maintain consistency with the pre-training phase.
Outcome:	The proposed approach reduces by up to 72% of the distributional disturbance when extending LLaMA2’s context window to 8k, and reduces it by up 32% when extending to 16k.

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Lei Huang, Xiaocheng Feng, Weitao Ma, Yuxuan Gu, Weihong Zhong, Xiachong Feng, Weijiang Yu, Weihua Peng, Duyu Tang, Dandan Tu, Bing Qin

SAVOIR: Learning Social Savoir-Faire via Shapley-based Reward Attribution (2026.findings-acl)

Challenge:	despite impressive performance, large language models still struggle with hallucinations . current approaches suffer from suboptimal citation quality due to reliance on in-context learning .
Approach:	They propose a framework that teaches large language models to generate fine-grained citations.
Outcome:	The proposed framework outperforms all baselines on the ALCE benchmark and achieves an average improvement of 14.21% in citation quality.

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Xiachong Feng, Yi Jiang, Xiaocheng Feng, Deyi Yin, Libo Qin, Yangfan Ye, Lei Huang, Weitao Ma, Yuxuan Gu, Chonghan Qin, Bing Qin, Lingpeng Kong

Improving Contextual Faithfulness of Large Language Models via Retrieval Heads-Induced Optimization (2025.acl-long)

Challenge:	Existing approaches to improve social intelligence of AI systems employ retrospective attributions and lack theoretical grounding.
Approach:	They propose a framework that uses Shapley values to ensure fair credit distribution with axiomatic guarantees of efficiency, symmetry, and marginality.
Outcome:	The proposed framework matches or exceeds proprietary models including GPT-4o and Claude-3.5-Sonnet.

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Lei Huang, Xiaocheng Feng, Weitao Ma, Yuchun Fan, Xiachong Feng, Yangfan Ye, Weihong Zhong, Yuxuan Gu, Baoxin Wang, Dayong Wu, Guoping Hu, Bing Qin

Lattice-BERT: Leveraging Multi-Granularity Representations in Chinese Pre-trained Language Models (2021.naacl-main)

Challenge:	Existing frameworks for retrieval-augmented large language models (LLMs) are lacking in LFQA faithfulness testing.
Approach:	They propose a framework to teach retrieval-augmented large language models to explicitly discriminate between faithful and unfaithful generations.
Outcome:	The proposed framework outperforms GPT-4o in LFQA scenarios and outperformed existing benchmarks.

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Yuxuan Lai, Yijia Liu, Yansong Feng, Songfang Huang, Dongyan Zhao