The strength of clinical evidence is recoverable from language model representations but not from their stated grades
Large language models (LLMs) increasingly summarize clinical evidence, where a claim's weight depends on how strongly it is supported. Yet these models convey confidence poorly, and properties they never state, such as truth, are often readable from their activations. Whether a clinical model registers evidence strength, distinct from truth, and states it when asked is untested, and any such signal could be lexical. We compiled 45,134 clinical claims from six public sources, harmonized 20,611 into a four-level evidence grade under three independent frameworks, and tested 22 local, open-weight LLMs from several developers (0.6-70 billion parameters; general, medical, and reasoning), with l...
| Authors | Soroosh Tayebi Arasteh |
|---|---|
| Primary category | cs.CL |
| Published | 27 June 2026 |
| Last updated at source | 27 June 2026 |
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