Papers by Wenqi Fan
C2KD: Cross-layer and Cross-head Knowledge Distillation for Small Language Model-based Recommendation (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) show promise but their size and high inference costs limit deployment on resource-constrained devices. |
| Approach: | They propose a framework to transfer task-relevant knowledge from two complementary dimensions to Large Language Models (LLMs) Large Language models (LLMS) have demonstrated great potential in sequential recommendation tasks . |
| Outcome: | Extensive experiments across diverse model families show that the proposed framework achieves competitive performance compared to LLMs. |
When Efficiency Becomes a Vulnerability: Computational Cost Attacks on WebAgents (2026.acl-long)
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| Challenge: | Existing WebAgents suffer from computational cost attacks due to long reasoning processes and excessive computational cost. |
| Approach: | They propose a framework that generates adversarial prompts and a reinforcement learning-enhanced selector to identify the most effective perturbations. |
| Outcome: | The proposed framework exploits large language models to generate diverse adversarial prompts and a reinforcement learning–enhanced selector to identify the most effective perturbations. |
LLM-REDIAL: A Large-Scale Dataset for Conversational Recommender Systems Created from User Behaviors with LLMs (2024.findings-acl)
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| Challenge: | Existing CRS datasets suffer from data inextensibility and semantic inconsistency . |
| Approach: | They introduce the LLM-REDIAL dataset to facilitate the research in CRS by leveraging large language models to generate high-quality dialogues. |
| Outcome: | The proposed dataset is the largest multi-domain CRS dataset which consists of 47.6k multi-turn dialogues with 482.6k utterances across 4 domains. |
Does Gender Matter? Towards Fairness in Dialogue Systems (2020.coling-main)
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| Challenge: | Recent studies have shown that AI is unfair in many real-world applications such as computer vision and recommendations. |
| Approach: | They propose to use a benchmark dataset to study the fairness of dialogue systems to understand their bias. |
| Outcome: | The proposed methods reduce the bias in dialogue systems significantly. |
ReRec: Reasoning-Augmented LLM-based Recommendation Assistant via Reinforcement Fine-tuning (2026.acl-long)
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| Challenge: | Existing reasoning-augmented systems that handle complex queries are lacking . we present a framework that enhances LLM-based recommendation assistants . |
| Approach: | They propose a reinforcement fine-tuning framework that enhances LLM-based recommendation . they use a dual-graph Enhanced Reward Shaping framework to integrate recommendation metrics . |
| Outcome: | The proposed framework outperforms state-of-the-art recommendations and preserves core abilities. |
X-LeBench: A Benchmark for Extremely Long Egocentric Video Understanding (2025.findings-emnlp)
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Wenqi Zhou, Kai Cao, Hao Zheng, Yunze Liu, Xinyi Zheng, Miao Liu, Per Ola Kristensson, Walterio W. Mayol-Cuevas, Fan Zhang, Weizhe Lin, Junxiao Shen
| Challenge: | Existing benchmark datasets focus on short to moderately long videos, leaving a substantial gap in evaluating extensive, ultra-long egocentric video recordings. |
| Approach: | X-LeBench is a benchmark dataset designed to evaluate long egocentric video recordings . it uses a life-logging pipeline to produce realistic, coherent daily plans . |
| Outcome: | X-LeBench is a new benchmark dataset designed to evaluate long-form egocentric video understanding . the approach produces realistic, coherent daily plans aligned with real-world video data . |
Datamart-Agent: LLM-Driven Game-Theoretic Agent for Data Marketplace Modeling (2026.findings-acl)
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| Challenge: | Existing studies on data marketplaces model static equilibria and complete information, which limits their realism. |
| Approach: | They propose an LLM-driven game-theoretic agent that makes equilibrium-consistent decisions in analytically tractable data marketplaces with evolving and incomplete-information. |
| Outcome: | The proposed framework matches equilibrium-consistent decision execution in a static data marketplace with a dynamic game tree memory and mechanism-guided reflection without updating parameters. |
CADMate: Generating CAD Assembly Plan with Geometric Chain-of-Thought and Spatial Physical Rewards (2026.acl-long)
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| Challenge: | Computer-aided design (CAD) is crucial in prototyping complex 3D objects . designers manually define assembly sequences for individual CAD parts . |
| Approach: | They propose a framework for computer-aided design that predicts actions for CAD parts . they use a reference design image and disassembled parts to generate 6-DoF transformations . |
| Outcome: | The proposed framework outperforms existing MLLMs in the design of CAD assemblies. |
Uni-Retrieval: A Multi-Style Retrieval Framework for STEM’s Education (2025.acl-long)
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| Challenge: | Current retrieval models focus on natural text-image retrieval, which is insufficient for STEM education contexts due to ambiguities in the retrieval process. |
| Approach: | They propose a diverse expression retrieval task tailored to educational scenarios . they extract query style features as prototypes and build a continuously updated Prompt Bank . |
| Outcome: | The proposed model outperforms existing retrieval models in most retrieval tasks. |
Advancing the Robustness of Large Language Models through Self-Denoised Smoothing (2024.naacl-short)
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Jiabao Ji, Bairu Hou, Zhen Zhang, Guanhua Zhang, Wenqi Fan, Qing Li, Yang Zhang, Gaowen Liu, Sijia Liu, Shiyu Chang
| Challenge: | Existing adversarial attacks can cause LLMs to make wrong predictions on downstream tasks or generate harmful content misaligned with human values. |
| Approach: | They propose to use randomized smoothing to add noise to the input and then make predictions based on these denoised versions. |
| Outcome: | The proposed method surpasses existing methods in both empirical and certified robustness in defending against adversarial perturbations for both downstream tasks and human alignments (i.e., jailbreak attacks). |
Benchmarking for Domain-Specific LLMs: A Case Study on Academia and Beyond (2025.findings-emnlp)
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| Challenge: | Comp-Comp is an iterative benchmarking framework grounded in the principles of comprehensiveness and compactness. |
| Approach: | They propose a benchmark framework that incorporates the principle of comprehensiveness and compactness. |
| Outcome: | The proposed framework is domain-agnostic and adaptable to a wide range of specialized fields. |
Knowledge Graph Retrieval-Augmented Generation for LLM-based Recommendation (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) have produced significant advances in the field of recommender systems. |
| Approach: | They propose to retrieve up-to-date structure information from the knowledge graph to augment recommendations by leveraging external knowledge sources. |
| Outcome: | Experiments on a large dataset show that the proposed method is effective in enhancing LLM-based recommendations. |
Exploring Graph Learning Tasks with Pure LLMs: A Comprehensive Benchmark and Investigation (2026.findings-acl)
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| Challenge: | Recent studies focus on performance benchmarks without fully comparing LLMs to graph learning models. |
| Approach: | They evaluate off-the-shelf and instruction-tuned graph learning models across a variety of scenarios. |
| Outcome: | The proposed models outperform traditional graph learning models in few-shot settings, the authors show . their models out perform models with instruction tuning, and they show excellent generalization and robustness. |
GLProtein: Global-and-Local Structure Aware Protein Representation Learning (2025.findings-emnlp)
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| Challenge: | Despite advances in protein sequence analysis, there remains potential for further exploration in integrating protein structural information. |
| Approach: | They propose a framework that integrates global structural similarity and local amino acid details to enhance protein pre-training. |
| Outcome: | The proposed framework outperforms existing methods in several bioinformatics tasks. |