Few shot chain-of-thought driven reasoning to prompt LLMs for open ended medical question answering

Researchers have proposed new methods to improve open-ended medical question answering using modified datasets and Chain of Thought (CoT) reasoning prompts.

A team of researchers has introduced a modified version of the MedQA-USMLE dataset, called MEDQA-OPEN, which includes open-ended medical questions without options to simulate real-world clinical scenarios^[1]. They have also developed a CoT reasoning prompt, CLINICR, designed to mimic the incremental reasoning process used by clinicians. According to the researchers, CLINICR outperforms the state-of-the-art 5-shot CoT-based prompt. Additionally, they have implemented a reward model mechanism to replace the elimination process used in previous methods. Another study proposed a dual-path retrieval framework, Hybrid-IR, which integrates graph-based retrieval and dense retrieval for complex medical question answering^[2]. The framework uses an iterative retrieve-reason loop to refine the reasoning trajectory, addressing the limitations of retrieval-augmented generation methods. Large language models are prone to hallucinations and outdated knowledge, making these advancements crucial for improving medical question answering.