Papers by Chuhan Wu

Know-MRI: A Knowledge Mechanisms Revealer&Interpreter for Large Language Models (2025.acl-demo)

Challenge:	Existing news recommendation methods rely on centralized storage of user behavior data for model training, which may lead to privacy concerns and risks due to the privacy-sensitive nature of user behaviors.
Approach:	They propose a privacy-preserving method where user behavior data is locally stored on user devices to train accurate news recommendation models.
Outcome:	The proposed method can train accurate news recommendation models without centralized storage of user behavior data.

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Jiaxiang Liu, Boxuan Xing, Chenhao Yuan, ChenxiangZhang ChenxiangZhang, Di Wu, Xiusheng Huang, Haida Yu, Chuhan Lang, Pengfei Cao, Jun Zhao, Kang Liu

Explainability & Interpretability in NLP

Challenge:	Existing interpretation methods only support tasks with specific inputs, limiting their practical applications.
Approach:	They propose an extensible module that matches different input data with interpretation methods and consolidates the interpreting outputs.
Outcome:	The proposed module can match different input data with interpretation methods and consolidate the interpreting outputs.

Uni-FedRec: A Unified Privacy-Preserving News Recommendation Framework for Model Training and Online Serving (2021.findings-emnlp)

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Tao Qi, Fangzhao Wu, Chuhan Wu, Yongfeng Huang, Xing Xie

Responsible & Trustworthy NLP

Challenge:	Existing news recommendation methods rely on user behavior data to model user interests and user interests.
Approach:	They propose a unified news recommendation framework that uses user data locally stored in user clients to train models and serve users in a privacy-preserving way.
Outcome:	The proposed framework outperforms baseline methods and effectively protects user privacy.

LLMTreeRec: Unleashing the Power of Large Language Models for Cold-Start Recommendations (2025.coling-main)

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Wenlin Zhang, Chuhan Wu, Xiangyang Li, Yuhao Wang, Kuicai Dong, Yichao Wang, Xinyi Dai, Xiangyu Zhao, Huifeng Guo, Ruiming Tang

Tiny-NewsRec: Effective and Efficient PLM-based News Recommendation (2022.emnlp-main)

Challenge:	Lack of training data leads to the system cold-start problem in recommendation systems, making them struggle to provide effective recommendations.
Approach:	They propose a tree-based LLM recommendation framework which structures all items into an item tree to improve the efficiency of LLM’s item retrieval.
Outcome:	The proposed framework outperforms the baseline model in the A/B test on Huawei industrial system.

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Yang Yu, Fangzhao Wu, Chuhan Wu, Jingwei Yi, Qi Liu

Reviews Meet Graphs: Enhancing User and Item Representations for Recommendation with Hierarchical Attentive Graph Neural Network (D19-1)

Challenge:	Existing work fine tunes the PLM with the news recommendation task, which can cause a domain shift problem.
Approach:	They propose a self-supervised method to adapt general PLM to news domain with a contrastive matching task between news titles and news bodies.
Outcome:	The proposed method can improve both the effectiveness and efficiency of the large PLM-based news recommendation model while maintaining its performance.

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Chuhan Wu, Fangzhao Wu, Tao Qi, Suyu Ge, Yongfeng Huang, Xing Xie

Hierarchical User and Item Representation with Three-Tier Attention for Recommendation (N19-1)

Challenge:	Existing methods to learn user and item representations from review texts do not take into account the user-user and item-item relatedness of the user.
Approach:	They propose to use review content and user-item graphs to integrate them as different views.
Outcome:	The proposed approach can learn user and item representations from review content and user-item graphs.

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Chuhan Wu, Fangzhao Wu, Junxin Liu, Yongfeng Huang

Neural News Recommendation with Heterogeneous User Behavior (D19-1)

Challenge:	Existing methods to learn user and item representations from reviews are limited . existing methods learn user representations based on ratings given by users .
Approach:	They propose a hierarchical user and item representation model with three-tier attention to learn user and items from reviews for recommendation.
Outcome:	The proposed model can learn user and item representations from reviews on four benchmark datasets.

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Chuhan Wu, Fangzhao Wu, Mingxiao An, Tao Qi, Jianqiang Huang, Yongfeng Huang, Xing Xie

NoisyTune: A Little Noise Can Help You Finetune Pretrained Language Models Better (2022.acl-short)

Challenge:	Existing news recommendation methods rely on news click history to model user interest, but data sparsity is a problem . other kinds of user behaviors such as webpage browsing and search queries can provide useful clues of users’ news reading interest.
Approach:	They propose to exploit heterogeneous user behaviors to learn news representations from their titles via CNN networks and apply attention networks to select important words.
Outcome:	The proposed approach exploits heterogeneous user behaviors on a real-world dataset.

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Neural News Recommendation with Long- and Short-term User Representations (P19-1)

Challenge:	Existing methods for finetuning pretrained language models (PLMs) have risks in overfitting the pretraining tasks and data, which may lead to suboptimal performance.
Approach:	They propose a method which adds noise to parameters of PLMs before fine-tuning.
Outcome:	The proposed method can be used on GLUE English and XTREME multilingual benchmarks.

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Mingxiao An, Fangzhao Wu, Chuhan Wu, Kun Zhang, Zheng Liu, Xing Xie

DA-Transformer: Distance-aware Transformer (2021.naacl-main)

Challenge:	Existing news recommendation methods learn a single representation for each user, which may be insufficient.
Approach:	They propose a neural news recommendation approach which can learn both long- and short-term user representations by using a news encoder and a user encoder.
Outcome:	The proposed approach can learn both long- and short-term user representations on a real-world dataset.

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Chuhan Wu, Fangzhao Wu, Yongfeng Huang

Attentive Pooling with Learnable Norms for Text Representation (2020.acl-main)

Challenge:	Existing models that capture token distances are not optimal for modeling the orders and relations of contexts.
Approach:	They propose a distance-aware Transformer that can exploit the real distances between tokens to re-scale the raw self-attention weights.
Outcome:	The proposed model outperforms the existing Transformer and its variants on five benchmark datasets and can improve the performance of many tasks.

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Chuhan Wu, Fangzhao Wu, Tao Qi, Xiaohui Cui, Yongfeng Huang

One Teacher is Enough? Pre-trained Language Model Distillation from Multiple Teachers (2021.findings-acl)

Challenge:	Existing pooling methods that use fixed pooling norms may not be optimal for learning text representations in different tasks.
Approach:	They propose to learn pooling norms in an end-to-end manner to automatically find the optimal ones for text representation in different tasks.
Outcome:	The proposed approach improves on four benchmark datasets on a neural NLP model.

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Chuhan Wu, Fangzhao Wu, Yongfeng Huang

HieRec: Hierarchical User Interest Modeling for Personalized News Recommendation (2021.acl-long)

Challenge:	Pre-trained language models (PLMs) have huge model sizes and computational complexity, making it difficult to deploy them to low-latency and high-concurrence online systems.
Approach:	They propose a multi-teacher knowledge distillation framework for pre-trained language model compression that can train high-quality student model from multiple teacher PLMs.
Outcome:	The proposed framework can train high-quality student model from multiple teacher PLMs with shared pooling and prediction layers to align output space for better collaborative teaching.

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Tao Qi, Fangzhao Wu, Chuhan Wu, Peiru Yang, Yang Yu, Xing Xie, Yongfeng Huang

Crowd Comparative Reasoning: Unlocking Comprehensive Evaluations for LLM-as-a-Judge (2025.acl-long)

Challenge:	Existing news recommendation methods learn a single user embedding for each user from their previous behaviors to represent their overall interest. Existing methods only learn 'one' embeddable representation vectors to model user interest.
Approach:	They propose a news recommendation method with hierarchical user interest modeling that captures user interest in news rather than a single user embedding.
Outcome:	The proposed method can better capture multi-grained user interest in news.

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Qiyuan Zhang, Yufei Wang, Yuxin Jiang, Liangyou Li, Chuhan Wu, Yasheng Wang, Xin Jiang, Lifeng Shang, Ruiming Tang, Fuyuan Lyu, Chen Ma

Reasoning

Challenge:	Existing methods rely on majority voting or criteria expansion to capture detailed and detailed details, often leading to incomplete outcomes.
Approach:	They propose a method which introduces additional crowd responses to compare with the candidate responses, thereby exposing deeper and more comprehensive details within the candidate answers.
Outcome:	Experiments show that the proposed method improves evaluation reliability and achieves an average gain of 6.7% across five benchmarks.

Learning to Edit: Aligning LLMs with Knowledge Editing (2024.acl-long)

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Yuxin Jiang, Yufei Wang, Chuhan Wu, Wanjun Zhong, Xingshan Zeng, Jiahui Gao, Liangyou Li, Xin Jiang, Lifeng Shang, Ruiming Tang, Qun Liu, Wei Wang

Challenge:	Existing knowledge editing techniques rely on memorizing updated knowledge, impeding LLMs from effectively combining the new knowledge with their inherent knowledge when answering questions.
Approach:	They propose a Learning to Edit framework that equips LLMs with the ability to apply updated knowledge to input questions through a two-phase process .
Outcome:	The proposed framework outperforms existing methods in knowledge editing tasks and compares it with four benchmarks and two LLM architectures.

Beyond Transcription: Unified Audio Schema for Perception-Aware AudioLLMs (2026.findings-acl)

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Linhao Zhang, Yuhan Song, Aiwei Liu, Chuhan Wu, Sijun Zhang, Wei Jia, Yuan Liu, Houfeng Wang, Zhou Xiao

Multimodality

Challenge:	Recent Audio Large Language Models (AudioLLMs) excel at reasoning tasks, but struggle at elementary auditory perception.
Approach:	They propose a framework that organizes audio information into three explicit components in a unified JSON format.
Outcome:	The proposed framework boosts fine-grained perception by 10.9% on MMSU over state-of-the-art models while preserving robust reasoning capabilities.

Hi-Transformer: Hierarchical Interactive Transformer for Efficient and Effective Long Document Modeling (2021.acl-short)

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Efficient-FedRec: Efficient Federated Learning Framework for Privacy-Preserving News Recommendation (2021.emnlp-main)

Challenge:	Existing approaches to model long documents are difficult due to the quadratic complexity of text length.
Approach:	They propose a hierarchical interactive Transformer for efficient long document modeling.
Outcome:	Extensive experiments on three benchmark datasets validate the efficiency and effectiveness of Hi-Transformer in long document modeling.

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Jingwei Yi, Fangzhao Wu, Chuhan Wu, Ruixuan Liu, Guangzhong Sun, Xing Xie

Responsible & Trustworthy NLP

Challenge:	Existing news recommendation methods rely on centralized storage of user click behavior data, which may lead to privacy concerns and hazards.
Approach:	They propose a federated learning framework for privacy-preserving news recommendation . they propose aggregation of news representations and user model by a client .
Outcome:	The proposed framework reduces computation and communication cost on clients while keeping promising model performance.

Two Birds with One Stone: Unified Model Learning for Both Recall and Ranking in News Recommendation (2022.findings-acl)

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PTUM: Pre-training User Model from Unlabeled User Behaviors via Self-supervision (2020.findings-emnlp)

Challenge:	Existing news recommender systems conduct news recall and ranking separately with different models, but maintaining multiple models leads to high computational cost and high latency.
Approach:	They propose a unified method for recall and ranking in news recommendation that uses historical news click behaviors to extract user embeddings for ranking from the user's attention query.
Outcome:	The proposed method improves recall and ranking efficiency and effectiveness on a benchmark dataset.

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Chuhan Wu, Fangzhao Wu, Tao Qi, Jianxun Lian, Yongfeng Huang, Xing Xie

PP-Rec: News Recommendation with Personalized User Interest and Time-aware News Popularity (2021.acl-long)

Challenge:	Existing methods for user modeling cannot exploit useful information in unlabeled data . Existing models only model task-specific user information and do not exploit universal user information encoded in user behaviors.
Approach:	They propose to pre-train user models from large-scale unlabeled user behavior data.
Outcome:	The proposed method can model relatedness between historical and future behaviors on two real-world datasets.

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Tao Qi, Fangzhao Wu, Chuhan Wu, Yongfeng Huang

Challenge:	Existing personalized news recommendation methods have difficulties in making accurate recommendations to cold-start users.
Approach:	They propose to incorporate news popularity information to improve cold-start recommendations . they propose to use a popularity-aware user encoder to eliminate popularity bias .
Outcome:	The proposed method improves accuracy and diversity of personalized news recommendation on two real-world datasets.

SentiRec: Sentiment Diversity-aware Neural News Recommendation (2020.aacl-main)

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Neural News Recommendation with Multi-Head Self-Attention (D19-1)

Sentiment Analysis

Challenge:	Existing news recommendation methods rank candidate news based on relevance to users’ historical browsed news, but if browsed data is dominated by certain kinds of sentiment, the model may recommend news with the same sentiment.
Approach:	They propose a sentiment diversity-aware neural news recommendation approach which can recommend news with more diverse sentiment without performance sacrifices.
Outcome:	The proposed approach can improve the sentiment diversity in news recommendation without performance sacrifice.

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Chuhan Wu, Fangzhao Wu, Suyu Ge, Tao Qi, Yongfeng Huang, Xing Xie

Neural News Recommendation with Topic-Aware News Representation (P19-1)

Challenge:	Precisely modeling news and users is critical for news recommendation, and capturing the contexts of words and news is important to learn news and user representations.
Approach:	They propose a neural news recommendation approach with multi-head self-attention to model the interactions between words and news and use multi-headed self- attention to capture relatedness between the news.
Outcome:	The proposed approach can learn representations from news titles by modeling the interactions between words and users and capture relatedness between the news.

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Chuhan Wu, Fangzhao Wu, Mingxiao An, Yongfeng Huang, Xing Xie

MIND: A Large-scale Dataset for News Recommendation (2020.acl-main)

Challenge:	Existing methods for learning accurate news representations do not consider topic information in news.
Approach:	They propose a neural news recommendation approach with topic-aware news representations using CNN networks and attention networks to select important words.
Outcome:	The proposed approach is based on a topic-aware news encoder and user encoder.

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Fangzhao Wu, Ying Qiao, Jiun-Hung Chen, Chuhan Wu, Tao Qi, Jianxun Lian, Danyang Liu, Xing Xie, Jianfeng Gao, Winnie Wu, Ming Zhou

POINTS-Reader: Distillation-Free Adaptation of Vision-Language Models for Document Conversion (2025.emnlp-main)

Challenge:	Personalized news recommendation is an important technique for personalized news service.
Approach:	They propose to build a large-scale news recommendation dataset from Microsoft News . they demonstrate that news recommendation relies on the quality of news content understanding .
Outcome:	The proposed dataset contains 1 million users and more than 160k English news articles, each of which has rich textual content such as title, abstract and body.

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Yuan Liu, Zhongyin Zhao, Le Tian, Haicheng Wang, Xubing Ye, Yangxiu You, Zilin Yu, Chuhan Wu, Zhou Xiao, Yang Yu, Jie Zhou

DebiasGAN: Eliminating Position Bias in News Recommendation with Adversarial Learning (2022.findings-emnlp)

Challenge:	Existing approaches to training document conversion models with manual annotation are costly and time-consuming, and training student models by distilling outputs from teacher models can significantly limit their performance in real-world applications.
Approach:	They propose a fully automated framework for constructing high-quality document extraction datasets and models capable of handling diverse document formats and layouts.
Outcome:	The proposed model outperforms existing models and improves on annotated documents.

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Chuhan Wu, Fangzhao Wu, Xiangnan He, Yongfeng Huang

Robustness in NLP

Challenge:	Existing news recommendation methods use click behaviors for interest inference and model training, but position biases can be inaccurate in targeting user interest.
Approach:	They propose a news recommendation method that eliminates position biases by adversarial learning by a candidate-aware click model and a bias-invariant click model.
Outcome:	The proposed method can effectively alleviate position biases on click behaviors and capture unbiased user interest.

Instruction-Tuning Data Synthesis from Scratch via Web Reconstruction (2025.findings-acl)

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Yuxin Jiang, Yufei Wang, Chuhan Wu, Xinyi Dai, Yan Xu, Weinan Gan, Yasheng Wang, Xin Jiang, Lifeng Shang, Ruiming Tang, Wei Wang

Low-Resource NLP

Challenge:	Existing methods for generating and curating high-quality instruction-tuning data rely heavily on the quality of seed data or strong assumptions about the structure and content of web documents.
Approach:	They propose a fully automated framework for synthesizing high-quality instruction-tuning (IT) data directly from raw web documents with minimal assumptions.
Outcome:	The proposed framework outperforms state-of-the-art baselines by 16.65% across four instruction-following benchmarks.

SceneAlign: Aligning Multimodal Reasoning to Scene Graphs in Complex Visual Scenes (2026.acl-long)

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Chuhan Wang, Xintong Li, Jennifer Yuntong Zhang, Junda Wu, Chengkai Huang, Lina Yao, Julian McAuley, Jingbo Shang

Multimodality

Challenge:	Existing preference-based approaches fail to address this challenge by exploiting language priors to bypass visual grounding.
Approach:	They propose a framework that leverages scene graphs as structured visual information to perform controllable structural interventions.
Outcome:	The proposed framework improves answer accuracy and reasoning faithfulness across seven visual reasoning benchmarks.

Named Entity Recognition in Multi-level Contexts (2020.aacl-main)

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Yubo Chen, Chuhan Wu, Tao Qi, Zhigang Yuan, Yongfeng Huang

Information Extraction & Text Mining

Challenge:	Existing methods for named entity recognition are unsatisfactory for recognizing entities in limited or ambiguous sentence-level contexts.
Approach:	They propose a framework to incorporate multi-level contexts for named entity recognition using TagLM as a baseline model and an auxiliary task to mine word-level contextual information.
Outcome:	The proposed framework is based on a set of sentence-level contexts and a document-level task to mine word-level contextual information.

NewsBERT: Distilling Pre-trained Language Model for Intelligent News Application (2021.findings-emnlp)

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Chuhan Wu, Fangzhao Wu, Yang Yu, Tao Qi, Yongfeng Huang, Qi Liu