Papers by Wenjun Wu
End-to-End Emotion-Cause Pair Extraction with Graph Convolutional Network (2020.coling-main)
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| Challenge: | Emotion-cause pair extraction (ECPE) aims to extract emotion expressions and their corresponding causes in a document simultaneously. |
| Approach: | They propose to model pair-level contexts so that to capture dependency information among local neighborhood candidate pairs. |
| Outcome: | The proposed model extracts emotion-cause pairs and their causes from documents . it is based on a benchmark Chinese emotion-case pair extraction corpus . |
GeoLaux: A Benchmark for Evaluating MLLMs’ Geometry Performance on Long-Step Problems Requiring Auxiliary Lines (2026.acl-long)
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| Challenge: | Existing benchmarks for Geometry problem solving lack fine-grained evaluation for long-step problems necessitating auxiliary line construction. |
| Approach: | They present a fine-grained annotated dataset with long-step reasoning and auxiliary line construction that provides a detailed evaluation of 23 leading MLLMs. |
| Outcome: | The proposed model performs significantly worse on long-step problems than short-step ones, with 18 models showing a performance drop of over 50%. |
RMoA: Optimizing Mixture-of-Agents through Diversity Maximization and Residual Compensation (2025.findings-acl)
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| Challenge: | Multi-agent systems based on large language models are limited by high computational overhead, information loss, and robustness. |
| Approach: | They propose a Residual Mixture-of-Agents (RMoA) that integrates residual connections to optimize efficiency and reliability. |
| Outcome: | The proposed model achieves state-of-the-art performance on benchmarks of alignment, mathematical reasoning, code generation, and multitasking understanding, while significantly reducing computational overhead. |
TriEx: A Game-based Tri-View Framework for Explaining Internal Reasoning in Multi-Agent LLMs (2026.acl-long)
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| Challenge: | Existing explainability methods for large language models have been limited in capturing interaction-dependent belief dynamics and multi-agent reasoning. |
| Approach: | They propose a tri-view explainability framework that instruments sequential decision making with aligned artifacts. |
| Outcome: | The proposed framework enables analysis of explanation faithfulness, belief dynamics, and evaluator reliability, revealing systematic mismatches between what agents say, what they believe, and what they do. |
Soft Knowledge Prompt: Help External Knowledge Become a Better Teacher to Instruct LLM in Knowledge-based VQA (2024.acl-long)
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| Challenge: | Recent research focuses on improving prediction performance and reliability of LLM. |
| Approach: | They propose a method to actively extract valuable information from the knowledge to produce a latent vector as a soft prompt, which is fused with the image embedding to form a knowledge-enhanced context to instruct LLM. |
| Outcome: | The proposed method improves performance on knowledge-based VQA benchmarks. |
DemMA: Dementia Multi-Turn Dialogue Agent with Expert-Guided Reasoning and Action Simulation (2026.findings-acl)
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Yutong Song, Jiang Wu, Kazi Shaharair Sharif, Pengfei Zhang, Wenjun Huang, Honghui Xu, Nikil Dutt, Amir M. Rahmani
| Challenge: | Simulating dementia patients with large language models is challenging due to the need to model cognitive impairment, emotional dynamics, and nonverbal behaviors over long conversations. |
| Approach: | They propose an expert-guided dementia dialogue agent for multi-turn patient simulation . they introduce a framework that trains a single LLM to jointly generate reasoning traces, patient utterances, and aligned behavioral actions . |
| Outcome: | The proposed model outperforms baselines in persona fidelity, clinical validity, and educational effectiveness. |
STELLA: A Multimodal LLM for Protein Functional Annotation via Unified Sequence-Structure Encoding (2026.findings-acl)
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Hongwang Xiao, Wenjun Lin, Xi Chen, Hui Wang, Kai Chen, Jiashan Li, Yuancheng Sun, Sicheng Dai, Boya Wu, Qiwei Ye
| Challenge: | a multimodal protein language model (LLM) integrates sequence, structure, and function into functional annotation. |
| Approach: | They propose a multimodal protein language model that synergistically aligns bimodal representations with the textual modality to advance protein functional annotation. |
| Outcome: | The proposed model synergizes bimodal representations with the textual modality to advance protein functional annotation. |
Are You Copying My Model? Protecting the Copyright of Large Language Models for EaaS via Backdoor Watermark (2023.acl-long)
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Wenjun Peng, Jingwei Yi, Fangzhao Wu, Shangxi Wu, Bin Bin Zhu, Lingjuan Lyu, Binxing Jiao, Tong Xu, Guangzhong Sun, Xing Xie
| Challenge: | Large language models (LLMs) have demonstrated exceptional abilities in both text understanding and generation. |
| Approach: | They propose an Embedding Watermark method that implants backdoors on embeddings to protect copyright of large language models. |
| Outcome: | The proposed method protects the copyright of large language models without compromising service quality while minimizing the adverse impact on the original embeddings’ utility. |
Diagram-Driven Course Questions Generation (2025.emnlp-main)
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Xinyu Zhang, Lingling Zhang, Yanrui Wu, Muye Huang, Wenjun Wu, Bo Li, Shaowei Wang, Basura Fernando, Jun Liu
| Challenge: | Visual Question Generation (VQG) research focuses on natural images while neglecting diagrams, a critical component of educational materials. |
| Approach: | They propose a diagram-driven course questions generation task to generate diagram-relevant questions for specific courses. |
| Outcome: | The proposed framework outperforms existing models on DiagramQG while maintaining strong generalizability across natural image datasets. |
Music Audio-Visual Question Answering Requires Specialized Multimodal Designs (2026.findings-acl)
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Wenhao You, Xingjian Diao, Wenjun Huang, Chunhui Zhang, Keyi Kong, Weiyi Wu, Chiyu Ma, Zhongyu Ouyang, Tingxuan Wu, Ming Cheng, Soroush Vosoughi, Jiang Gui
| 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 . |