Summary of Medsafetybench: Evaluating and Improving the Medical Safety Of Large Language Models, by Tessa Han et al.
MedSafetyBench: Evaluating and Improving the Medical Safety of Large Language Models
by Tessa Han, Aounon Kumar, Chirag Agarwal, Himabindu Lakkaraju
First submitted to arxiv on: 6 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The abstract discusses the medical safety of large language models (LLMs) in medical settings, as their capabilities and applications have far-reaching implications for personal and public health. The notion of medical safety is defined based on the American Medical Association’s Principles of Medical Ethics, and a benchmark dataset called MedSafetyBench is introduced to measure and improve the medical safety of LLMs. Publicly-available medical LLMs do not meet standards of medical safety, but fine-tuning them using MedSafetyBench improves their medical safety while preserving performance. The work enables a systematic study of medical safety in LLMs and motivates future research to mitigate safety risks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are super smart computers that can understand and generate human-like text. But they’re being used in medicine, which is really important for people’s health. There’s no way to check if these computers are safe, so scientists want to change that. They came up with a new way to measure how safe these computers are when they’re used in medicine. They found that the computers aren’t very safe right now, but by making them better, they can be safer and still do their job well. |
Keywords
» Artificial intelligence » Fine tuning
