This repository contains the source code for the paper Large Language Models Perform Diagnostic Reasoning, which is accepted as a Tiny Paper at The Eleventh International Conference on Learning Representations (ICLR 2023) in Kigali, Rwanda. In this work, we propose DR-CoT prompting, which elicits the diagnostic reasoning ability of LLMs and exhibit striking empirical improvements for automatic diagnosis.
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.
WIP
We will update the citation format once the paper is included in the proceedings.
@misc{
wu2023large,
title={Large Language Models Perform Diagnostic Reasoning},
author={Cheng-Kuang Wu and Wei-Lin Chen and Hsin-Hsi Chen},
year={2023},
url={https://openreview.net/forum?id=N0lQfjeNWOE}
}