Few shot chain-of-thought driven reasoning to prompt LLMs for open ended medical question answering
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Computer Science > Computation and Language
Title:Few shot chain-of-thought driven reasoning to prompt LLMs for open ended medical question answering
Abstract:In this paper, we propose a modified version of the MedQA-USMLE dataset, named MEDQA-OPEN, which contains open-ended medical questions without options to mimic clinical scenarios, along with clinician-approved reasoned answers. Additionally, we implement a prompt driven by Chain of Thought (CoT) reasoning, CLINICR, to mirror the prospective process of incremental reasoning, reaching a correct response to medical questions. We empirically demonstrate how CLINICR outperforms the state-of-the-art 5-shot CoT-based prompt (Liévin et al., 2022). We also present an approach that mirrors real-life clinical practice by first exploring multiple differential diagnoses through MCQ-CLINICR and subsequently narrowing down to a final diagnosis using MCQ-ELIMINATIVE. Finally, emphasizing the importance of response verification in medical settings, we utilize a reward model mechanism, replacing the elimination process performed by MCQ-ELIMINATIVE.
| Comments: | The paper is accepted in EMNLP 2024 Findings |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2403.04890 [cs.CL] |
| (or arXiv:2403.04890v4 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2403.04890
arXiv-issued DOI via DataCite
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Submission history
From: Saeel Nachane [view email][v1] Thu, 7 Mar 2024 20:48:40 UTC (9,608 KB)
[v2] Thu, 10 Oct 2024 22:04:32 UTC (11,320 KB)
[v3] Tue, 15 Oct 2024 21:03:11 UTC (9,855 KB)
[v4] Tue, 23 Jun 2026 03:21:15 UTC (9,853 KB)
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