Papers by Richard Yi Da Xu

2 papers
Knowledge-Augmented Multimodal Clinical Rationale Generation for Disease Diagnosis with Small Language Models (2025.acl-long)

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Challenge: Existing models struggle to balance predictive accuracy with human-understandable rationales.
Approach: They propose to enhance LLMs by leveraging rationale distillation and domain knowledge injection for trustworthy multimodal rationale generation.
Outcome: Experiments on real-world medical datasets show that ClinRaGen achieves state-of-the-art performance in disease diagnosis and rationale generation.
ProMedTS: A Self-Supervised, Prompt-Guided Multimodal Approach for Integrating Medical Text and Time Series (2025.findings-acl)

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Challenge: Large language models excel at processing unstructured data, but integrating time series data with text remains a challenge.
Approach: They propose a self-supervised multimodal framework that uses prompt-guided learning to unify heterogeneous data types.
Outcome: The proposed framework outperforms state-of-the-art approaches on disease diagnosis tasks using real-world datasets.

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