Quantifying Hallucinations in Language Language Models on Medical Textbooks
Quick summary
arXiv:2603.09986v3 Announce Type: replace-cross Abstract: Hallucinations, the tendency for large language models to provide responses with factually incorrect and unsupported claims, is a serious problem within natural language processing for which we do not yet have an effective solution to mitigate against. Existing benchmarks for medical QA rarely evaluate this behavior against a fixed evidence source. We ask how often hallucinations occur on textbook-grounded QA and how responses to medical QA prompts vary across models. We conduct two experiments, the first experiment to determine the pre
Key takeaways
- arXiv:2603.09986v3 Announce Type: replace-cross Abstract: Hallucinations, the tendency for large language models to provide responses with factually incorrect and unsupported claims, is a serious problem within natural language processing for which we do not yet have an effective solution to mitigate against.
- Existing benchmarks for medical QA rarely evaluate this behavior against a fixed evidence source.
- We ask how often hallucinations occur on textbook-grounded QA and how responses to medical QA prompts vary across models.
Why it matters
The importance of “Quantifying Hallucinations in Language Language Models on Medical Textbooks” will be measured by what changes in practice. User behavior, access conditions, verifiable performance and responsible-use outcomes are the signals worth following.
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