Papers by You Wu

ReasonBERT: Pre-trained to Reason with Distant Supervision (2021.emnlp-main)

Copied to clipboard

Xiang Deng, Yu Su, Alyssa Lees, You Wu, Cong Yu, Huan Sun

Challenge:	Existing pre-training methods only harvest learning signals from local contexts of naturally occurring texts . ReasonBert provides a method for reasoning over long-range relations and multiple, possibly hybrid contexts.
Approach:	They propose a method that augments language models with the ability to reason over long-range relations and multiple, possibly hybrid contexts.
Outcome:	The proposed method significantly improves sample efficiency over strong baselines.

Superpose Task-specific Features for Model Merging (2025.emnlp-main)

Copied to clipboard

Haiquan Qiu, You Wu, Dong Li, Jianmin Guo, Quanming Yao

Challenge:	Existing methods for model merging are limited by resource demands . recent studies validate the linear representation hypothesis .
Approach:	They propose a method that superposes task-specific features from individual models into a merged model.
Outcome:	The proposed method outperforms existing methods on multiple benchmarks and models.

Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What Matters (2023.acl-long)

Copied to clipboard

Boshi Wang, Sewon Min, Xiang Deng, Jiaming Shen, You Wu, Luke Zettlemoyer, Huan Sun

Low-Resource NLP

Challenge:	Chain-of-Thought (CoT) prompting can dramatically improve the multi-step reasoning abilities of large language models (LLMs).
Approach:	They propose to use Chain-of-Thought (CoT) prompting to encourage the LLM to generate intermediate rationales for solving a problem by providing a series of reasoning steps in the demonstrations.
Outcome:	The proposed model can generate coherent lines of reasoning even with invalid demonstrations while still generating coherent lines during inference.

RouteMoA: Dynamic Routing without Pre-Inference Boosts Efficient Mixture-of-Agents (2026.acl-long)

Copied to clipboard

Jize Wang, Han Wu, Zhiyuan You, Yiming Song, Yijun Wang, Zifei Shan, Yining Li, Songyang Zhang, Xinyi Le, Cailian Chen, Xinping Guan, Dacheng Tao

Green & Sustainable NLP

Challenge:	Existing methods for mixing-of-agents (MoA) lack model selection criteria and struggle with large model pools.
Approach:	They propose a mixture-of-agents framework with dynamic routing that uses a lightweight scorer to perform initial screening and refines the model scores through self- and cross-assessment.
Outcome:	The proposed framework outperforms existing methods for large model pools and tasks . it reduces cost by 89.8% and latency by 63.6% in the large-scale model pool.

EvoHyper: Evolving Hypergraph Topologies for Unified Collaboration in Multi-Agent Communication (2026.findings-acl)

Copied to clipboard

Heng Zhang, Yihao Zhong, Lubin Gan, Zhihe Chen, Jiajun Wu, Yuling Shi, Xiaodong Gu, Hao Zhang, Haochen You, Jin Huang

Challenge:	Existing methods for multi-agent collaboration use a fixed communication graph and manage collaboration structure and shared memory in separate modules.
Approach:	They propose a framework that uses an evolving hypergraph topology for multi-agent collaboration.
Outcome:	The proposed framework achieves 3.2% to 7.8% accuracy gains over state-of-the-art methods and efficient, reducing token consumption by up to 23.5%.

MMSearch-R1: Incentivizing LMMs to Search (2026.acl-long)

Copied to clipboard

Jinming Wu, Zihao Deng, Wei Li, Yiding Liu, Bo You, Bo Li, Zejun MA, Ziwei Liu

Multimodality

Challenge:	Existing approaches to deploying large multimodal models rely on rigid pipelines . Existing methods such as retrieval-augmented generation and prompt engineered search rely only on rigid knowledge sources.
Approach:	They propose a framework that enables LMMs to perform on-demand, multi-turn search in real-world Internet environments.
Outcome:	The proposed model outperforms existing models while reducing search calls by over 30%.

Music Audio-Visual Question Answering Requires Specialized Multimodal Designs (2026.findings-acl)

Copied to clipboard

Wenhao You, Xingjian Diao, Wenjun Huang, Chunhui Zhang, Keyi Kong, Weiyi Wu, Chiyu Ma, Zhongyu Ouyang, Tingxuan Wu, Ming Cheng, Soroush Vosoughi, Jiang Gui

Multimodality

Challenge:	Music audio-visual question answering presents unique challenges with dense audio-visual content, intricate temporal dynamics, and the need for domain-specific knowledge.
Approach:	They analyze Music AVQA datasets and analyze their results to identify key design patterns . they propose concrete future directions for incorporating musical priors .
Outcome:	The proposed architectures are critical for success in Music AVQA, the authors argue . they suggest concrete future directions for incorporating musical priors .

A Systematic Study of Cross-Layer KV Sharing for Efficient LLM Inference (2025.naacl-short)

Copied to clipboard

You Wu, Haoyi Wu, Kewei Tu

Language Models

Challenge:	Recent studies have shown that sharing key-value (KV) cache across layers is effective in efficient inference of large language models.
Approach:	They propose a unified framework that covers several recent methods and their novel variants to investigate cross-layer KV sharing.
Outcome:	The proposed framework achieves higher throughput and better performance when reducing the size of the key-value cache by 2 while maintaining competitive performance.

LCAN: A Label-Aware Contrastive Attention Network for Multi-Intent Recognition and Slot Filling in Task-Oriented Dialogue Systems (2025.findings-emnlp)

Copied to clipboard

Shuli Zhang, Zhiqiang You, Xiao Xiang Qi, Peng Liu, Gaode Wu, Kan Xia, Shenguang Huang

Semantic Parsing

Challenge:	Multi-intent utterances processing remains a persistent challenge due to intricate intent-slot dependencies and semantic ambiguities.
Approach:	They propose a label-aware contrastive attention network (LCAN) that integrates label-based attention and contrastive learning strategies to improve semantic understanding and generalization in multi-intent scenarios.
Outcome:	The proposed model improves intent recognition and slot filling performance in multi-intent dialogue systems.

Evaluating the Expressive Appropriateness of Speech in Rich Contexts (2026.acl-long)

Copied to clipboard

Tianrui Wang, Ziyang Ma, Yizhou Peng, Haoyu Wang, Zhikang Niu, Zikang Huang, Yihao Wu, Yi-Wen Chao, Yu Jiang, Yuheng Lu, Guanrou Yang, Xuanchen Li, Hexin Liu, Chunyu Qiang, Cheng Gong, Yifan Yang, Tianchi Liu, Junyu Wang, Nana Hou, Meng Ge, Fuming You, Yang Wei, Zhongqian Sun, Hu Haifeng, Xiaobao Wang, Eng Siong Chng, Xie Chen, Longbiao Wang, Jianwu Dang

Speech & Audio in NLP

Challenge:	Existing methods for evaluating expressive speech focus on word accuracy, naturalness, signal quality, or emotional intensity at the utterance level.
Approach:	They propose a framework for Evaluating Expressive Appropriateness in speech that assesses whether a speech sample aligns with the underlying communicative intent implied by its discourse-level narrative context.
Outcome:	The proposed framework outperforms existing speech evaluation and analysis systems on a human-annotated test set.

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

Copied to clipboard

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

Language Models

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.

End-to-end Spoken Conversational Question Answering: Task, Dataset and Model (2022.findings-naacl)

Copied to clipboard

Chenyu You, Nuo Chen, Fenglin Liu, Shen Ge, Xian Wu, Yuexian Zou

Natural Language Interfaces

Challenge:	Existing methods for conversational question answering significantly degrade on datasets . a new task aims to enable systems to model complex dialogues flow given the speech documents .
Approach:	They propose a new Spoken Conversational Question Answering task to model human conversations . they propose DDNet, which ingests cross-modal information to achieve fine-grained representations of speech and language modalities.
Outcome:	The proposed method achieves superior performance in spoken conversational question answering.

A Survey of Generative Information Extraction (2025.coling-main)

Copied to clipboard

Zikang Zhang, Wangjie You, Tianci Wu, Xinrui Wang, Juntao Li, Min Zhang

Language Models

Challenge:	Information Extraction (IE) is a popular and fundamental task in natural language processing.
Approach:	They first review generative information extraction methods based on pre-trained language models and large language models focusing on their adaptation and generalization capabilities.
Outcome:	The proposed methods are based on pre-trained language models and large language models, and emphasize the importance of model collaboration.