[論文レビュー] Large Language Models Perform Diagnostic Reasoning

Cheng-Kuang Wu, Weilin Chen+1 more|arXiv (Cornell University)|Jul 18, 2023

Topic Modeling被引用数 9

ひとこと要約

DR-CoT prompting improves diagnostic accuracy of LLMs for automatic diagnosis by about 15% over standard prompting, with an 18% gain in out-domain settings.

ABSTRACT

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.

研究の動機と目的

• Motivate extending chain-of-thought prompting to medical reasoning for automatic diagnosis.
• Propose Diagnostic-Reasoning CoT (DR-CoT) to elicit expert-like reasoning in LLMs.
• Develop a few-shot LLM-based dialogue system for automatic diagnosis.
• Introduce a language-model-role-playing evaluation framework to simulate patient-doctor interactions.

提案手法

• Prompt LLMs with a two-shot DR-CoT template to guide evidence gathering and differential diagnosis generation.
• Augment the instruction to summarize evidence and formulate a differential diagnosis before formulating the next question.
• Replace standard prompts with a DR-CoT-driven prompt that ties evidence to a ranked differential and next query.
• Use a non-pipelined, few-shot dialogue setup where the model generates questions and a final diagnosis.
• Evaluate using a language-model-role-playing framework where the LLM acts as both doctor and patient in self-chat dialogues.
• Conduct experiments on the DDXPlus dataset with in-domain and out-domain splits.

Figure 3: The initial prompt includes the instruction I , the shots S , and the input D . The generated question $q_{i}$ of the prompted model (i.e., the DSAD) and the answer $a_{i}$ from the patient bot is presented in the remaining text in black.

実験結果

リサーチクエスチョン

• RQ1 Can DR-CoT prompting improve diagnostic accuracy of LLM-based automatic diagnosis compared to standard prompting?
• RQ2 Does DR-CoT generalize to out-domain initial evidences beyond the exemplars?
• RQ3 Does the DR-CoT approach lead to more informative questioning that supports correct diagnoses?
• RQ4 Is a language-model-role-playing evaluation framework a viable proxy for realistic DSAD assessment?

主な発見

• DR-CoT prompting yields a 15% improvement in diagnostic accuracy over standard prompting.
• The accuracy improvement with DR-CoT is 18% in out-domain settings.
• Two-shot exemplars with DR-CoT significantly enhance convergence speed and diagnostic performance.
• A physician-evaluated human study supports that DR-CoT prompts help the model ask more critical questions.
• The evaluation framework using role-playing between doctor and patient enables automated, end-to-end assessment.