Jeong, D.P., Garg, S., Lipton, Z.C. et al. Medical Adaptation of Large Language and Vision-Language Models: Are We Making Progress?. arXiv:2411.04118v1 [cs.CL], 6 Nov 2024.
This research paper investigates the effectiveness of domain-adaptive pretraining (DAPT) for specializing large language models (LLMs) and vision-language models (VLMs) in the medical domain, specifically focusing on their performance in question-answering (QA) tasks.
The authors conducted a head-to-head comparison of seven medical LLMs and two medical VLMs against their general-domain base models on 13 textual and 8 visual QA datasets. They employed zero-shot and few-shot prompting techniques, optimizing the prompt format and example selection for each model independently. Statistical significance was assessed using the percentile bootstrap method.
The study suggests that state-of-the-art general-domain LLMs and VLMs may already possess significant medical knowledge and reasoning capabilities. The authors argue that claims of improved performance through medical DAPT should be supported by rigorous head-to-head comparisons with appropriate prompt optimization and statistical analysis.
This research highlights the importance of careful evaluation and interpretation of performance gains attributed to domain adaptation in LLMs and VLMs for medical applications. It emphasizes the need for standardized evaluation protocols and cautious claims regarding the benefits of DAPT.
The study focused on closed-ended medical QA tasks and did not explore fine-tuning or other medical applications of LLMs and VLMs. Future research could investigate the effectiveness of DAPT on a wider range of tasks and explore alternative domain adaptation techniques.
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by Daniel P. Je... at arxiv.org 11-07-2024
https://arxiv.org/pdf/2411.04118.pdfDeeper Inquiries