Papers by Kam-Fai Wong

Improving Factual Consistency for Knowledge-Grounded Dialogue Systems via Knowledge Enhancement and Alignment (2023.findings-emnlp)

Challenge:	Existing text-based recommendation frameworks that use pretrained language models (PLMs) can improve performance on text-related tasks.
Approach:	They propose a unified local- and global-attention Transformer encoder to better model two-level contexts of user history.
Outcome:	The proposed framework improves on three text-based recommendation tasks.

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Boyang Xue, Weichao Wang, Hongru Wang, Fei Mi, Rui Wang, Yasheng Wang, Lifeng Shang, Xin Jiang, Qun Liu, Kam-Fai Wong

AppBench: Planning of Multiple APIs from Various APPs for Complex User Instruction (2024.emnlp-main)

Challenge:	Experimental results show that pretrained language models generate inconsistent factual knowledge in many conversational tasks.
Approach:	They propose a method which explicitly introduces extended feedforward networks (FFNs) in Transformers to enhance factual knowledge expressions given the specific patterns of knowledge-grounded dialogue inputs.
Outcome:	The proposed methods improve the factual expression capability of feedforward networks (FFNs) in knowledge-grounded dialogue systems by knowledge enhancement and alignment respectively.

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Hongru Wang, Rui Wang, Boyang Xue, Heming Xia, Jingtao Cao, Zeming Liu, Jeff Pan, Kam-Fai Wong

Structured Data in NLP

Challenge:	Existing state-of-the-art Large Language Models (LLMs) still cannot perform well in this situation even with the help of in-context learning and finetuning.
Approach:	They propose a benchmark to evaluate LLMs’ ability to plan and execute multiple APIs from various sources in order to complete the user’s task.
Outcome:	The proposed benchmarks show that the existing state-of-the-art LLMs still cannot perform well in this situation even with in-context learning and finetuning.

Steering Knowledge Selection Behaviours in LLMs via SAE-Based Representation Engineering (2025.naacl-long)

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Yu Zhao, Alessio Devoto, Giwon Hong, Xiaotang Du, Aryo Pradipta Gema, Hongru Wang, Xuanli He, Kam-Fai Wong, Pasquale Minervini

Multi-modal Stance Detection: New Datasets and Model (2024.findings-acl)

Challenge:	Large language models store factual knowledge in their parameters but their parametric knowledge can conflict with the information provided in the context.
Approach:	They propose a training-free representation engineering method that uses pre-trained sparse auto-encoders to control the knowledge selection behaviour of large language models.
Outcome:	The proposed method can control the use of both knowledge sources to resolve knowledge conflict in open-domain question-answering tasks surpassing existing representation engineering methods (+10%) and contrastive decoding methods (+5%).

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Bin Liang, Ang Li, Jingqian Zhao, Lin Gui, Min Yang, Yue Yu, Kam-Fai Wong, Ruifeng Xu

Multimodality

Challenge:	Existing methods for stance detection for pure texts have limited results to multi-modal content.
Approach:	They propose a multi-modal stance detection framework that leverages target information to learn multi-modal stance features from textual and visual modalities.
Outcome:	The proposed framework achieves state-of-the-art in multi-modal stance detection on five datasets based on Twitter .

DPDLLM: A Black-box Framework for Detecting Pre-training Data from Large Language Models (2024.findings-acl)

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Baohang Zhou, Zezhong Wang, Lingzhi Wang, Hongru Wang, Ying Zhang, Kehui Song, Xuhui Sui, Kam-Fai Wong

Guaranteeing Knowledge Integration with Joint Decoding for Retrieval-Augmented Generation (2026.acl-long)

Challenge:	Existing methods to detect pretraining data from large language models are unrealistic to them.
Approach:	They propose to detect pre-training data from LLM in a black-box way by using GPT-2 as reference model and feed it with sequence probabilities to detect whether it was used to train it.
Outcome:	The proposed framework outperforms existing methods on the benchmark datasets and shows that it is effective on different popular LLMs.

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Zhengyi Zhao, Shubo Zhang, Zezhong Wang, Yuxi Zhang, Huimin Wang, Yutian Zhao, Yefeng Zheng, Binyang Li, Kam-Fai Wong, Xian Wu

Learning When and What to Quote: A Quotation Recommender System with Mutual Promotion of Recommendation and Generation (2022.findings-emnlp)

Challenge:	Retrieval-Augmented Generation (RAG) provides access to external knowledge, but current research focuses on retrieval quality and 'integration bottleneck' .
Approach:	They propose a framework that explicitly decouples reasoning from evidence integration by generating an 'Inner-Answer' and a 'Refer-Aswer" they propose 'a joint decoding mechanism that dynamically fuses the logical coherence of the Inner-Andswer with the factual precision of the Refer-Adswer at the token level'
Outcome:	The proposed framework improves accuracy by 12.1% and reduces hallucinations by 16.3% on five QA benchmarks.

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Lingzhi Wang, Xingshan Zeng, Kam-Fai Wong

Challenge:	Existing quotation recommendation system focuses on what to quote, but ignores whether or when to quote.
Approach:	They propose a framework that learns to predict when to quote and what to quote jointly.
Outcome:	The proposed framework achieves significantly better performance than baselines on two datasets.

MlingConf: A Comprehensive Study of Multilingual Confidence Estimation on Large Language Models (2025.findings-acl)

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Boyang Xue, Hongru Wang, Rui Wang, Sheng Wang, Zezhong Wang, Yiming Du, Bin Liang, Wenxuan Zhang, Kam-Fai Wong

Robust Tool Use via Fission-GRPO: Learning to Recover from Execution Errors (2026.acl-long)

Challenge:	Existing studies on LLM confidence estimations in languages other than English have been limited to English.
Approach:	They propose to use question-related language to prompt LLMs to assess their confidence in large language models.
Outcome:	The proposed model improves on question-related language prompts for LS tasks, while English exhibits notable linguistic dominance in confidence estimations.

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Zhiwei Zhang, Fei Zhao, Rui Wang, Zezhong Wang, Bin Liang, Jiakang Wang, Yao Hu, Shaosheng Cao, Kam-Fai Wong

Robustness in NLP

Challenge:	Large language models (LLMs) can call tools effectively, but they remain brittle in multi-turn execution.
Approach:	They propose a framework that converts execution errors into on-policy corrective supervision within the RL training loop.
Outcome:	The proposed framework improves the error recovery rate of Qwen3-8B by 5.7% absolute and overall accuracy by 4.0% on BFCL v4 Multi-Turn.

Learning Efficient Dialogue Policy from Demonstrations through Shaping (2020.acl-main)

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Huimin Wang, Baolin Peng, Kam-Fai Wong

MemeReaCon: Probing Contextual Meme Understanding in Large Vision-Language Models (2025.emnlp-main)

Challenge:	Using reinforcement learning to learn dialogue policy requires a large volume of interactions with users.
Approach:	They propose a task-oriented dialogue agent that efficiently learns dialogue policy from demonstrations . they use an imitation model to distill knowledge from demonstration and reward shaping .
Outcome:	The proposed agent efficiently learns dialogue policy from demonstrations through policy shaping and reward shaping.

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Zhengyi Zhao, Shubo Zhang, Yuxi Zhang, Yanxi Zhao, Yifan Zhang, Zezhong Wang, Huimin Wang, Yutian Zhao, Bin Liang, Yefeng Zheng, Binyang Li, Kam-Fai Wong, Xian Wu

Multimodality

Challenge:	Current approaches focus on isolated meme analysis, either for harmful content detection or standalone interpretation, overlooking a fundamental challenge: the same meme can express different intents depending on its conversational context.
Approach:	They propose a benchmark to evaluate how large vision language models understand memes in their original context.
Outcome:	The proposed benchmark evaluates how large vision language models understand meme intent in their original context.

Dynamic Online Conversation Recommendation (2020.acl-main)

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Xingshan Zeng, Jing Li, Lu Wang, Zhiming Mao, Kam-Fai Wong

RecInDial: A Unified Framework for Conversational Recommendation with Pretrained Language Models (2022.aacl-main)

Challenge:	Existing models that assume static user interests are unable to capture the temporal aspects of user interactions and interest changes over time.
Approach:	They propose a neural architecture to exploit changes of user interactions and interests over time to predict which discussions they are likely to enter.
Outcome:	The proposed model outperforms state-of-the-art models that assume static user interests and handle future conversations that are unseen during training time.

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Lingzhi Wang, Huang Hu, Lei Sha, Can Xu, Daxin Jiang, Kam-Fai Wong

Coupling Global and Local Context for Unsupervised Aspect Extraction (D19-1)

Challenge:	Existing generative methods to recommend items are shallowly integrated into the model training and have poor chit-chat ability.
Approach:	They propose a framework that integrates recommendation into the dialog generation by introducing a vocabulary pointer.
Outcome:	The proposed framework outperforms the state-of-the-art models on a benchmark dataset.

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Ming Liao, Jing Li, Haisong Zhang, Lingzhi Wang, Xixin Wu, Kam-Fai Wong

Fast and Scalable Dialogue State Tracking with Explicit Modular Decomposition (2021.naacl-main)

Challenge:	Existing studies on aspect extraction focus on sequence tagging models trained on human-annotated data.
Approach:	They propose a novel neural model capable of coupling global and local representations to discover aspect words by combining global and locale contexts.
Outcome:	The proposed model outperforms state-of-the-art models on laptop and restaurant reviews on two benchmarks.

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Dingmin Wang, Chenghua Lin, Qi Liu, Kam-Fai Wong

Understanding and Mitigating Bias Inheritance in LLM-based Data Augmentation on Downstream Tasks (2026.acl-long)

Challenge:	Existing approaches for dialogue state tracking are mainly based on classification-based and extraction-based methods.
Approach:	They propose a model which incorporates both classification-based and extraction-based methods and integrates four modules to jointly extract dialogue states.
Outcome:	The proposed model outperforms the state-of-the-art models in multi-domain dialogues with many turns of utterances.

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Miaomiao Li, Hao Chen, Yang Wang, Tingyuan Zhu, Weijia Zhang, Kaijie Zhu, Kam-Fai Wong, Jindong Wang

IndiVec: An Exploration of Leveraging Large Language Models for Media Bias Detection with Fine-Grained Bias Indicators (2024.findings-eacl)

Challenge:	Generating synthetic datasets via large language models (LLMs) has emerged as promising approach to improve LLM performance.
Approach:	They propose three mitigation strategies to mitigate bias inheritance in LLMs by analyzing real and LLM-augmented data.
Outcome:	The proposed methods can work differently on different tasks and biases.

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Luyang Lin, Lingzhi Wang, Xiaoyan Zhao, Jing Li, Kam-Fai Wong

Large Language Models as Source Planner for Personalized Knowledge-grounded Dialogues (2023.findings-emnlp)

Challenge:	Existing studies on social media bias detection focus on fine-tuning models specific to particular datasets and testing them on corresponding test sets.
Approach:	They propose a general bias detection framework, IndiVec, built upon large language models and vector databases.
Outcome:	The proposed framework outperforms baseline methods on four political bias datasets and provides explicit top-k indicators to interpret bias predictions.

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Hongru Wang, Minda Hu, Yang Deng, Rui Wang, Fei Mi, Weichao Wang, Yasheng Wang, Wai-Chung Kwan, Irwin King, Kam-Fai Wong

Investigating Bias in LLM-Based Bias Detection: Disparities between LLMs and Human Perception (2025.coling-main)

Challenge:	Existing knowledge-grounded dialogue systems focus on a single knowledge source or ignore the dependency between multiple knowledge sources.
Approach:	They propose a framework that integrates multiple knowledge sources and dependencies between them.
Outcome:	The proposed framework can produce persona-consistent and knowledge-enhanced responses on a knowledge-grounded dialogue dataset.

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Luyang Lin, Lingzhi Wang, Jinsong Guo, Kam-Fai Wong

Cue-CoT: Chain-of-thought Prompting for Responding to In-depth Dialogue Questions with LLMs (2023.findings-emnlp)

Challenge:	Detecting media bias is critical due to the spread of misinformation and disinformation on social media platforms.
Approach:	They investigate the presence and nature of bias within large language models and its consequential impact on media bias detection.
Outcome:	The proposed debiasing strategies include prompt engineering and model fine-tuning.

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Hongru Wang, Rui Wang, Fei Mi, Yang Deng, Zezhong Wang, Bin Liang, Ruifeng Xu, Kam-Fai Wong

DIGAT: Modeling News Recommendation with Dual-Graph Interaction (2022.findings-emnlp)

Challenge:	Existing LLMs generate responses based on the dialogue context, overlooking the underlying linguistic cues about the user status exhibited in the context.
Approach:	They propose a linguistic cue-based chain-of-thoughts method which enhances the LLMs inference with an intermediate reasoning step to find cues exhibited in the dialogue.
Outcome:	The proposed method outperforms standard prompting methods on in-depth dialogue questions and linguistic cues exhibited in the context.

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Zhiming Mao, Jian Li, Hongru Wang, Xingshan Zeng, Kam-Fai Wong

Structured Data in NLP

Challenge:	Existing news recommendation methods lack effective news-user feature interaction.
Approach:	They propose to use news-graph and user-graph channels to enhance news encodings . they also propose to perform effective feature interaction between news and user graphs based on semantic-augmented graphs.
Outcome:	The proposed graph attention networks outperform existing NR methods on the benchmark dataset MIND.

Beyond Factuality: A Comprehensive Evaluation of Large Language Models as Knowledge Generators (2023.emnlp-main)

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Liang Chen, Yang Deng, Yatao Bian, Zeyu Qin, Bingzhe Wu, Tat-Seng Chua, Kam-Fai Wong

Sentence-Level Evidence Embedding for Claim Verification with Hierarchical Attention Networks (P19-1)

Challenge:	Large language models outperform information retrieval techniques for downstream knowledge-intensive tasks when being prompted to generate world knowledge.
Approach:	They propose a COmpreheNsive kNowledge Evaluation framework to evaluate generated knowledge from six important perspectives . they conduct extensive empirical analysis of generated knowledge on two widely studied knowledge-intensive tasks .
Outcome:	The proposed framework evaluates generated knowledge from six important perspectives on two knowledge-intensive tasks.

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Jing Ma, Wei Gao, Shafiq Joty, Kam-Fai Wong

Deep Dyna-Q: Integrating Planning for Task-Completion Dialogue Policy Learning (P18-1)

Challenge:	Claim verification is cumbersome and inefficient for human fact-checkers to find consistent pieces of evidence.
Approach:	They propose an end-to-end hierarchical attention network that learns to represent coherent evidence and their semantic relatedness with the claim.
Outcome:	The proposed model outperforms state-of-the-art models on three datasets . it is based on a coherence-based attention layer and entailment-based one .

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Baolin Peng, Xiujun Li, Jianfeng Gao, Jingjing Liu, Kam-Fai Wong

Mitigating Context Interference for Reliable and Efficient Search Agents (2026.acl-long)

Challenge:	Training a task-completion dialogue agent via reinforcement learning (RL) is costly because it requires many interactions with real users.
Approach:	They propose a framework that integrates planning for task-completion dialogue policy learning into a dialogue agent using a world model to mimic real user response and generate simulated experience.
Outcome:	The proposed framework integrates planning for task-completion dialogue policy learning with real user interaction and simulated user behavior.

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Boyang Xue, Bin Wu, Shuofei Qiao, Sheng Wang, Rui Wang, Yiming Du, Hongru Wang, Jeff Z. Pan, Emine Yilmaz, Kam-Fai Wong, Aldo Lipani

Green & Sustainable NLP

Challenge:	Recent research empowers Large Language Models (LLMs) as multi-turn search agents to iteratively retrieve and generate outputs until complex tasks are solved.
Approach:	They propose a distill-based context refiner to dynamically mitigate context interference . they also propose RLs that refine contexts to generate outputs .
Outcome:	The proposed refiner can mitigate context interference in multi-turn search agents.

COPR: Continual Human Preference Learning via Optimal Policy Regularization (2025.findings-acl)

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Han Zhang, Lin Gui, Yu Lei, Yuanzhao Zhai, Yehong Zhang, Zhuo Zhang, Yulan He, Hui Wang, Yue Yu, Kam-Fai Wong, Bin Liang, Ruifeng Xu

WebAggregator: Enhancing Compositional Reasoning Capabilities of Deep Research Agent Foundation Models (2026.acl-long)

Challenge:	Reinforcement Learning from Human Feedback (RLHF) is effective for aligning Large Language Models with human preferences, but its complex process limits its ability to continually learn human feedback.
Approach:	They propose a non-RL offline method to convert historical optimal policies into optimization constraints when continually learning new preferences.
Outcome:	The proposed method outperforms strong CL baselines in terms of reward-based evaluations and human assessment.

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Rui Wang, Ce Zhang, Jun-Yu Ma, Jianshu Zhang, Hongru Wang, Yi Chen, Boyang Xue, Tianqing Fang, Zhisong Zhang, Hongming Zhang, Haitao Mi, Dong Yu, Kam-Fai Wong

Stepwise Reasoning Checkpoint Analysis: A Test Time Scaling Method to Enhance LLMs’ Reasoning (2025.emnlp-main)

Challenge:	Existing agentic systems are retrieval-heavy but reasoning-light . current systems lack compositional reasoning, a key component of deep research .
Approach:	They propose a data synthesis pipeline WebAggregator to shift agentic paradigm . they use Proactive Explorer to collect interconnected knowledge and Compositional Logic Proposer to weave knowledge into complex questions .
Outcome:	The proposed pipeline surpasses GPT-4.1 and matches Claude-3.7-Sonnet on GAIA, WebWalkerQA, and XBench.

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Zezhong Wang, Xingshan Zeng, Weiwen Liu, Yufei Wang, Liangyou Li, Yasheng Wang, Lifeng Shang, Xin Jiang, Qun Liu, Kam-Fai Wong

Strategize Before Teaching: A Conversational Tutoring System with Pedagogy Self-Distillation (2023.findings-eacl)

Challenge:	Existing methods that use Chain-of-Thought suffer from path homogenization and inefficient use of intermediate results.
Approach:	They propose a framework that introduces checkpoints between reasoning steps to reduce path homogenization and create fault-tolerant mechanisms.
Outcome:	The proposed framework reduces path homogenization and creates fault-tolerant mechanism by utilizing high-quality intermediate results.

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Lingzhi Wang, Mrinmaya Sachan, Xingshan Zeng, Kam-Fai Wong

Neural News Recommendation with Collaborative News Encoding and Structural User Encoding (2021.findings-emnlp)

Challenge:	Conversational tutoring systems (CTSs) aim to help students master educational material with natural language interaction in the form of a dialog.
Approach:	They propose to jointly predict teaching strategies and generate tutor responses accordingly to help students master educational material through dialog.
Outcome:	The proposed framework is based on three dialog tutoring datasets and is more realistic than previous models that generate responses given the strategies as input.

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Zhiming Mao, Xingshan Zeng, Kam-Fai Wong

Information Extraction & Text Mining

Challenge:	Existing news recommendation models encode news title and content separately without leveraging the structural correlation of user browsing histories to reflect user interests explicitly.
Approach:	They propose a news recommendation framework consisting of collaborative news encoding and structural user encode to enhance news and user representation learning.
Outcome:	The proposed framework improves the performance of news recommendation on the MIND dataset.

A Training-Free Debiasing Framework with Counterfactual Reasoning for Conversational Emotion Detection (2023.emnlp-main)

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Geng Tu, Ran Jing, Bin Liang, Min Yang, Kam-Fai Wong, Ruifeng Xu

Continuity of Topic, Interaction, and Query: Learning to Quote in Online Conversations (2020.emnlp-main)

Challenge:	Existing studies in Emotion Recognition in Conversations (ERC) focus on capturing context-sensitive and speaker-sensitive dependencies, ignoring the unintended dataset biases of data.
Approach:	They propose a training-free debiasing framework that extracts biases from the model by generating counterfactual utterances and contexts and mitigates them using simple yet empirically robust element-wise subtraction operations.
Outcome:	Experiments on three public datasets show that the proposed framework improves generalization ability and fairness across different ERC models.

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Lingzhi Wang, Jing Li, Xingshan Zeng, Haisong Zhang, Kam-Fai Wong

Enhancing Large Language Models Against Inductive Instructions with Dual-critique Prompting (2024.naacl-long)

Challenge:	Quotations are crucial for successful explanations and persuasions in interpersonal communications.
Approach:	They propose to use an encoder-decoder neural framework to continue the context with a quotation via language generation to capture latent topics, interactions with the dialogue history, and coherence to the existing contents.
Outcome:	The proposed model outperforms state-of-the-art models on two large-scale datasets in English and Chinese and shows that topic, interaction, and query consistency are helpful to learn how to quote in online conversations.

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Rui Wang, Hongru Wang, Fei Mi, Boyang Xue, Yi Chen, Kam-Fai Wong, Ruifeng Xu

Rumor Detection on Twitter with Tree-structured Recursive Neural Networks (P18-1)

Challenge:	Existing studies have focused on how LLMs handle inductive instructions, which may stem from users’ false beliefs or malicious intents.
Approach:	They propose a benchmark of Inductive Instructions where false knowledge is incorporated into instructions in multiple different styles.
Outcome:	The proposed model improves robustness against inductive instructions, despite different inductive styles and complexity.

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Jing Ma, Wei Gao, Kam-Fai Wong

Challenge:	Existing methods for detecting rumors are difficult to implement and require a lot of effort.
Approach:	They propose two recursive neural models that follow tweets' propagation layouts to learn discriminative features from tweets and generate more powerful representations for rumors detection.
Outcome:	The proposed models perform better than state-of-the-art approaches on two public Twitter datasets and show superior performance on detecting rumors at very early stage.

UniRetriever: Multi-task Candidates Selection for Various Context-Adaptive Conversational Retrieval (2024.lrec-main)

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Hongru Wang, Boyang Xue, Baohang Zhou, Rui Wang, Fei Mi, Weichao Wang, Yasheng Wang, Kam-Fai Wong

Low-Resource NLP

Challenge:	Existing methods for retrieving information from a large corpus of data are sub-optimal and low efficiency.
Approach:	They propose a multi-task framework that functions as a universal retriever for three dominant retrieval tasks during the conversation.
Outcome:	The proposed framework can perform persona selection, knowledge selection, and response selection tasks simultaneously.

Self-Reasoning Language Models: Unfold Hidden Reasoning Chains with Few Reasoning Catalyst (2025.findings-acl)

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Hongru Wang, Deng Cai, Wanjun Zhong, Shijue Huang, Jeff Z. Pan, Zeming Liu, Kam-Fai Wong

Learning First-Order Logic Rules for Argumentation Mining (2025.acl-long)

Challenge:	Recent studies have demonstrated that inference-time scaling increases performance of Large Language Models (LLMs) in various reasoning tasks such as mathematics and complex question answering by increasing the length of Chain-of-Thought (CoT).
Approach:	They propose a model which synthesizes longer CoT data and iteratively improves performance through self-training by incorporating a few demonstration examples.
Outcome:	The proposed model achieves an average improvement of more than +2.5 points across five reasoning tasks: MMLU, GSM8K, ARC-C, HellaSwag, and BBH on two backbone models.

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Yang Sun, Guanrong Chen, Hamid Alinejad-Rokny, Jianzhu Bao, Yuqi Huang, Bin Liang, Kam-Fai Wong, Min Yang, Ruifeng Xu

EventWeave: A Dynamic Framework for Capturing Core and Supporting Events in Dialogue Systems (2026.acl-long)

Challenge:	Argumentation Mining (AM) aims to extract argumentative structures from texts by identifying argumentation components (ACs) and their argumentative relations (ARs).
Approach:	They propose a First- Order Logic reasoning framework for AM to capture logical reasoning paths within argumentative texts.
Outcome:	The proposed framework outperforms strong baselines while significantly improving explainability.

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Zhengyi Zhao, Shubo Zhang, Yiming Du, Bin Liang, Baojun Wang, Zhongyang Li, Binyang Li, Kam-Fai Wong

LLMEdgeRefine: Enhancing Text Clustering with LLM-Based Boundary Point Refinement (2024.emnlp-main)

Challenge:	Existing dialogue systems process conversational turns in isolation, overlooking event structures that guide natural interactions.
Approach:	They propose a framework that explicitly models relationships between conversational events to generate more contextually appropriate dialogue responses.
Outcome:	Experiments on three dialogue datasets show that the proposed approach produces more natural responses while requiring less computational overhead.

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Zijin Feng, Luyang Lin, Lingzhi Wang, Hong Cheng, Kam-Fai Wong

Text Clustering

Challenge:	LLMEdgeRefine is an iterative clustering method enhanced by large language models . existing clustering methods struggle with domain-specific fine-tuning and outliers .
Approach:	They propose an iterative clustering method enhanced by large language models focusing on edge points refinement . authors propose to use LLMs to iterate clusters and iterating to improve semantic coherence .
Outcome:	The proposed method outperforms state-of-the-art methods and offers robustness, adaptability, and cost-efficiency for diverse text clustering applications.

SeRTS: Self-Rewarding Tree Search for Biomedical Retrieval-Augmented Generation (2024.findings-emnlp)

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Minda Hu, Licheng Zong, Hongru Wang, Jingyan Zhou, Jingjing Li, Yichen Gao, Kam-Fai Wong, Yu Li, Irwin King

Information Retrieval

Challenge:	Existing retrieval-augmented approaches to large language models face performance limitations due to the lack of publicly available training data.
Approach:	They propose a plug-and-play LLM-based retrieval method called Self-Rewarding Tree Search based on Monte Carlo Tree Search and a self-rewarding paradigm to address these limitations.
Outcome:	The proposed method improves the performance of the BM25 retriever and surpasses the baseline of self-reflection in both efficiency and scalability.

T2: An Adaptive Test-Time Scaling Strategy for Contextual Question Answering (2025.emnlp-main)

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Zhengyi Zhao, Shubo Zhang, Zezhong Wang, Huimin Wang, Yutian Zhao, Bin Liang, Yefeng Zheng, Binyang Li, Kam-Fai Wong, Xian Wu

Question Answering

Challenge:	Existing efficient test-time scaling methods introduce budget constraints or early stop mechanisms to avoid overthinking for straightforward questions but add human bias to the reasoning process.
Approach:	They propose a framework that dynamically adapts reasoning depth based on question complexity.
Outcome:	Experimental results show that the proposed framework achieves higher accuracy than baseline methods and reduces computational overhead by up to 25.2%.

Set Learning for Generative Information Extraction (2023.emnlp-main)

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Jiangnan Li, Yice Zhang, Bin Liang, Kam-Fai Wong, Ruifeng Xu

Information Extraction & Text Mining

Challenge:	Recent efforts to employ sequence-to-sequence models to solve IE tasks have been focused on a single problem: structured objects are an unordered set, resulting in a potential order bias.
Approach:	They propose a sequence-to-sequence (Seq2Sequen) model that considers multiple permutations of structured objects to optimize set probability approximately.
Outcome:	The proposed model improves existing frameworks on vast tasks and datasets.

Self-DC: When to Reason and When to Act? Self Divide-and-Conquer for Compositional Unknown Questions (2025.naacl-long)

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Hongru Wang, Boyang Xue, Baohang Zhou, Tianhua Zhang, Cunxiang Wang, Huimin Wang, Guanhua Chen, Kam-Fai Wong

Microblog Conversation Recommendation via Joint Modeling of Topics and Discourse (N18-1)

Challenge:	Existing studies focus on leveraging internal knowledge of Large Language Models (LLMs) to answer known questions.
Approach:	They propose a framework that allows LLMs to choose between internal and external knowledge . they use a dataset to analyze compositional questions that are composed of unknown sub-questions .
Outcome:	The proposed framework can achieve comparable or even better performance with much fewer external calls compared with several strong baselines.

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Xingshan Zeng, Jing Li, Lu Wang, Nicholas Beauchamp, Sarah Shugars, Kam-Fai Wong

SELF-GUARD: Empower the LLM to Safeguard Itself (2024.naacl-long)

Challenge:	Existing methods for recommendation focus on content of individual posts, but we exploit both context and user content and behavior preferences.
Approach:	They propose a method that captures conversational context and user content and behavior preferences.
Outcome:	The proposed method outperforms methods that only model content without considering discourse on two Twitter datasets.

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Zezhong Wang, Fangkai Yang, Lu Wang, Pu Zhao, Hongru Wang, Liang Chen, Qingwei Lin, Kam-Fai Wong

When Cantonese NLP Meets Pre-training: Progress and Challenges (2022.aacl-tutorials)

Challenge:	Recent studies have investigated methods to improve the safety of large language models (LLMs) safety training involves fine-tuning the LLM with adversarial samples, which activate the LRM’s capabilities against jailbreak.
Approach:	They propose a safety training approach that integrates safety training and safeguards to train the LLM to perform harmfulness detection on its own outputs.
Outcome:	The proposed method reduces harmful output and adds a [harmful] or [harmless] tag to the end of the LLM's response.

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Rong Xiang, Hanzhuo Tan, Jing Li, Mingyu Wan, Kam-Fai Wong

CoAD: Automatic Diagnosis through Symptom and Disease Collaborative Generation (2023.acl-long)

Challenge:	Cantonese is an influential Chinese variant with a large population of speakers worldwide.
Approach:	This tutorial will review Cantonese's progress in linguistics and NLP . it will introduce transformer-based pre-training methods for a wide range of downstream tasks .
Outcome:	This tutorial will present the main challenges for Cantonese NLP in relation to Cantonesian language idiosyncrasies of colloquialism and multilingualism.

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Huimin Wang, Wai Chung Kwan, Kam-Fai Wong, Yefeng Zheng

Challenge:	Automated diagnosis (AD) is a critical application of AI in healthcare . despite its simplicity and superior performance, a decline in disease diagnosis accuracy is observed .
Approach:	They propose a new collaborative disease and symptom generation framework to improve automatic diagnosis.
Outcome:	The Transformer-based method achieves an average 2.3% improvement over previous state-of-the-art methods . it can be used to query patients about their symptoms and health concerns .

Neural Conversation Recommendation with Online Interaction Modeling (D19-1)

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Xingshan Zeng, Jing Li, Lu Wang, Kam-Fai Wong

KGA: A General Machine Unlearning Framework Based on Knowledge Gap Alignment (2023.acl-long)

Challenge:	Existing models that only use lexical features and ignore past user interactions in online conversations are inadequate to identify and engage in online discussions.
Approach:	They propose a framework that automatically recommends conversations based on user's prior conversation behaviors by exploring deep semantic features that measure how a user’s preferences match an ongoing conversation’s context.
Outcome:	The proposed model outperforms state-of-the-art models on two large-scale datasets from Twitter and Reddit showing that it incorporates deep semantic features that measure how a user’s preferences match an ongoing conversation’s context.

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Lingzhi Wang, Tong Chen, Wei Yuan, Xingshan Zeng, Kam-Fai Wong, Hongzhi Yin

Challenge:	Recent legislation of the "right to be forgotten" has led to the interest in machine unlearning . MU can be used to forget specific training instances as if they have never existed .
Approach:	They propose a general unlearning framework called KGA to induce forgetfulness . they propose several unlearning evaluation metrics with pertinence .
Outcome:	The proposed framework improves on large-scale datasets and provides insight into unlearning for NLP tasks.

ToolFlow: Boosting LLM Tool-Calling Through Natural and Coherent Dialogue Synthesis (2025.naacl-long)

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Zezhong Wang, Xingshan Zeng, Weiwen Liu, Liangyou Li, Yasheng Wang, Lifeng Shang, Xin Jiang, Qun Liu, Kam-Fai Wong

WatME: Towards Lossless Watermarking Through Lexical Redundancy (2024.acl-long)

Challenge:	Large Language Models (LLMs) can be enhanced by using supervised fine-tuning . however, access to fine-timing data can be limited.
Approach:	They propose a Graph-based Sampling strategy and a Planned-generation strategy to enhance the coherence between dialogues by using 8,000 synthetic dialogues.
Outcome:	The proposed model achieves tool-calling performance comparable to or surpassing GPT-4 while maintaining strong general capabilities.

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Liang Chen, Yatao Bian, Yang Deng, Deng Cai, Shuaiyi Li, Peilin Zhao, Kam-Fai Wong

Challenge:	Existing methods for text watermarking rely on arbitrary vocabulary partitioning during decoding, which compromises the availability of suitable tokens and significantly degrades the quality of responses.
Approach:	They propose a method that leverages linguistic prior knowledge of lexical redundancies in LLM vocabularies to seamlessly integrate watermarks.
Outcome:	The proposed approach preserves the expressive power of large language models while preserving watermark detectability.

Chain-of-Probe: Examining the Necessity and Accuracy of CoT Step-by-Step (2025.findings-naacl)

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Zezhong Wang, Xingshan Zeng, Weiwen Liu, Yufei Wang, Liangyou Li, Yasheng Wang, Lifeng Shang, Xin Jiang, Qun Liu, Kam-Fai Wong

Quotation Recommendation and Interpretation Based on Transformation from Queries to Quotations (2021.acl-short)

Challenge:	Current research found the issue of Early Answering in large language models where the models already have an answer before generating the Chain-of-Thought (CoT).
Approach:	They propose a method to probe changes in confidence during the model’s reasoning and prioritize answers with correct reasoning among multiple candidates.
Outcome:	The proposed method reveals that in a significant number of question-answer cases, CoT appears to be unnecessary and this necessity correlates with the simplicity of the task, defined by the reasoning steps required.

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Lingzhi Wang, Xingshan Zeng, Kam-Fai Wong

Explainability & Interpretability in NLP

Challenge:	Existing models of quotation recommendation ignore the relationship between quotations and queries.
Approach:	They propose a transformation matrix that directly maps quotations to quotation representations.
Outcome:	The proposed model outperforms state-of-the-art models on two datasets in English and Chinese.

ReadPrompt: A Readable Prompting Method for Reliable Knowledge Probing (2023.findings-emnlp)

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Zezhong Wang, Luyao Ye, Hongru Wang, Wai-Chung Kwan, David Ho, Kam-Fai Wong

Flexibly Utilize Memory for Long-Term Conversation via a Fragment-then-Compose Framework (2025.emnlp-main)

Challenge:	Existing methods to prob pre-trained language models (PLMs) lack readability and credibility.
Approach:	They propose a method to identify meaningful sentences to serve as prompts to assess the knowledge encoded within pre-trained language models (PLMs).
Outcome:	The proposed method achieves state-of-the-art on the current knowledge probing benchmark.

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Cai Ke, Yiming Du, Bin Liang, Yifan Xiang, Lin Gui, Zhongyang Li, Baojun Wang, Yue Yu, Hui Wang, Kam-Fai Wong, Ruifeng Xu

PACAR: Automated Fact-Checking with Planning and Customized Action Reasoning Using Large Language Models (2024.lrec-main)

Challenge:	Large language models extract useful information from conversation history to enhance the response in long-term conversations.
Approach:	They propose a Fragment-then-Compose framework to optimize memory utilization for long-term open-domain conversation.
Outcome:	The proposed framework can be used to extract useful information from conversation history . it can be adapted to different situations and improve response generation .

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Xiaoyan Zhao, Lingzhi Wang, Zhanghao Wang, Hong Cheng, Rui Zhang, Kam-Fai Wong

JoTR: A Joint Transformer and Reinforcement Learning Framework for Dialogue Policy Learning (2024.lrec-main)

Challenge:	Existing studies rely on idealized "gold" evidence for predictions, which is unrealistic due to its limited availability in real-world scenarios.
Approach:	They propose a fact-checking framework based on planning and customized action reasoning using LLMs.
Outcome:	The proposed framework outperforms baseline methods across three datasets and with varying complexity levels.

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Wai-Chung Kwan, Huimin Wang, Hongru Wang, Zezhong Wang, Bin Liang, Xian Wu, Yefeng Zheng, Kam-Fai Wong

In-context Learning for Few-shot Multimodal Named Entity Recognition (2023.findings-emnlp)

Challenge:	Dialogue policy learning (DPL) aims to determine an abstract representation (also known as action) to guide what the response should be.
Approach:	They propose a joint Transformer-based model that generates a token-grained policy that allows more dynamic dialogue action generation without the need for predefined action candidates.
Outcome:	The proposed model outperforms existing models showing improvements of 9% and 13% in success rate and 34% and 37% in diversity of dialogue actions across two benchmark dialogue modeling tasks.

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Chenran Cai, Qianlong Wang, Bin Liang, Bing Qin, Min Yang, Kam-Fai Wong, Ruifeng Xu

Multimodality

Challenge:	Existing methods for named entity recognition are time-consuming and laborintensive.
Approach:	They propose a few-shot multimodal named entity recognition task that uses few examples to locate and identify named entities for a text-image pair.
Outcome:	The proposed framework outperforms baselines under several few-shot settings.

“I Know Who You Are”: Character-Based Features for Conversational Humor Recognition in Chinese (2022.findings-emnlp)

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Wenbo Shang, Jiangjiang Zhao, Zezhong Wang, Binyang Li, Fangchun Yang, Kam-Fai Wong

An Empirical Study on Multiple Knowledge from ChatGPT for Emotion Recognition in Conversations (2023.findings-emnlp)

Challenge:	a recent study has focused on how to recognize punchlines from dialogues, but has neglected character information.
Approach:	They propose a character-fusion conversational humor recognition model that uses character information to recognize punchlines from dialogue.
Outcome:	The proposed model improves performance on Chinese sitcoms corpus and punchline identification.

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Geng Tu, Bin Liang, Bing Qin, Kam-Fai Wong, Ruifeng Xu

Re-entry Prediction for Online Conversations via Self-Supervised Learning (2021.findings-emnlp)

Challenge:	Existing efforts in ERC focus on context- and speaker-sensitive dependencies, but lack of annotated data and high cost of obtaining such knowledge is a blank slate.
Approach:	They propose a Multiple Knowledge Fusion Model to integrate multiple knowledge generated by Large Language Models (LLMs) they analyze the contribution and complementarity of this knowledge into the model.
Outcome:	The proposed model integrates multiple knowledge generated by LLMs and analyzes its contribution and complementarity on three public datasets.

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Lingzhi Wang, Xingshan Zeng, Huang Hu, Kam-Fai Wong, Daxin Jiang

Rethinking Stateful Tool Use in Multi-Turn Dialogues: Benchmarks and Challenges (2025.findings-acl)

Challenge:	Existing work on re-entry prediction ignores conversation thread patterns and repeated engagement of target users.
Approach:	They propose to use conversation thread patterns to predict whether a user will come back to a conversation they once participated in to train a model on labels that are automatically derived from the data.
Outcome:	The proposed task outperforms the state-of-the-art models on two social media datasets with fewer parameters and faster convergence.

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Hongru Wang, Wenyu Huang, Yufei Wang, Yuanhao Xi, Jianqiao Lu, Huan Zhang, Nan Hu, Zeming Liu, Jeff Z. Pan, Kam-Fai Wong

A Collaborative Multi-agent Reinforcement Learning Framework for Dialog Action Decomposition (2021.emnlp-main)

Challenge:	Existing benchmarks that assess Language Models (LMs) as Language Agents (LAs) for tool use focus on stateless, single-turn interactions or partial evaluations, overlooking the inherent stateful nature of interactions in multi-turn applications.
Approach:	They propose a multi-turn dialogue dataset with stateful tool interactions considering the whole life cycle of tool use across six key tasks in three stages . they also build VirtualMobile – an embodied virtual mobile evaluation environment to simulate API calls and assess the robustness of the created APIs.
Outcome:	The proposed dataset evaluates 13 open- and closed-source LLMs and provides detailed analysis at each stage.

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Huimin Wang, Kam-Fai Wong

UAlign: Leveraging Uncertainty Estimations for Factuality Alignment on Large Language Models (2025.acl-long)

Challenge:	Currently, most reinforcement learning methods for dialog policy learning train a centralized agent that selects a predefined joint action concatenating domain name, intent type, and slot name.
Approach:	They propose a hierarchical multi-agent framework in which each part of the action is led by a different agent and a joint optimization process that makes agents can exchange their policy information.
Outcome:	The proposed framework reduces labor costs for action templates and decreases the size of the action space for each agent.

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Boyang Xue, Fei Mi, Qi Zhu, Hongru Wang, Rui Wang, Sheng Wang, Erxin Yu, Xuming Hu, Kam-Fai Wong

Towards Robust Personalized Dialogue Generation via Order-Insensitive Representation Regularization (2023.findings-acl)

Challenge:	Large Language Models (LLMs) often struggle to accurately express factual knowledge, especially in cases where the knowledge boundaries are ambiguous.
Approach:	They propose a framework that leverages Uncertainty estimations to represent knowledge boundaries and incorporates these representations into prompts for LLMs to Align with factual knowledge.
Outcome:	The proposed framework significantly improves the LLMs’ capacities to confidently answer known questions and refuse unknown questions on both in-domain and out-of-domain tasks.

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Liang Chen, Hongru Wang, Yang Deng, Wai Chung Kwan, Zezhong Wang, Kam-Fai Wong

Retrieval-free Knowledge Injection through Multi-Document Traversal for Dialogue Models (2023.acl-long)

Challenge:	Recent studies have focused on developing persona consistent dialogue models . order sensitivity affects the quality and consistency of generated response .
Approach:	They propose a model-agnostic framework to improve persona consistent dialogue response generation by concatenating persona texts and dialogue history as a single input sequence.
Outcome:	The proposed framework outperforms existing models on the Persona-Chat dataset and shows that it is more robust under different persona orders and more consistent with the persona profile.

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Rui Wang, Jianzhu Bao, Fei Mi, Yi Chen, Hongru Wang, Yasheng Wang, Yitong Li, Lifeng Shang, Kam-Fai Wong, Ruifeng Xu

VLEU: a Method for Automatic Evaluation for Generalizability of Text-to-Image Models (2024.emnlp-main)

Challenge:	Existing research on retrieval-augmented and retrieval free dialogue models focuses on retrieving knowledge from external sources and rely on finely annotated retrieval training data and knowledge-grounded responses.
Approach:	They propose a retrieval-free approach by turning knowledge documents into simulated multi-turn dialogues using a Multi-Document Traversal algorithm.
Outcome:	The proposed approach outperforms retrieval-augmented models while being cheaper and faster at domain transfer.

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Jingtao Cao, Zhang Zheng, Hongru Wang, Kam-Fai Wong

Role Prompting Guided Domain Adaptation with General Capability Preserve for Large Language Models (2024.findings-naacl)

Challenge:	Existing metrics, such as CLIP, measure the semantic alignment between single prompts and their corresponding images, but they fail to evaluate a model’s generalizability across a broad spectrum of textual inputs.
Approach:	They propose a metric that leverages the power of Large Language Models to sample from the visual text domain and assess its generalizability.
Outcome:	The proposed metric evaluates the generalizability of T2I models and provides valuable insights during the finetuning process.

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Rui Wang, Fei Mi, Yi Chen, Boyang Xue, Hongru Wang, Qi Zhu, Kam-Fai Wong, Ruifeng Xu

Joint Effects of Context and User History for Predicting Online Conversation Re-entries (P19-1)

Challenge:	Large Language Models (LLMs) suffer catastrophic forgetting when tailored to specific domains . authors present a novel approach to manage multi-domain LLM adaptation .
Approach:	They propose a strategy to manage multi-domain LLM adaptation using self-distillation and role integration.
Outcome:	The proposed model alleviates catastrophic forgetting and inter-domain confusion while maintaining robust general capabilities.

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Xingshan Zeng, Jing Li, Lu Wang, Kam-Fai Wong

MT-Eval: A Multi-Turn Capabilities Evaluation Benchmark for Large Language Models (2024.emnlp-main)

Challenge:	Existing methods for predicting online conversation re-entry focus on modeling engagement patterns in ongoing conversations or ignoring the rich information in users' previous chatting history.
Approach:	They propose a neural framework with three main layers to model the conversation context and user history and their interactions with Twitter and Reddit to predict whether a user will return to a conversation they once participated in.
Outcome:	The proposed framework outperforms the state-of-the-art methods on two large-scale Twitter and Reddit conversations, and achieves an F1 score of 61.1 on Twitter conversations.

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Wai-Chung Kwan, Xingshan Zeng, Yuxin Jiang, Yufei Wang, Liangyou Li, Lifeng Shang, Xin Jiang, Qun Liu, Kam-Fai Wong

ReSURE: Regularizing Supervision Unreliability for Multi-turn Dialogue Fine-tuning (2025.emnlp-main)

Challenge:	Existing evaluation frameworks focus on single-turn evaluations, overlooking the models’ capabilities in multi-turn interactions.
Approach:	They propose a benchmark to evaluate the multi-turn conversational abilities of large language models (LLMs) by analyzing human-LLM conversations and constructing multi-turned queries for each category using GPT-4.
Outcome:	The proposed model outperforms open-source models in multi-turn tasks while retaining and recalling historical information.

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Yiming Du, Yifan Xiang, Bin Liang, Dahua Lin, Kam-Fai Wong, Fei Tan