A literature review of reasoning-LLM techniques for medicine, from CoT prompting to RL-trained medical models, with no new experiments and several placeholder citations.
On the Performance of an Explainable Language Model on PubMedQA
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abstract
Large language models (LLMs) have shown significant abilities in retrieving medical knowledge, reasoning over it and answering medical questions comparably to physicians. However, these models are not interpretable, hallucinate, are difficult to maintain and require enormous compute resources for training and inference. In this paper, we report results from Gyan, an explainable language model based on an alternative architecture, on the PubmedQA data set. The Gyan LLM is a compositional language model and the model is decoupled from knowledge. Gyan is trustable, transparent, does not hallucinate and does not require significant training or compute resources. Gyan is easily transferable across domains. Gyan-4.3 achieves SOTA results on PubmedQA with 87.1% accuracy compared to 82% by MedPrompt based on GPT-4 and 81.8% by Med-PaLM 2 (Google and DeepMind). We will be reporting results for other medical data sets - MedQA, MedMCQA, MMLU - Medicine in the future.
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Reasoning LLMs in the Medical Domain: A Literature Survey
A literature review of reasoning-LLM techniques for medicine, from CoT prompting to RL-trained medical models, with no new experiments and several placeholder citations.