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LLMs for Medical Q&A: New Reasoning Prompts and Knowledge-Graph Grounding Explored

By PulseAugur Editorial · [3 sources] ·

Researchers are exploring methods to improve Large Language Models (LLMs) for open-ended medical question answering. One approach involves a Chain of Thought (CoT) reasoning prompt called CLINICR, which aims to mimic clinical reasoning and has shown superior performance to existing 5-shot CoT prompts on modified datasets like MEDQA-OPEN. Another study investigates the effectiveness of knowledge-graph (KG) grounding, finding that it significantly boosts LLM accuracy only when the required information is outside the model's training data, particularly for novel or private knowledge, while offering little benefit for known facts. AI

IMPACT These studies suggest that advanced reasoning techniques and targeted knowledge integration can significantly enhance LLM capabilities in specialized domains like medicine, potentially leading to more reliable AI assistants in healthcare.

RANK_REASON Two arXiv papers presenting novel research on improving LLM performance for medical question answering.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

LLMs for Medical Q&A: New Reasoning Prompts and Knowledge-Graph Grounding Explored

COVERAGE [3]

  1. arXiv cs.CL TIER_1 English(EN) · Saeel Sandeep Nachane, Ojas Gramopadhye, Prateek Chanda, Ganesh Ramakrishnan, Kshitij Sharad Jadhav, Yatin Nandwani, Dinesh Raghu, Sachindra Joshi ·

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

    arXiv:2403.04890v4 Announce Type: replace 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. …

  2. arXiv cs.CL TIER_1 English(EN) · Madhulatha Mandarapu, Sandeep Kunkunuru ·

    Knowledge-Graph Grounding Helps LLMs Only for Out-of-Training Knowledge: A Controlled Study on Clinical Question Answering

    arXiv:2606.22419v2 Announce Type: replace Abstract: A recent Nature Medicine study reports that general-purpose frontier LLMs outperform specialized retrieval-augmented clinical tools on medical benchmarks, and that retrieval can hurt strong models. We ask the natural follow-up: …

  3. arXiv cs.CL TIER_1 English(EN) · Sandeep Kunkunuru ·

    Knowledge-Graph Grounding Helps LLMs Only for Out-of-Training Knowledge: A Controlled Study on Clinical Question Answering

    A recent Nature Medicine study reports that general-purpose frontier LLMs outperform specialized retrieval-augmented clinical tools on medical benchmarks, and that retrieval can hurt strong models. We ask the natural follow-up: does structured knowledge-graph (KG) grounding chang…

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