Large Language Models Perform Diagnostic Reasoning

Autor:	Wu, Cheng-Kuang, Chen, Wei-Lin, Chen, Hsin-Hsi
Rok vydání:	2023
Předmět:	Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	We explore the extension of chain-of-thought (CoT) prompting to medical reasoning for the task of automatic diagnosis. Motivated by doctors' underlying reasoning process, we present Diagnostic-Reasoning CoT (DR-CoT). Empirical results demonstrate that by simply prompting large language models trained only on general text corpus with two DR-CoT exemplars, the diagnostic accuracy improves by 15% comparing to standard prompting. Moreover, the gap reaches a pronounced 18% in out-domain settings. Our findings suggest expert-knowledge reasoning in large language models can be elicited through proper promptings. Comment: Accepted as a Tiny Paper at ICLR 2023 (10 pages, 5 figures)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2307.08922 Zobrazit plný text záznamu View this record from Arxiv