Papers by Yi Feng

MS2SL: Multimodal Spoken Data-Driven Continuous Sign Language Production (2024.findings-acl)

Challenge:	Existing methods handle long-term memory (LTM) and short-term (STM) as separate components, relying on heuristics or auxiliary controllers, which limits adaptability and end-to-end optimization.
Approach:	They propose a framework that integrates LTM and STM management directly into the agent's policy and propose 'agentic memory' to train such unified behaviors.
Outcome:	The proposed framework outperforms strong memory-augmented baselines on five long-horizon benchmarks and achieves higher-quality long-term memory and more efficient context usage.

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Jian Ma, Wenguan Wang, Yi Yang, Feng Zheng

From Superficial to Deep: Integrating External Knowledge for Follow-up Question Generation Using Knowledge Graph and LLM (2025.coling-main)

Challenge:	Existing solutions for sign language production are limited due to phonological differences and data scarcity.
Approach:	They propose a unified framework for continuous sign language production that generates sign predictions step by step from text or speech embeddings.
Outcome:	The proposed model achieves competitive performance on how2sign and PHOENIX14T datasets.

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Jianyu Liu, Yi Huang, Sheng Bi, Junlan Feng, Guilin Qi

Knowledge Representation

Challenge:	Existing methods for generating follow-up questions are limited to shallow contextual questions that are uninspiring and have a large gap to the human level.
Approach:	They propose a three-stage external knowledge-enhanced follow-up question generation method which generates questions by identifying contextual topics, building a knowledge graph online, and finally combining these with a large language model to generate the final question.
Outcome:	The proposed method generates questions by identifying contextual topics, building a knowledge graph (KG) online, and finally combining these with a large language model to generate the final question.

Generalized Intent Discovery: Learning from Open World Dialogue System (2022.coling-1)

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Yutao Mou, Keqing He, Yanan Wu, Pei Wang, Jingang Wang, Wei Wu, Yi Huang, Junlan Feng, Weiran Xu

JECC: Commonsense Reasoning Tasks Derived from Interactive Fictions (2023.findings-acl)

Challenge:	Existing intent classification models rely on a pre-defined intent set and supervised labels, which is limited in some practical scenarios.
Approach:	They propose to extend an IND intent classifier to an open-world intent set including IND and OOD intents.
Outcome:	The proposed task can classify IND and OOD intents while discovering new unlabeled OOD types incrementally.

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Mo Yu, Yi Gu, Xiaoxiao Guo, Yufei Feng, Xiaodan Zhu, Michael Greenspan, Murray Campbell, Chuang Gan

Legal Case Retrieval: A Survey of the State of the Art (2024.acl-long)

Challenge:	Existing benchmarks focus on a single reasoning type and ask human annotators to write candidate statements related to the particular type of commonsense.
Approach:	They propose a new commonsense reasoning dataset based on human’s Interactive Fiction (IF) gameplaywalkthroughs.
Outcome:	The proposed dataset is challenging to previous machine reading models and large language models with a significant 20%performance gap compared to human experts.

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Yi Feng, Chuanyi Li, Vincent Ng

LJPCheck: Functional Tests for Legal Judgment Prediction (2024.findings-acl)

Challenge:	Recent years have seen increasing attention on Legal Case Retrieval (LCR) this task involves retrieving cases from a legal database of historical cases that are similar to a given query case.
Approach:	They present a survey of the major milestones made in legal case retrieval research . they seek to understand the datasets and recent neural models and their performances .
Outcome:	The proposed task is based on a dataset of historical cases similar to a given query case.

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Yuan Zhang, Wanhong Huang, Yi Feng, Chuanyi Li, Zhiwei Fei, Jidong Ge, Bin Luo, Vincent Ng

Responsible & Trustworthy NLP

Challenge:	Existing LJP models fail to evaluate specific aspects of their performance, such as legal fairness and judicial fairness.
Approach:	They propose a suite of functional tests for LJP models to comprehend LJp models’ behaviors and offer diagnostic insights.
Outcome:	Extensive tests reveal weaknesses in LJP models and provide diagnostic insights.

PsyChain: A Collaborative Chain-of-Agents Framework for Generating Personalized and Professional Counseling Dialogues (2026.findings-acl)

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Yi Feng, Zijie Yang, Chen Zhang, Wenxuan Zhang, Dongming Zhang, Liping Jing, Jian Yu

PERM: Psychology-grounded Empathetic Reward Modeling for Large Language Models (2026.findings-acl)

Challenge:	Existing psychological counseling datasets suffer from monolithic client personas, insufficient therapeutic depth, and a lack of process controllability.
Approach:	They propose a framework that evolves static counseling corpora into high-fidelity dialogues . they use a Client Profiler that pairs life scenarios with psychological personality archetypes based on client personality and stage progression .
Outcome:	The proposed framework achieves 61-91% win rates against domain-specific baselines in pairwise evaluation and the highest average score in human evaluation, indicating potential for real-world counseling.

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Chengbing Wang, Wuqiang Zheng, Yang Zhang, Fengbin Zhu, Junyi Cheng, Yi Xie, Wenjie Wang, Fuli Feng

Mitigating Over-Generation for Unsupervised Keyphrase Extraction with Heterogeneous Centrality Detection (2023.emnlp-main)

Challenge:	Existing reward models evaluate empathy from a single perspective, overlooking bidirectional interaction nature of empathy.
Approach:	They propose a reward model that evaluates empathy from a single perspective . they propose PERM to integrate a bystander perspective to monitor overall interaction quality .
Outcome:	a new reward model outperforms state-of-the-art models on an emotional intelligence benchmark and an industrial daily conversation dataset.

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Mingyang Song, Pengyu Xu, Yi Feng, Huafeng Liu, Liping Jing

Reframe Your Life Story: Interactive Narrative Therapist and Innovative Moment Assessment with Large Language Models (2025.emnlp-main)

Challenge:	Existing keyphrase extraction models incorrectly determine a keyphrase as a phrase but output other candidates as keyphrases because they contain the same word.
Approach:	They propose a new approach that detects both implicit and explicit centrality within a heterogeneous graph as the importance score of each candidate keyphrase.
Outcome:	The proposed approach outperforms state-of-the-art keyphrase extraction models on three benchmark datasets.

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Yi Feng, Jiaqi Wang, Wenxuan Zhang, Zhuang Chen, Shen Yutong, Xiyao Xiao, Minlie Huang, Liping Jing, Jian Yu

Can Multi-agent Help Disambiguation in Multi-domain Translation? (2026.findings-acl)

Challenge:	Existing approaches to mental health support lack realism and capture therapeutic progression over time.
Approach:	They propose a framework that simulates expert narrative therapists by planning therapeutic stages, guiding reflection levels, and generating contextually appropriate responses through retrieval-augmentation.
Outcome:	The proposed framework outperforms standard methods in quality and depth on 260 simulated clients and 230 human participants.

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Zhibo Man, Shaoyang Xu, Yujie Zhang, Yi Feng, Yuanmeng Chen, Yufeng Chen, Xu Jinan, Wenxuan Zhang

Machine Translation

Challenge:	Existing multi-agent systems have shown strong potential for machine translation (MT) but their performance in multidomain translation remains unsatisfactory due to cross-domain word ambiguity .
Approach:	They propose a multi-agent collaborative disambiguation framework for MDT that leverages the collaborative capabilities of LLMs for disambiguations.
Outcome:	The proposed framework improves translation performance across multiple domains and improves disambiguation accuracy.

TofuEval: Evaluating Hallucinations of LLMs on Topic-Focused Dialogue Summarization (2024.naacl-long)

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Liyan Tang, Igor Shalyminov, Amy Wong, Jon Burnsky, Jake Vincent, Yu’an Yang, Siffi Singh, Song Feng, Hwanjun Song, Hang Su, Lijia Sun, Yi Zhang, Saab Mansour, Kathleen McKeown

Natural Language Interfaces

Challenge:	Existing LLMs hallucinate significant amounts of factual errors in the dialogue domain, regardless of the model’s size.
Approach:	They propose to evaluate topic-focused dialogue summarization by using large language models (LLMs) they use human annotations to evaluate factual consistency and explain factually inconsistent sentences.
Outcome:	The proposed evaluation benchmark on topic-focused dialogue summarization shows that existing LLMs hallucinate significant amounts of factual errors regardless of the model’s size.

Counterfactual Debating with Preset Stances for Hallucination Elimination of LLMs (2025.coling-main)

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Yi Fang, Moxin Li, Wenjie Wang, Lin Hui, Fuli Feng

IPR: Intelligent Prompt Routing with User-Controlled Quality-Cost Trade-offs (2025.emnlp-industry)

Challenge:	Existing solutions to alleviate hallucination have considered utilizing LLMs’ inherent reasoning abilities to alleviating hallucinism, such as self-correction and diverse sampling methods.
Approach:	They propose a counterfactual multi-agent debate framework that predetermines LLMs' stances to override their inherent biases for answer inspection.
Outcome:	Extensive experiments on four datasets of three tasks demonstrate the superiority of the proposed framework over existing methods.

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Aosong Feng, Balasubramaniam Srinivasan, Yun Zhou, Zhichao Xu, Kang Zhou, Sheng Guan, Yueyan Chen, Xian Wu, Ninad Kulkarni, Yi Zhang, Zhengyuan Shen, Dmitriy Bespalov, Soumya Smruti Mishra, Yifei Teng, Darren Yow-Bang Wang, Haibo Ding, Lin Lee Cheong

Low-Resource NLP

Challenge:	Existing systems require users to manually select models or employ rigid routing rules that fail to capture the continuous spectrum of query complexity.
Approach:	They propose a quality-constrained intelligent prompt routing framework that automatically selects optimal models based on predicted response quality and user-specified tolerance levels.
Outcome:	The proposed framework achieves 43.9% cost reduction while maintaining quality parity with strongest model in the Claude family and processes requests with sub-150ms latency.

PSSAT: A Perturbed Semantic Structure Awareness Transferring Method for Perturbation-Robust Slot Filling (2022.coling-1)

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Guanting Dong, Daichi Guo, Liwen Wang, Xuefeng Li, Zechen Wang, Chen Zeng, Keqing He, Jinzheng Zhao, Hao Lei, Xinyue Cui, Yi Huang, Junlan Feng, Weiran Xu

Robustness in NLP

Challenge:	Existing slot filling models memorize inherent patterns of entities and contexts from training data.
Approach:	They propose a perturbed semantic structure awareness transferring method for slot filling models . they use two MLM-based training strategies to learn contextual semantic structure and word distribution .
Outcome:	The proposed method outperforms existing methods and gains strong generalization while preventing model from memorizing inherent patterns of entities and contexts.

D2PCM:A Multi-Turn Dialogue Dataset with Personalized Contextual Memory (2026.findings-acl)

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Zhe Yang, Yi Huang, Yaqin Chen, Chunyang Gao, Jingyu Yao, Junlan Feng

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

Challenge:	Conventional interactive algorithms have predominantly treated memory as a contextual element, neglecting the nuanced cognitive processes involved in individualized memory encoding and retrieval.
Approach:	They propose a multi-turn dialogue dataset with Personalized Contextual Memory to facilitate advanced research on personalized memory processing.
Outcome:	The proposed datasets provide a comprehensive benchmark to facilitate advanced research on personalized memory processing.

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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

PLAWBENCH: A Rubric-Based Benchmark for Evaluating LLMs in Real-World Legal Practice (2026.acl-long)

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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Yuzhen Shi, Huanghai Liu, Yiran HU, Song Gaojie, Xu Xinran, Yubo Ma, Tianyi Tang, Li Zhang, Qingjing Chen, Feng Di, Wenbo Lv, Weiheng Wu, Kexin Yang, Sen Yang, Wei Wang, Rongyao Shi, Qiu Yuanyang, Yuemeng Qi, Zhang Jingwen, Sui Xiaoyu, Yifan Chen, Zhang Yi, An Yang, Bowen Yu, Dayiheng Liu, Junyang Lin, Weixing Shen, Bing Zhao, Charles L. A. Clarke, HU Wei

Towards Exploiting Sticker for Multimodal Sentiment Analysis in Social Media: A New Dataset and Baseline (2022.coling-1)

Challenge:	Existing benchmarks for large language models (LLMs) are coarse, single-dimensional metrics and do not explicitly assess fine-grained legal reasoning.
Approach:	They propose a Practical Law Benchmark to evaluate large language models in real-world legal practice scenarios.
Outcome:	The proposed model is based on 850 questions and 13 scenarios with expert-designed evaluation rubrics.

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Feng Ge, Weizhao Li, Haopeng Ren, Yi Cai

Sentiment Analysis

Challenge:	Sentiment analysis in social media is challenging because of the lack of context.
Approach:	They propose to use stickers to perform a multimodal sentiment analysis task using Chinese stickers.
Outcome:	The proposed model performs best compared with other models.

Meta-Reinforced Multi-Domain State Generator for Dialogue Systems (2020.acl-main)

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Yi Huang, Junlan Feng, Min Hu, Xiaoting Wu, Xiaoyu Du, Shuo Ma

Controllable LLM Reasoning via Sparse Autoencoder-Based Steering (2026.acl-long)

Challenge:	Existing methods to train a multi-domain dialogue state tracker are lacking in accuracy.
Approach:	They propose a Meta-Reinforced Multi-Domain State Generator to train a DST meta-learning model with a few domains as source domains and a new domain as target domain.
Outcome:	The proposed system outperforms the traditional training approach with extremely little training data in target domain.

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Yi Fang, Wenjie Wang, Mingfeng Xue, Boyi Deng, Fengli Xu, Dayiheng Liu, Fuli Feng

Don’t Miss the Potential Customers! Retrieving Similar Ads to Improve User Targeting (2021.findings-emnlp)

Challenge:	Existing methods struggle to control fine-grained reasoning strategies due to conceptual entanglement in LRMs’ hidden states.
Approach:	They propose to decompose strategy-entangled hidden states into a disentangled feature space by using Sparse Autoencoders to identify the few strategy-specific features from the vast pool of SAE features.
Outcome:	The proposed method outperforms existing methods by 15% in control effectiveness.

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Yi Feng, Ting Wang, Chuanyi Li, Vincent Ng, Jidong Ge, Bin Luo, Yucheng Hu, Xiaopeng Zhang

OMIBench: Benchmarking Olympiad-Level Multi-Image Reasoning in Large Vision-Language Models (2026.acl-long)

Challenge:	a method for user targeting is developed to identify online users to whom an ad should be targeted.
Approach:	They propose a method for automatic augmentation of positive and negative clickthrough data for user targeting models.
Outcome:	The proposed method can increase positive and negative instances of positive training instances on two datasets.

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Qiguang Chen, Chengyu Luan, Jiajun Wu, Qiming Yu, Yi Yang, Yizhuo Li, Jingqi Tong, Xiachong Feng, Libo Qin, Wanxiang Che

Match More, Extract Better! Hybrid Matching Model for Open Domain Web Keyphrase Extraction (2024.findings-acl)

Challenge:	Existing multimodal reasoning benchmarks for large vision-language models emphasize single-image analysis and fail to exploit contextual information across multiple images.
Approach:	They propose a benchmark to evaluate Olympiad-level reasoning when evidence is distributed over multiple images.
Outcome:	The proposed model outperforms existing models on bi-image Olympiads and Gemini-3-Pro on multimodal Olympiad-level reasoning tasks.

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Mingyang Song, Liping Jing, Yi Feng

DuNST: Dual Noisy Self Training for Semi-Supervised Controllable Text Generation (2023.acl-long)

Challenge:	Existing models for keyphrase extraction use noisy information to filter the salient phrases from the document.
Approach:	They propose a hybrid matching model that combines representation-focused and interaction-based matching modules into a unified framework for improving keyphrase extraction.
Outcome:	The proposed model outperforms state-of-the-art keyphrase extraction models on the OpenKP dataset.

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Yuxi Feng, Xiaoyuan Yi, Xiting Wang, Laks Lakshmanan, V.S., Xing Xie

Responsible & Trustworthy NLP

Challenge:	Existing approaches to augment self-training (ST) in attribute-controllable language generation are limited and limited.
Approach:	They propose a new ST framework that integrates self-generated pseudo text into attribute-controllable language generation.
Outcome:	The proposed framework can be applied to semi-supervised controllable language generation.

R3: End-to-End Reasoning-based Planning for Multi-step Retrosynthesis via Reinforcement Learning (2026.acl-long)

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YiFei Wang, Qizhi Pei, Jiangtao Feng, Yuntian Shi, Yi Duan, Lihao Wang, Lei Bai, Lijun Wu, Wei-Ying Ma, Hao Zhou

Eval-RAR: Evaluation-Driven Retrieval-Augmented Reasoning via Reinforcement Learning (2026.findings-acl)

Challenge:	Experimental results show that R3 is a superior alternative to traditional search algorithms for multistep retrosynthesis planning.
Approach:	They propose a framework that reformulates multistep retrosynthetic planning as a generative reasoning task.
Outcome:	The proposed framework achieves state-of-the-art Top-1 accuracy of 43.7% on retrobench . it leverages Large Language Models to reformulate multistep retrosynthesis as a generative reasoning task.

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Heng Yu, Rui Li, Qi Liu, Wenjun Feng, Junfeng Kang, Yi Zhan

Towards Low-Resource Semi-Supervised Dialogue Generation with Meta-Learning (2020.findings-emnlp)

Challenge:	Existing methods for retrieval-augmented generation fail to provide explicit supervision for internal reasoning process.
Approach:	They propose an Evaluation-driven Retrieval-Augmented Reasoning framework that uses reinforcement learning and a fine-grained evaluation reward to optimize the process.
Outcome:	Eval-RAR outperforms existing methods on QA benchmarks on seven single-hop and multi-hop tasks.

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Yi Huang, Junlan Feng, Shuo Ma, Xiaoyu Du, Xiaoting Wu

Palette of Language Models: A Solver for Controlled Text Generation (2025.naacl-long)

Challenge:	Existing systems that use labelled data to generate dialogues are lacking in high accuracy.
Approach:	They propose a meta-learning based semi-supervised explicit dialogue state tracker for neural dialogue generation, denoted as MEDST.
Outcome:	The proposed system outperforms existing systems by 18.7% goal accuracy and 14.3% entity match rate on the KVRET corpus with 2% labelled data in semi-supervision.

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Zhe Yang, Yi Huang, Yaqin Chen, XiaotingWu XiaotingWu, Junlan Feng, Chao Deng

RelCLIP: Adapting Language-Image Pretraining for Visual Relationship Detection via Relational Contrastive Learning (2022.emnlp-main)

Challenge:	Recent advances in large language models have revolutionized text generation with their remarkable capabilities.
Approach:	They propose to combine a single-attribute model with a discriminative model to achieve a combination strategy that incorporates positive correlation and attribute enhancement.
Outcome:	The proposed method is adapted for single-attribute control scenario and achieves surpassing results.

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Yi Zhu, Zhaoqing Zhu, Bingqian Lin, Xiaodan Liang, Feng Zhao, Jianzhuang Liu

AutoSchemaKG: Autonomous Knowledge Graph Construction through Dynamic Schema Induction from Web-Scale Corpora (2026.acl-long)

Challenge:	Existing visual relationship detection models only use numeric ids of relation labels for training, but ignore semantic correlation between labels.
Approach:	They propose a visual Relationship prediction framework that transfers natural language knowledge from Contrastive Language-Image Pre-training models to enhance the relationship prediction.
Outcome:	The proposed framework improves visual relationship prediction by matching semantic correlations with relation triplets.

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Jiaxin Bai, Wei Fan, Qi Hu, Qing Zong, Chunyang Li, Hong Ting Tsang, Hongyu Luo, Yauwai Yim, Haoyu Huang, Xiao Zhou, Feng Qin, Tianshi Zheng, Xi Peng, Xin Yao, Huiwen Yang, Leijie Wu, JI Yi, Gong Zhang, Renhai Chen, Yangqiu Song

From Query to Counsel: Structured Reasoning with a Multi-Agent Framework and Dataset for Legal Consultation (2026.acl-long)

Challenge:	Existing knowledge graph construction frameworks require predefined schemas, limiting their scalability and domain coverage.
Approach:	They propose a framework for fully autonomous knowledge graph construction that eliminates the need for predefined schemas.
Outcome:	The proposed framework outperforms state-of-the-art models on multi-hop QA tasks and enhances LLM factuality.

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Mingfei Lu, Yi Zhang, Mengjia Wu, Yue Feng

MultiFinBen: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application (2026.acl-long)

Challenge:	Legal consultation question answering presents unique challenges compared to traditional legal QA tasks .
Approach:	They propose a framework that converts queries into a legal element graph . jurisMA supports dynamic routing, statutory grounding, and stylistic optimization .
Outcome:	The proposed framework outperforms general-purpose and legal-domain LLMs across multiple lexical and semantic metrics.

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Xueqing Peng, Lingfei Qian, Yan Wang, Ruoyu Xiang, Yueru He, Yang Ren, Mingyang Jiang, Vincent Jim Zhang, Yuqing Guo, Jeff Zhao, Huan He, Yi Han, Yun Feng, Yuechen Jiang, Yupeng Cao, Haohang Li, Yangyang Yu, Xiaoyu Wang, Penglei Gao, Shengyuan Lin, Keyi Wang, Shanshan Yang, Yilun Zhao, Zhiwei Liu, Peng Lu, Jerry Huang, Suyuchen Wang, Triantafillos Papadopoulos, Polydoros Giannouris, Efstathia Soufleri, Nuo Chen, Zhiyang Deng, Heming Fu, Yijia Zhao, Mingquan Lin, Meikang Qiu, Kaleb E Smith, Arman Cohan, Xiao-Yang Liu, Jimin Huang, Guojun Xiong, Alejandro Lopez-Lira, Xi Chen, Junichi Tsujii, Jian-Yun Nie, Sophia Ananiadou, Qianqian Xie

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

Challenge:	Existing evaluations of LLMs in finance are text-only, monolingual, and largely saturated by current models.
Approach:	They propose a multilingual and multimodal benchmark for evaluating LLMs in real financial contexts.
Outcome:	The first expert-annotated multilingual and multimodal benchmark is released . it evaluates 21 leading LLMs and shows they perform better in multilingual settings .

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

Legal Judgment Prediction via Event Extraction with Constraints (2022.acl-long)

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

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Yi Feng, Chuanyi Li, Vincent Ng

Ask Again, Then Fail: Large Language Models’ Vacillations in Judgment (2024.acl-long)

Challenge:	Existing models fail to locate key event information that determines the judgment results.
Approach:	They propose an Event-based Prediction Model with constraints that exploits constraints in LJP.
Outcome:	The proposed model surpasses existing models on a standard LJP dataset in English and French.

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Qiming Xie, Zengzhi Wang, Yi Feng, Rui Xia

Joint Modeling of Structure Identification and Nuclearity Recognition in Macro Chinese Discourse Treebank (C18-1)

Challenge:	Existing large language models often waver in their judgments when faced with follow-up questions . this is a challenge for generating reliable responses and building user trust .
Approach:	They propose a Follow-up Questioning Mechanism and two metrics to quantify this inconsistency . they also develop a framework that teaches large language models to maintain original correct judgments .
Outcome:	The proposed framework improves the general capabilities of large language models by allowing them to maintain original correct judgments.

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Xiaomin Chu, Feng Jiang, Yi Zhou, Guodong Zhou, Qiaoming Zhu

Hyperbolic Relevance Matching for Neural Keyphrase Extraction (2022.naacl-main)

Challenge:	Discourse parsing is a challenging task and plays a critical role in discourse analysis.
Approach:	They propose a macro discourse structure presentation schema to present the macro level discourse structure analysis.
Outcome:	The proposed corpus is based on two tasks of macro discourse structure analysis, including structure identification and nuclearity recognition.

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Mingyang Song, Yi Feng, Liping Jing

ChatVLA: Unified Multimodal Understanding and Robot Control with Vision-Language-Action Model (2025.emnlp-main)

Challenge:	Keyphrase extraction is a fundamental task in natural language processing that aims to extract a set of phrases with important information from a source document.
Approach:	They propose a hyperbolic matching model to explore keyphrase extraction in hyperbolical space using word embeddings from RoBERTa to capture hierarchical syntactic and semantic structures.
Outcome:	The proposed model outperforms the state-of-the-art models on six benchmark datasets and outperformed previous models.

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Zhongyi Zhou, Yichen Zhu, Minjie Zhu, Junjie Wen, Ning Liu, Zhiyuan Xu, Weibin Meng, Yaxin Peng, Chaomin Shen, Feifei Feng, Yi Xu

UniCorn: Towards Self-Improving Unified Multimodal Models through Self-Generated Supervision (2026.acl-long)

Challenge:	Recent advances in vision-language-action models prioritize robotic action mastery . however, models trained on visual-text pairs struggle to interpret multimodal data .
Approach:	They propose a framework that integrates multimodal data after initial control mastery and a Mixture-of-Experts architecture to minimize task interference.
Outcome:	The proposed framework surpasses state-of-the-art vision-language-action (VLA) methods on multimodal understanding benchmarks and achieves six times higher performance on visual question-answering datasets.

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Zhen Fang, Ruiyan Han, XinYu Sun, Yuchen Ma, Ziheng Wang, Yu Zeng, Zehui Chen, Lin Chen, Wenxuan Huang, Wei-Jie Xu, Yi Cao, Feng Zhao

A Survey on Recent Advances in Keyphrase Extraction from Pre-trained Language Models (2023.findings-eacl)

Challenge:	Unified Multimodal Models have achieved remarkable success in cross-modal comprehension, but a gap persists in their ability to translate internal knowledge into faithful and controllable synthesis.
Approach:	They propose a self-improvement framework that partitions a single UMM into three collaborative roles: Proposer, Solver, and Judge.
Outcome:	The proposed framework improves on TIIF, DPG, CompBench and UniCycle benchmarks.

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Mingyang Song, Yi Feng, Liping Jing

LLM as a metric critic for low resource relation identification (2024.findings-emnlp)

Challenge:	Keyphrase extraction is a key component in Natural Language Processing (NLP) systems for selecting a set of phrases from the document that could summarize the important information discussed in the source document.
Approach:	They propose to use supervised and unsupervised keyphrase extraction techniques to investigate the state-of-the-art models for keyphrase extracting.
Outcome:	The proposed keyphrase extraction system can significantly accelerate the speed of retrieval and help people get first-hand information from a long document quickly and accurately.

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Zhe Yang, Yi Huang, Yaqin Chen, Xiaoting Wu, Junlan Feng, Chao Deng

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

Challenge:	Existing studies show that small language models (SLMs) overfit in low resource situations . however, the gap between pre-training and fine-tuning leads to performance decay .
Approach:	They propose to combine large language models and LLM for relation identification by co-evolution . they propose to use a masked language model prompt to generate a relation identification task .
Outcome:	The proposed model can handle low resource relation identification tasks with minimal overfitting . the proposed model provides essential background knowledge to assist training process .

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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

Cognitive Alpha Mining via LLM-Driven Code-Based Evolution (2026.acl-long)

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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Fengyuan Liu, Yi Huang, Sichun Luo, Yuqi Wang, Yazheng Yang, Xinye Li, Zefa Hu, Junlan Feng, Qi Liu

Improving Embedding-based Unsupervised Keyphrase Extraction by Incorporating Structural Information (2023.findings-acl)

Challenge:	Existing approaches to finding effective predictive signals from financial data are limited by their complexity and low signal-to-noise ratio.
Approach:	They propose a framework that combines code-level alpha representation with LLM-driven reasoning and evolutionary search.
Outcome:	The proposed framework combines code-level alpha representation with LLM-driven reasoning and evolutionary search.

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Mingyang Song, Huafeng Liu, Yi Feng, Liping Jing

Responsible & Trustworthy NLP

Challenge:	Existing unsupervised keyphrase extraction models ignore the indicative role of the highlights in certain locations, leading to wrong keyphrases extraction.
Approach:	They propose a Highlight-Guided Unsupervised Keyphrase Extraction model that models phrase-document relevance via the highlights of documents and calculates cross-phrase relevance between all candidate phrases.
Outcome:	The proposed model outperforms the state-of-the-art unsupervised keyphrase extraction models on three benchmarks.

CMDL: A Large-Scale Chinese Multi-Defendant Legal Judgment Prediction Dataset (2024.findings-acl)

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Wanhong Huang, Yi Feng, Chuanyi Li, Honghan Wu, Jidong Ge, Vincent Ng

Challenge:	Legal Judgment Prediction (LJP) has attracted significant attention in recent years.
Approach:	They propose a large-scale Chinese Multi-Defendant LJP dataset . they propose case-level evaluation metrics dedicated for the multi-defendant scenario .
Outcome:	The proposed methods show weaknesses when applied to cases involving multiple defendants.

EAVE: Efficient Product Attribute Value Extraction via Lightweight Sparse-layer Interaction (2024.findings-emnlp)

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Li Yang, Qifan Wang, Jianfeng Chi, Jiahao Liu, Jingang Wang, Fuli Feng, Zenglin Xu, Yi Fang, Lifu Huang, Dongfang Liu

Beyond Static Profiles: Capturing the Fluidity of User Preferences in Diverse Scenarios (2026.findings-acl)

Challenge:	Existing methods to extract product attribute value require multiple extractions to obtain all corresponding values.
Approach:	They propose an Efficient product Attribute Value Extraction approach using lightweight sparse-layer interaction.
Outcome:	The proposed method achieves significant efficiency gains with neutral or marginal loss in performance when the context is long and number of attributes is large.

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Chunyang Gao, Yi Huang, Jingyu Yao, Xiaoting Wu, Junlan Feng

Challenge:	Existing approaches to personalize Large Language Models often default to homogeneous behaviors . preferences can shift, and conflict, depending on context, authors argue .
Approach:	They propose a hierarchical taxonomy to differentiate between stable and situational preferences . they use a dataset of 10k meticulously curated preferences to test their taxonomies .
Outcome:	The proposed model differentiates between stable and situational preferences based on curated user preferences . it provides a practical testbed for advancing dynamic, context-aware personalization in conversational agents.

Entriever: Energy-based Retriever for Knowledge-Grounded Dialog Systems (2025.findings-acl)

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Yucheng Cai, Ke Li, Yi Huang, Junlan Feng, Zhijian Ou

Rethinking Multimodal Entity and Relation Extraction from a Translation Point of View (2023.acl-long)

Challenge:	Existing knowledge retrieval methods fail to account for interrelationship between knowledge pieces . however, current methods fail in a situation where multiple knowledge pieces are relevant .
Approach:	They propose an energy-based retriever that directly models the candidate retrieval results as a whole instead of modeling the knowledge pieces separately.
Outcome:	The proposed retriever outperforms the baseline energy-based retriever in knowledge retrieval tasks.

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Changmeng Zheng, Junhao Feng, Yi Cai, Xiaoyong Wei, Qing Li

Multilinguality

Challenge:	Special attention is paid to the cross-modal misalignment in text-image datasets which may mislead the learning.
Approach:	They propose a multimodal back-translation method which uses diffusion-based generative models for pseudo-paralleled pairs and a divergence estimator to construct a high-resource corpora as a bridge for low-ressource learners.
Outcome:	The proposed method outperforms 14 state-of-the-art methods in both entity and relation extraction tasks.

Multimodal Neural Machine Translation: A Survey of the State of the Art (2025.emnlp-main)

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Yi Feng, Chuanyi Li, Jiatong He, Zhenyu Hou, Vincent Ng

Multilinguality

Challenge:	Multimodal neural machine translation (MNMT) is a task that aims to translate text into the target language using neural networks.
Approach:	They propose to integrate other modalities with textual data to enhance translation performance.
Outcome:	The proposed task aims to integrate visual modality with textual data to improve translation quality.

Learning to Leverage High-Order Medical Knowledge Graph for Joint Entity and Relation Extraction (2023.findings-acl)

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Zhe Yang, Yi Huang, Junlan Feng

AlphaQT-Bench: Diagnosing the Gap between Financial Code Generation and Quantitative Reasoning in LLMs (2026.findings-acl)

Challenge:	Medical terms are difficult to understand and relations between medical entities become complicated.
Approach:	They propose to leverage medical domain knowledge for extracting entities and relations for Chinese medical texts by building a heterogeneous graph based on medical knowledge graph.
Outcome:	The proposed method is more effective than state-of-the-art methods on real Chinese medical texts.

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Sichun Luo, Yi Huang, Shichang Meng, Fengyuan Liu, Mukai Li, Qinghua Yao, Zefa Hu, Junlan Feng, Qi Liu

Programming Languages in NLP

Challenge:	Existing benchmarks rely on outcome-driven metrics such as profitability and look-ahead bias.
Approach:	They propose a diagnostic benchmark for instruction-grounded financial code generation under strict semantic and temporal constraints.
Outcome:	The proposed benchmarks show that the models fail under causal, structural, or functional constraints.

Investigating Human and LLMs’ Decisions in Unverifiable Environments: A Case Study with GitHub Activity Overview (2026.findings-acl)

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Zheng Jiang, Wei Wang, Gaowei Zhang, Yang Feng, Yi Wang

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

Challenge:	examining the behaviors of Large Language Models as artificial social actors is underexplored, especially in unverifiable scenarios where conventional benchmarking has little to help improve their abilities.
Approach:	They propose a method to collect, compare, and reason about human and LLMs' decisions in an unverifiable scenario and use it to examine their behaviors.
Outcome:	The proposed method compared human and LLM decisions in an unverifiable scenario on GitHub and found that proprietary LLMs behave more like humans than open-source LLM systems.

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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 Entity Linking through Semantic Reinforced Entity Embeddings (2020.acl-main)

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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Feng Hou, Ruili Wang, Jun He, Yi Zhou

Thinking Alignment of Scenario-Oriented User Simulation (2026.acl-long)

Challenge:	Existing entity embeddings are effective, but too distinctive for linking models to learn contextual commonality.
Approach:	They propose a method to inject fine-grained semantic information into entity embeddings . they use word embedds of type words to generate semantic embeddngs based on existing embeddables a sample of semantic information is injected into the embedded entities .
Outcome:	The proposed method reduces the distinctiveness of existing embeddings and improves performance.

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Xiaoting Wu, Yi Huang, Chunyang Gao, Mengfei Guo, Jingyu Yao, Junlan Feng

CGBridge: Bridging Code Graphs and Large Language Models for Better Structure-Aware Code Understanding (2026.findings-acl)

Challenge:	Existing user simulators based on prompting to role-play or SFT focus on imitating textual utterances without considering multi-faceted cognitive processes that underlie human decision-making during interactions.
Approach:	They construct a user-simulator dataset that augments 51k human–LLM conversations by reconstructing the user’s inner reasoning during and at the end of each dialogue.
Outcome:	The proposed user simulators augment 51k human–LLM conversations by reconstructing the user’s inner reasoning both during and at the end of each dialogue.

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Zeqi Chen, Zhaoyang Chu, Yi Gui, Feng Guo, Yao Wan, Chuan Shi

Structured List-Grounded Question Answering (2025.coling-main)

Challenge:	Existing structure-aware approaches treat structure as serialized text prompts or auxiliary training objectives, failing to provide explicit guidance during inference.
Approach:	They propose a plug-and-play method that enhances Large Language Models with Code Graph information through an external, trainable Bridge module.
Outcome:	The proposed method decouples structural reasoning from textual generation without updating the backbone.

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Mujeen Sung, Song Feng, James Gung, Raphael Shu, Yi Zhang, Saab Mansour

Natural Language Interfaces

Challenge:	Document-grounded dialogue systems aim to answer user queries by leveraging external information.
Approach:	They propose a dataset to evaluate QA systems' ability to interpret and use structured lists . they use language models and model-based filtering processes to enhance data quality .
Outcome:	The proposed model outperforms baselines on the LIST2QA dataset . it shows that the proposed model is more accurate and complete than baselines .

MathFlow: Enhancing the Perceptual Flow of MLLMs for Visual Mathematical Problems (2026.acl-long)

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Shuhang Chen, Hangjie Yuan, Yunqiu Xu, Pengwei Liu, Tao Feng, Jun Cen, Zeying Huang, Yi Yang

Generation Meets Verification: Accelerating Large Language Model Inference with Smart Parallel Auto-Correct Decoding (2024.findings-acl)

Challenge:	Existing multimodal large language models (MLLMs) exhibit significant limitations when extracting essential information and reasoned properties from diagrams and performing complex reasoning based on these visual inputs.
Approach:	They propose a benchmark that provides a fine-grained evaluation of MLLMs’ perception and reasoning capabilities.
Outcome:	The proposed benchmark shows that existing MLLMs exhibit limitations when extracting essential information and reasoned properties from diagrams and performing complex reasoning based on these visual inputs.

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Hanling Yi, Feng Lin, Hongbin Li, Ning Peiyang, Xiaotian Yu, Rong Xiao