Papers by Xiaokun Yuan

2 papers
Unity in Diversity: Collaborative Pre-training Across Multimodal Medical Sources (2024.acl-long)

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Challenge: Current pre-training techniques rely on a limited scope of medical data, limiting the range of downstream tasks.
Approach: They propose a pre-training strategy that unifies patient data within individual sources and captures explicit and implicit correlations between patients across different sources.
Outcome: The proposed strategy bridges the gap between multimodal medical sources by aggregating patient data within individual sources and capturing explicit and implicit correlations between patients across sources.
TriPlay-RL: Tri-Role Self-Play Reinforcement Learning for LLM Safety Alignment (2026.acl-long)

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Challenge: Existing approaches to safety alignment of large language models rely on costly manual annotations or human review.
Approach: They propose a closed-loop reinforcement learning framework called TriPlay-RL that enables iterative collaboration among three roles with near-zero manual annotation.
Outcome: The proposed framework achieves 20%–50% improvement in adversarial effectiveness while preserving high output diversity while achieving 10%–30% gains in safety performance without degrading general reasoning capability.

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