Summary of Medfuzz: Exploring the Robustness Of Large Language Models in Medical Question Answering, by Robert Osazuwa Ness et al.
MedFuzz: Exploring the Robustness of Large Language Models in Medical Question Answering
by Robert Osazuwa Ness, Katie Matton, Hayden Helm, Sheng Zhang, Junaid Bajwa, Carey E. Priebe, Eric Horvitz
First submitted to arxiv on: 3 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 Large language models (LLMs) have achieved impressive performance on medical question-answering benchmarks, but this does not necessarily translate to real-world clinical settings. While LLMs learn broad knowledge that can help them generalize to practical conditions, current benchmarks rely on unrealistic assumptions that may not hold in the clinic. We propose MedFuzz, an adversarial method aimed at confounding LLMs by modifying benchmark questions to target strong assumptions about patient characteristics. Our approach demonstrates the ability of MedFuzzed benchmarks and individual successful attacks to provide insights into an LLM’s robustness in realistic settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models have gotten very good at answering medical questions, but this doesn’t mean they’ll do well in real hospitals. These models learn a lot, but the tests they take don’t match what happens in real life. We created MedFuzz to see how these models will do when things get tricky. It’s like trying to trick the model into giving an answer that’s not right. Our test shows that MedFuzz can help us understand if these models are good enough for everyday use. |
Keywords
» Artificial intelligence » Question answering
