Papers by Wenjie Feng
Personalized Generation In Large Model Era: A Survey (2025.acl-long)
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Yiyan Xu, Jinghao Zhang, Alireza Salemi, Xinting Hu, Wenjie Wang, Fuli Feng, Hamed Zamani, Xiangnan He, Tat-Seng Chua
| Challenge: | Recent advances in large generative models have catalyzed a paradigm shift in content generation to Personalized Generation (PGen). |
| Approach: | They propose a multi-level taxonomy that systematically formalizes PGen's key components, core objectives, and abstract workflows. |
| Outcome: | The proposed taxonomy bridging PGen research across multiple modalities highlights open challenges and promising directions for future exploration. |
Attack Prompt Generation for Red Teaming and Defending Large Language Models (2023.findings-emnlp)
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| Challenge: | Existing studies construct attack prompts via manual or automatic methods, but these methods have limitations on cost and quality. |
| Approach: | They propose an attack framework to instruct LLMs to mimic human-generated prompts through in-context learning and a defense framework that fine-tunes victim LLM's through iterative interactions with the attack framework. |
| Outcome: | The proposed approach is based on experiments on different LLMs to evaluate their effectiveness against red teaming attacks. |
Hypothetical Training for Robust Machine Reading Comprehension of Tabular Context (2023.findings-acl)
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| Challenge: | Counterfactual training is expensive because of the complexity of tabular data. |
| Approach: | They propose a hypothetical training framework that uses paired examples with different hypothetical questions to supervise the direction of model gradient towards the counterfactual answer change. |
| Outcome: | The proposed framework improves on tabular MRC datasets. |
PERM: Psychology-grounded Empathetic Reward Modeling for Large Language Models (2026.findings-acl)
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| 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. |
Adversary-Aware DPO: Enhancing Safety Alignment in Vision Language Models via Adversarial Training (2025.findings-emnlp)
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| Challenge: | Adversary-aware DPO (ADPO) is a training framework that explicitly considers adversary. |
| Approach: | a new framework integrates adversarial training into a pre-trained large language model to enhance safety alignment . adversary-aware DPO provides a framework that explicitly considers adversary . |
| Outcome: | a new training framework outperforms baselines in safety alignment and general utility of large language models. |
SemRoDe: Macro Adversarial Training to Learn Representations that are Robust to Word-Level Attacks (2024.naacl-long)
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| Challenge: | Existing approaches to defend against word-level attacks have been limited. |
| Approach: | They propose a new approach called Semantic Robust Defence to enhance the robustness of language models by aligning the domains with a distance-based objective. |
| Outcome: | The proposed approach can be generalized across word embeddings, even when they share minimal overlap at both vocabulary and word-substitution levels. |
Counterfactual Debating with Preset Stances for Hallucination Elimination of LLMs (2025.coling-main)
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| 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. |
Controllable LLM Reasoning via Sparse Autoencoder-Based Steering (2026.acl-long)
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| 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. |
Can Graph Descriptive Order Affect Solving Graph Problems with LLMs? (2025.acl-long)
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| Challenge: | Large language models (LLMs) have achieved significant success in reasoning tasks, including mathematical reasoning and logical deduction. |
| Approach: | They conduct the first comprehensive analysis of how the order of graph descriptions impacts LLM performance. |
| Outcome: | The results show that graph descriptions significantly improve LLMs’ comprehension of graph structures, and the robustness of LLM models to graph description order varies across different tasks. |
ReList: A Multi-objective Reasoning Framework for Diversified Listwise Query Recommendation (2026.acl-industry)
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| Challenge: | Existing methods for related search have limited semantic redundancy and wasted retrieval quota . generative retrieval approaches lack explicit reasoning, relying on superficial click-through rate rewards . |
| Approach: | They propose a framework that transforms related search into a reasoning-enhanced listwise generation task. |
| Outcome: | Experimental results show that ReList outperforms state-of-the-art methods in query diversity and user engagement. |
Measuring What Makes You Unique: Difference-Aware User Modeling for Enhancing LLM Personalization (2025.findings-acl)
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| Challenge: | Extensive experiments on real-world datasets demonstrate that DPL significantly enhances LLM personalization. |
| Approach: | They propose a novel approach that emphasizes extracting inter-user differences to enhance LLM personalization. |
| Outcome: | The proposed approach extracts inter-user differences to enhance LLM personalization. |
Length Controlled Generation for Black-box LLMs (2025.acl-long)
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Yuxuan Gu, Wenjie Wang, Xiaocheng Feng, Weihong Zhong, Kun Zhu, Lei Huang, Ting Liu, Bing Qin, Tat-Seng Chua
| Challenge: | Existing length control methods involve fine-tuning the parameters of LLMs, which is inefficient and suboptimal for practical use. |
| Approach: | They propose an iterative sampling framework that regulates LLMs to generate length-constrained text without modifying the underlying parameters. |
| Outcome: | The proposed method achieves 100% success rates on Llama3.1 tasks with minimal additional computational overhead. |
HellaSwag-Pro: A Large-Scale Bilingual Benchmark for Evaluating the Robustness of LLMs in Commonsense Reasoning (2025.findings-acl)
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Xiaoyuan Li, Moxin Li, Rui Men, Yichang Zhang, Keqin Bao, Wenjie Wang, Fuli Feng, Dayiheng Liu, Junyang Lin
| Challenge: | Existing studies show that large language models are robust in commonsense reasoning . however, some variations in questions can lead to incorrect responses . |
| Approach: | They propose a large-scale bilingual benchmark consisting of 11,200 cases . they conduct extensive experiments on 41 representative LLMs . |
| Outcome: | The proposed benchmark systematically evaluates the robustness of large language models in commonsense reasoning. |
MTR-Bench: A Comprehensive Benchmark for Multi-Turn Reasoning Evaluation (2026.acl-long)
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Xiaoyuan Li, Keqin Bao, Yubo Ma, Moxin Li, Wenjie Wang, Rui Men, Yichang Zhang, Fuli Feng, Dayiheng Liu
| Challenge: | Recent advances in Large Language Models (LLMs) have shown promising results in complex reasoning tasks. |
| Approach: | They propose to use a multi-turn reasoning evaluation framework to cover multi-turn interactions with the environments of large language models. |
| Outcome: | The proposed framework covers diverse reasoning capabilities, fine-grained difficulty granularity, and necessitates multi-turn interactions with the environments. |
Evaluating Mathematical Reasoning of Large Language Models: A Focus on Error Identification and Correction (2024.findings-acl)
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| Challenge: | Existing evaluations focus on problem-solving from examiner perspective, overlooking a dual perspective of examiner regarding error identification and correction. |
| Approach: | They propose to use an annotated dataset to evaluate large language models from the examiner perspective and to use diverse prompts to evaluate eleven representative LLMs. |
| Outcome: | The proposed model outperforms all models while LLaMA-2-7B has comparable abilities to closed-source models GPT-3.5 and Gemini Pro. |
Consistency-Aware Online Multi-Objective Alignment for Related Search Query Generation (2025.acl-industry)
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| Challenge: | Existing methods fail to reconcile click-through rate (CTR) optimization with topic expansion. |
| Approach: | They propose a query generation framework that aligns click-through rate and topic expansion goals through an online DPO paradigm. |
| Outcome: | The proposed approach achieves significant CTR gains (+2.3%) and higher human-rated query quality compared to state-of-the-art methods. |
Text-like Encoding of Collaborative Information in Large Language Models for Recommendation (2024.acl-long)
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| Challenge: | Existing methods to adapt Large Language Models for Recommendation (LLMRec) do not represent collaborative information in a text-like format, which may not align optimally with LLMs. |
| Approach: | They propose a novel LLMRec method that integrates collaborative information through text-like encoding. |
| Outcome: | Extensive experiments show that BinLLM integrates collaborative information better with LLMs. |
Self-Improvement Towards Pareto Optimality: Mitigating Preference Conflicts in Multi-Objective Alignment (2025.findings-acl)
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| Challenge: | Existing approaches to optimize large language models with human preferences suffer from preference conflicts in the data. |
| Approach: | They propose to construct Pareto-optimal responses to resolve preference conflicts by using a self-improving DPO framework that enables LLMs to self-generate and select Paret-optimized responses. |
| Outcome: | The proposed framework achieves superior Pareto Front performance over baselines on two datasets. |
Customizing In-context Learning for Dynamic Interest Adaption in LLM-based Recommendation (2025.findings-acl)
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| Challenge: | Existing Large Language Model (LLM)-based recommender systems face challenges to adapt to dynamic user interests without any model-level updates. |
| Approach: | They propose a framework that establishes recommendation-oriented in-context learning by structuring recent user interactions and current inputs into ICL formats. |
| Outcome: | The proposed model adapts to dynamic user interests without model updates without any model updates and is available online at https://anonymous.4open.science/r/RecICL-8003. |
Robust Prompt Optimization for Large Language Models Against Distribution Shifts (2023.emnlp-main)
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| Challenge: | Existing research has explored automatic prompt optimization methods to eliminate manual effort in identifying effective prompts for a given task. |
| Approach: | They propose a framework for prompt optimization that can be generalized to an unlabeled target group. |
| Outcome: | The proposed framework improves on target group and source group while generalizing to unlabeled target group. |
Counterfactual Active Learning for Out-of-Distribution Generalization (2023.acl-long)
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| Challenge: | Existing studies on active learning methods focus on the out-of-distribution generalization of out- of-distortion samples. |
| Approach: | They propose a counterfactual active learning approach that empowers active learning with counterfact thinking to bridge the seen samples with unseen cases. |
| Outcome: | The proposed approach outperforms existing active learning methods on public datasets with comparable IID performance. |
Think Twice Before Trusting: Self-Detection for Large Language Models through Comprehensive Answer Reflection (2024.findings-emnlp)
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| Challenge: | Existing approaches to self-detection only retrospectively evaluate LLM-generated answers, leading to over-trust in incorrectly generated answers. |
| Approach: | They propose a self-detection paradigm that considers the comprehensive answer space beyond LLM-generated answers to mitigate the over-trust in LLM generated incorrect answers. |
| Outcome: | The proposed framework can be integrated with existing approaches for superior self-detection. |
Tunable LLM-based Proactive Recommendation Agent (2025.acl-long)
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| Challenge: | Current methods focus on catering to existing user interests, leading to polarized recommendation distributions. |
| Approach: | They propose an LLM-based Actor-Critic Agent framework to cultivate latent interests through multi-step recommendations. |
| Outcome: | The proposed framework optimizes long-term rewards and dynamically evolves with user feedback. |