Feb. 14, 2024, 5:46 a.m. | Taeyoon Kwon Kai Tzu-iunn Ong Dongjin Kang Seungjun Moon Jeong Ryong Lee Dosik Hwang Yongsik Sim

cs.CL updates on arXiv.org arxiv.org

Machine reasoning has made great progress in recent years owing to large language models (LLMs). In the clinical domain, however, most NLP-driven projects mainly focus on clinical classification or reading comprehension, and under-explore clinical reasoning for disease diagnosis due to the expensive rationale annotation with clinicians. In this work, we present a ``reasoning-aware'' diagnosis framework that rationalizes the diagnostic process via prompt-based learning in a time- and labor-efficient manner, and learns to reason over the prompt-generated rationales. Specifically, we address …

annotation classification clinical clinicians cs.ai cs.cl diagnosis disease disease diagnosis domain explore focus framework generated language language models large language large language models llms machine nlp progress projects prompt reading reasoning

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