Summary of Enabling Scalable Evaluation Of Bias Patterns in Medical Llms, by Hamed Fayyaz et al.
Enabling Scalable Evaluation of Bias Patterns in Medical LLMs
by Hamed Fayyaz, Raphael Poulain, Rahmatollah Beheshti
First submitted to arxiv on: 18 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper introduces a new method for evaluating bias in large language models (LLMs) designed for medical applications. The proposed approach automates the generation of test cases based on rigorous medical evidence, addressing challenges related to domain-specificity, hallucination, and dependencies between health outcomes and sensitive attributes. The authors integrate their generative pipeline with medical knowledge graphs, ontologies, and customized LLM evaluation frameworks. Through extensive experiments, they demonstrate that their method can effectively reveal bias patterns in Med LLMs at larger scales than human-crafted datasets. A large bias evaluation dataset is published for a few medical case studies, along with a live demo of the application for vignette generation. The code is available on GitHub. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure that computer programs designed to help doctors are fair and don’t make mistakes because they were trained on biased data. The authors created a new way to test these programs by using medical information to create fake scenarios. They tested their method with several real-life medical cases and found it was effective in detecting biases. This is important because we want computer programs that can help doctors make good decisions, not ones that might harm patients because of unfair biases. |
Keywords
» Artificial intelligence » Hallucination
