Large Language Models Encode Clinical Knowledge, Karan Singhal et al. 44-page PDF

This paper explores the potential of Large Language Models (LLMs) in the medical field, specifically focusing on their ability to answer medical questions. The authors present a comprehensive study on the performance of LLMs in providing accurate, factual, and safe responses to medical queries. They also introduce a new benchmark, MultiMedQA, to evaluate the models’ performance across a range of medical and scientific domains.

1️⃣ The study demonstrates that LLMs, when scaled up, can encode medical knowledge and perform well in question-answering tasks. They outperform previous models trained on biomedical corpus, such as BioGPT and PubMedGPT, without any fine-tuning.

2️⃣ Despite their impressive performance, the study points out that scale alone is not sufficient. The models can still generate answers that are inappropriate for the safety-critical medical domain. However, with instruction prompt tuning, the models can improve in terms of accuracy, factuality, consistency, safety, harm, and bias.

3️⃣ The authors acknowledge several limitations in their study and propose future research directions. These include expanding the MultiMedQA benchmark, developing new LLM capabilities, and improving the approach to human evaluation.

This paper is worth reading as it provides a comprehensive analysis of the potential and limitations of LLMs in the medical field. It not only presents promising results but also acknowledges the challenges and proposes future research directions. The study is a significant step towards bringing these models closer to real-world clinical applications.

✍🏻 Large Language Models Encode Clinical Knowledge, Karan Singhal et al. 44-page PDF https://arxiv.org/abs/2212.13138. DOI: 10.1101/2023.05.23.445244

💡 What are your thoughts on the potential of Large Language Models in revolutionizing the medical field?

本文探讨了大型语言模型 (LLM) 在医学领域的潜力，特别关注它们回答医学问题的能力。作者对 LLM 在对医学查询提供准确、真实和安全的响应方面的表现进行了全面研究。他们还引入了一个新的基准 MultiMedQA，以评估模型在一系列医学和科学领域的表现。

1️⃣ 该研究表明，LLM在扩大规模时可以编码医学知识并在问答任务中表现出色。它们优于以前在生物医学语料库上训练的模型，例如 BioGPT和PubMedGPT，没有任何微调。

2️⃣ 尽管他们的表现令人印象深刻，但研究指出，仅靠规模是不够的。这些模型仍然可以生成不适合安全关键医学领域的答案。然而，通过指令提示调整，模型可以在准确性、真实性、一致性、安全性、危害和偏差方面得到改善。

3️⃣ 作者承认他们研究中的一些局限性并提出了未来的研究方向。其中包括扩展MultiMedQA 基准、开发新的LLM功能以及改进人工评估方法。

这篇论文值得一读，因为它全面分析了法学硕士在医学领域的潜力和局限性。它不仅提出有希望的结果，而且承认了挑战并提出了未来的研究方向。该研究是使这些模型更接近现实世界临床应用的重要一步。

✍🏻 大型语言模型编码临床知识，Karan Singhal等人，44页便携文档https://arxiv.org/abs/2212.13138; DOI: 10.1101/2023.05.23.445244

💡 您对大型语言模型在医学领域革命中的潜力有何看法？

Share & Translate: Chinou Gea (秦陇纪) @2023 DSS-SDS, IFS-AHSC. Data Simplicity Community Facebook Group https://m.facebook.com/groups/290760182638656/ #DataSimp #DataScience #computing #program #IoT #IT #AI #ArtificialIntelligence #MachineLearning #ML #PatternRecognition #ethics #deeplearning