Papers by Yifei Yuan

Chain of Ideas: Revolutionizing Research Via Novel Idea Development with LLM Agents (2025.findings-emnlp)

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Long Li, Weiwen Xu, Jiayan Guo, Ruochen Zhao, Xingxuan Li, Yuqian Yuan, Boqiang Zhang, Yuming Jiang, Yifei Xin, Ronghao Dang, Yu Rong, Deli Zhao, Tian Feng, Lidong Bing

Challenge:	Existing methods for idea generation either trivially prompt LLMs or expose LLM to extensive literature without indicating useful information.
Approach:	They propose a chain-of-ideas agent that organizes literature in a chains structure . they propose evaluating idea-generation methods from different perspectives .
Outcome:	The proposed agent outperforms existing methods and matches human quality in idea generation.

Topology Matters: Measuring Memory Leakage in Multi-Agent LLMs (2026.findings-acl)

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Jinbo Liu, Defu Cao, Yifei Wei, Tianyao Su, Yuan Liang, Yushun Dong, Yan Liu, Yue Zhao, Xiyang Hu

Robustness in NLP

Challenge:	Multi-agent LLMs are rapidly moving from prototype to real-world use . network topology is a first-order security parameter in multi-aggent systems .
Approach:	They propose a framework for comparing topology-conditioned memory leakage in multi-agent LLM systems.
Outcome:	The proposed framework evaluates topology-conditioned memory leakage in multi-agent LLM systems.

Knowledge-enhanced Mixed-initiative Dialogue System for Emotional Support Conversations (2023.acl-long)

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Yang Deng, Wenxuan Zhang, Yifei Yuan, Wai Lam

Natural Language Interfaces

Challenge:	Experimental results show the superiority of a mixed-initiative framework for emotional support conversation (ESC) ESC systems are emerging to provide prompt and convenient emotional support for helpseekers, including mental health support, counseling or motivational interviewing.
Approach:	They propose a knowledge-enhanced mixed-initiative framework that retrieves actual case knowledge from a large-scale mental health knowledge graph for generating mixed-initiative responses.
Outcome:	The proposed framework retrieves actual case knowledge from a large-scale mental health knowledge graph for generating mixed-initiative responses.

What if Othello-Playing Language Models Could See? (2025.findings-emnlp)

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Xinyi Chen, Yifei Yuan, Jiaang Li, Serge Belongie, Maarten de Rijke, Anders Søgaard

Semantic Text Processing

Challenge:	a multi-modal model trained on move sequences and board images is a popular testbed for language models .
Approach:	They propose a multi-modal model trained jointly on move sequences and board images.
Outcome:	The proposed multi-modal model trains on move sequences and board images.

Identifying the Achilles’ Heel: An Iterative Method for Uncovering Factual Errors in Large Language Models (2026.findings-acl)

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Wenxuan Wang, Yuk-Kit Chan, Zixuan Ling, Shi Juluan, Youliang Yuan, Jen-tse Huang, Yifei Zhang, Wenxiang Jiao, Zhaopeng Tu, Michael R. Lyu

Language Models

Challenge:	Current methods for evaluating LLMs’ veracity are limited by the need for extensive human labor, test data contamination, or limited scope, hindering efficient and effective exposure of errors.
Approach:	They propose a framework that extracts fact triplets to generate diverse question types using rule-based natural language processing techniques.
Outcome:	The proposed framework can trigger factual errors in up to 55% of questions in large LLMs while maintaining coverage of questions.

McQueen: a Benchmark for Multimodal Conversational Query Rewrite (2022.emnlp-main)

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Yifei Yuan, Chen Shi, Runze Wang, Liyi Chen, Feijun Jiang, Yuan You, Wai Lam

Natural Language Interfaces

Challenge:	Recent studies have focused on conversational-related tasks that involve drawing information from more than one modality.
Approach:	They propose a task of multimodal conversational query rewrite which performs query . they collect a large-scale visual conversation dataset and benchmark it against other tasks .
Outcome:	The proposed task performs on a large-scale visual conversation dataset . it eliminates coreference and ellipsis in the original query without changing its semantic information.

CtrlNews: LLM-based Multi-Agent Controllable News Writing via Knowledge Gravitational Field (2025.findings-emnlp)

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Yifei Xu, Yingjie Zong, Wang Zhonghua, Sirui Wu, Yuan Rao, Dan Zhang, Shuiguang Deng

Challenge:	Current approaches to news writing rely on superficially retrieved information and oversimplified knowledge enumeration resulting in shallow, repetitive, and unordered outputs.
Approach:	They propose an LLM-based multi-agent controllable news writing framework called CtrlNews . they propose a fine-grained viewpoint control mechanism to regulate bias, emotion, and exaggeration attributes.
Outcome:	The proposed framework simulates expert questioning through automated role assignment and question generation followed by a three-layer hierarchical gravitational graph iteratively refined via expansion-reflection cycles.

JX4MEI: Multimodal Semantically-Enhanced LLM for Joint Multimodal Emotion-Intent Explanation and Classification (2026.findings-acl)

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YiJie Huang, Xiaocui Yang, Shi Feng, Daling Wang, Yifei Zhang, Ning Yuan, Zhuoyue Jia, Wen Zhang

Information Retrieval

Challenge:	Existing multimodal emotion and intent recognition tasks focus on classification, not rationale and intrinsic connections between these states.
Approach:	They propose a task that requires models to jointly predict emotion and intent while generating natural language explanations for why they co-occur.
Outcome:	The proposed model outperforms baseline models in prediction and explanation generation.

Unlocking Markets: A Multilingual Benchmark to Cross-Market Question Answering (2024.emnlp-main)

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Yifei Yuan, Yang Deng, Anders Søgaard, Mohammad Aliannejadi

Natural Language Interfaces

Challenge:	Product-related question answering (PQA) involves utilizing product-related resources to provide precise answers to users.
Approach:	They propose a task of multilingual cross-market product-based question answering that combines product-related questions with product-specific questions from a multilingual marketplace.
Outcome:	The proposed task provides answers to product-related questions in a multilingual marketplace even in fewer languages.

FoodieQA: A Multimodal Dataset for Fine-Grained Understanding of Chinese Food Culture (2024.emnlp-main)

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Wenyan Li, Crystina Zhang, Jiaang Li, Qiwei Peng, Raphael Tang, Li Zhou, Weijia Zhang, Guimin Hu, Yifei Yuan, Anders Søgaard, Daniel Hershcovich, Desmond Elliott

Multimodality

Challenge:	FoodieQA is a manually curated, fine-grained image-text dataset capturing the intricate features of food cultures across various regions in China.
Approach:	They evaluate vision–language Models and large language models on unseen food images and corresponding questions.
Outcome:	The proposed dataset evaluates vision–language Models and large language models on unseen food images and corresponding questions.

Improving Role-Oriented Dialogue Summarization with Interaction-Aware Contrastive Learning (2024.lrec-main)

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Weihong Guan, Shi Feng, Daling Wang, Faliang Huang, Yifei Zhang, Yuan Cui

Semantic Text Processing

Challenge:	Existing methods for encoding dialogues do not capture interaction information between roles, thus ignore interaction-related key information.
Approach:	They propose a contrastive learning based interaction-aware model for the role-oriented dialogue summarization namely CIAM and use it to train the decoder to learn role-level interaction.
Outcome:	The proposed model captures interaction information between different roles and produces informative summaries on two public datasets.

Point-of-Interest Oriented Question Answering with Joint Inference of Semantic Matching and Distance Correlation (2020.aacl-main)

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Yifei Yuan, Jingbo Zhou, Wai Lam

Question Answering

Challenge:	Existing methods for POI oriented question answering lack ability to handle important POI related information.
Approach:	They propose a deep learning framework integrated with joint inference to capture tag semantic and geographic correlation between question and POIs.
Outcome:	The proposed model captures both tag semantic and geographic correlation between question and POIs.

Summarize-Exemplify-Reflect: Data-driven Insight Distillation Empowers LLMs for Few-shot Tabular Classification (2025.findings-emnlp)

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Yifei Yuan, Jiatong Li, Weijia Zhang, Mohammad Aliannejadi, Evangelos Kanoulas, Renjun Hu

Information Extraction & Text Mining

Challenge:	Recent studies show the promise of large language models for few-shot tabular classification but highlight challenges due to the variability in structured data.
Approach:	They propose a framework that distills data into actionable insights to enable robust and effective classification by large language models.
Outcome:	The proposed framework integrates rule summarization, strategic exemplification, and insight reflection through deep collaboration between LLMs and data modeling techniques.

CO3: Low-resource Contrastive Co-training for Generative Conversational Query Rewrite (2024.lrec-main)

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Yifei Yuan, Chen Shi, Wang Runze, Liyi Chen, Renjun Hu, Zengming Zhang, Feijun Jiang, Wai Lam

Responsible & Trustworthy NLP

Challenge:	Recent advances in conversational IR systems have seen a resurgent interest in conversation . generative query rewrite generates reconstructed query based on the conversation history .
Approach:	They propose to use unlabeled data to make further improvements using contrastive co-training paradigm.
Outcome:	The proposed model is robust to noise and language style shift under few-shot and zero-shot scenarios.

Aspect Sentiment Quad Prediction as Paraphrase Generation (2021.emnlp-main)

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Wenxuan Zhang, Yang Deng, Xin Li, Yifei Yuan, Lidong Bing, Wai Lam

Sentiment Analysis

Challenge:	Existing studies focus on predicting the four elements in one shot, instead of predicting them all.
Approach:	They propose a task to jointly detect all sentiment elements in quads for a given opinionated sentence.
Outcome:	The proposed method can generate the semantics of the sentiment elements in the natural language form.

RefGPT: Dialogue Generation of GPT, by GPT, and for GPT (2023.findings-emnlp)

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Dongjie Yang, Ruifeng Yuan, Yuantao Fan, Yifei Yang, Zili Wang, Shusen Wang, Hai Zhao

Dialogue Systems & Conversational Agents

Challenge:	Large Language Models (LLMs) have impressive capability to resolve a wide range of NLP tasks by fine-tuning high-quality instruction data.
Approach:	They propose a method to generate huge truthful and customized dialogues without worrying about factual errors caused by the model hallucination.
Outcome:	The proposed method solves the model hallucination in dialogue generation by restricting the LLMs to leverage the given reference instead of reciting their own knowledge to generate dialogues.

BERT-BC: A Unified Alignment and Interaction Model over Hierarchical BERT for Response Selection (2024.lrec-main)

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Zhenfei Yang, Beiming Yu, Yuan Cui, Shi Feng, Daling Wang, Yifei Zhang

Dialogue Systems & Conversational Agents

Challenge:	Recent performance boosting for dialogue response selection task achieved by Cross-Encoder based models is limited and the learned models have poor generalization capability in realistic scenarios.
Approach:	They propose a model that combines the representation-based Bi-Encoder and interaction-based Cross-Encoding to achieve better semantic representation.
Outcome:	The proposed model can achieve state-of-the-art performance on three benchmark datasets for multi-turn response selection.

On the Multi-turn Instruction Following for Conversational Web Agents (2024.acl-long)

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Yang Deng, Xuan Zhang, Wenxuan Zhang, Yifei Yuan, See-Kiong Ng, Tat-Seng Chua

Natural Language Interfaces

Challenge:	Large Language Models (LLMs) have demonstrated remarkable abilities in planning and executing multi-step interactions within web-based environments.
Approach:	They propose a framework for conversational web navigation that uses multi-turn interactions with both the user and the environment.
Outcome:	The proposed framework is based on a multi-turn Mind2Web (MT-Mind2Web) it is designed to perform multi-step interactions with web-based environments .