Use of large language models for generative tasks in critical domains like medicine is fraught with challenges like hallucination. In the domain of medicine, hallucination may take a unique shape where the LLM-generated language is not inaccurate but the suggested treatment or medication has now been discontinued in a specific context. Reinforcement learning based solutions for building reliable LLM-based frameworks are limited by the fact that the reinforcement is typically focused on only identifying the mistake; correcting the mistake is left up to the primary LLM. We propose an innovative solution where a two-phase question answering framework composed of two LLMs is designed such that one LLM learns to generate answers while the other learns to correct any mistakes in the answer generated by the first model. We experimented with the particular domain of prostate cancer and LLMs designed for various domains and showed that domain-specific LLMs outperform generic or wide-domain LLMs.

The authors have declared no competing interest.

This study did not receive any funding

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The study was conducted by transforming the text of prostate cancer treatment guidelines of American Cancer Society into question-answer pairs. The treatment guidelines are available at https://www.cancer.org/cancer/types/prostate-cancer/treating.html Curated question-answer pairs can be made available upon reasonable request.

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All data produced in the present study are available upon reasonable request to the authors.