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Pei-Xing Wan

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#machine learning Preprint Sep 2026

PCQC: Privileged Counterfactual Question Credit for Multi-Turn Medical Dialogue

Privileged Counterfactual Question Credit (PCQC), which uses privileged patient information during training to learn from questions never asked, is introduced, which achieves 63.10% mean diagnostic accuracy, outperforming GRPO and ATPO by 4.38 and 4.21 percentage points, respectively.

Chen-Xuan Li, Jia-Yi Wan, Xin-Rong Chen et al. · 0 citations
#natural language process... Preprint Aug 2026

Beyond Information Seeking: Severity-Aware Question Supervision for Proactive Medical Dialogue

Proactive medical dialogue requires an agent to decide what to ask from incomplete patient information. Existing information-seeking approaches commonly prioritize questions that most reduce diagnostic uncertainty, but this criterion overlooks an important property of medical diagnosis: different diagnostic errors can...

Chen-Xuan Li, Xin-Rong Chen, Lu-Ya Zhang et al. · 0 citations
Aug 2026

A bimodal large language model reduces misalignment in patient education: A double-blinded randomized trial.

BACKGROUND Effective patient education requires accurate communication aligned with patients' emotional and semantical needs. Text-based large language models (LLMs) lack access to non-verbal cues, which may contribute to misaligned responses. METHODS We evaluated emotional and semantic misalignment in a text-based L...

Peixing Wan, Zigeng Huang, Haoquan Huang et al. · 0 citations

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