Microsoft Research
Large language models (LLMs) have achieved unprecedented accuracy on medical question-answering benchmarks, showcasing their potential to revolutionize healthcare by supporting clinicians and patients. However, these benchmarks often fail to capture the full complexity of real-world medical scenarios. To truly harness the power of LLMs in healthcare, we must go beyond these benchmarks by introducing challenges […] The post MedFuzz: Exploring the robustness of LLMs on medical challenge problems appeared first on Microsoft Research. A chart showing the accuracy of various models in the MedQA benchmark with different numbers of MedFuzz attack attempts. The horizontal line is average human performance on USMLE exams (76.6%). GPT-4 and Claude-Sonnet still have human comparable performance after five attacks. BioMistral-7B is surprisingly robust to attacks. The horizontal line is the average score of human test takers on USMLE medical exams (76.6%). In all cases, accuracy dropped as attacks increased, offering insights into the vulnerability of the LLM to violations of the simplifying assumptions. Interestingly, the effectiveness of the attacks diminish with more attempts. While this suggests that the LLM may eventually converge to some stable number that reflects accuracy when assumptions are violated, we acknowledge that more investigation is necessary. Medical judgment based on stereotypes and biases, like those included in the example, can lead to misdiagnosis and inappropriate treatments that may be harmful to patients. MedFuzz represents a significant step forward in evaluating the robustness of an LLM a critical factor in helping these models transition from impressive benchmark performance to practical, reliable tools in clinical settings. For more details on the MedFuzz methodology and its implications, you can read the full research paper by Robert Osazuwa Ness, Katie Matton, Hayden Helm, Sheng Zhang, Junaid Bajwa, Carey E. Priebe, and Eric Horvitz. Opens in a new tabThe post MedFuzz: Exploring the robustness of LLMs on medical challenge problems appeared first on Microsoft Research.
Published: 2024-09-10T16:00:00
