Papers by Xun Xiao
LLaVA Steering: Visual Instruction Tuning with 500x Fewer Parameters through Modality Linear Representation-Steering (2025.acl-long)
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| Challenge: | Multimodal Large Language Models (MLLMs) enhance visual tasks by integrating visual representations into large language models. |
| Approach: | They propose a method to re-balance modalities by steering visual representations . they propose LLaVA Steering, a platform that enables rapid customization of MLLMs a component-based architecture . |
| Outcome: | The proposed model re-balances the modalities of visual representations in large language models . the model requires 500 times fewer trainable parameters than LoRA while maintaining comparable performance . |
World to Code: Multi-modal Data Generation via Self-Instructed Compositional Captioning and Filtering (2024.emnlp-main)
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| Challenge: | Recent advances in Vision-Language Models and the scarcity of high-quality multi-modal alignment data have inspired numerous researches on synthetic VLM data generation. |
| Approach: | They propose a multi-modal data construction pipeline that organizes the final output into a Python code format. |
| Outcome: | The proposed pipeline improves visual question answering and visual grounding benchmarks across different VLMs. |
MultiAgentESC: A LLM-based Multi-Agent Collaboration Framework for Emotional Support Conversation (2025.emnlp-main)
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| Challenge: | Existing studies focus on generating responses directly and neglect integration of domain-specific reasoning and expert interaction. |
| Approach: | They propose a training-free multi-agent collaboration framework for ESC to emulate human-like process of providing emotional support through dialogue analysis, strategy deliberation, and response generation. |
| Outcome: | The proposed framework excels at providing emotional support and diversifying support strategy selection. |
Navigating the OverKill in Large Language Models (2024.acl-long)
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Chenyu Shi, Xiao Wang, Qiming Ge, Songyang Gao, Xianjun Yang, Tao Gui, Qi Zhang, Xuanjing Huang, Xun Zhao, Dahua Lin
| Challenge: | Recent studies have highlighted a tendency among large language models to refuse to answer benign queries. |
| Approach: | They propose a model-agnostic approach to reduce excessive attention to harmful words like ‘kill’ and a method to decode the next-token predictions by contrastive decoding. |
| Outcome: | The proposed approach reduces the refusal rate by 20% while having little impact on safety. |