The strength of clinical evidence is recoverable from language model representations but not from their stated grades
arXiv:2606.29034v1 Announce Type: cross Abstract: 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 indepen
The proliferation of LLMs in all domains, including sensitive fields like clinical evidence, necessitates rigorous evaluation of their reliability beyond stated confidence.
This research reveals a critical limitation and a potential workaround for using LLMs in high-stakes fields where accuracy and evidence strength are paramount.
The ability to recover evidence strength from LLM activations, even when not explicitly stated, opens pathways for more nuanced and trustworthy AI applications in clinical decision support and other fields.
- · Healthcare providers
- · AI developers
- · Patients
- · Clinical research
- · LLMs without robust interpretability
- · Misinformation
- · Undocumented biases in AI systems
This research directly improves the reliability and trustworthiness of AI models designed for critical applications.
It will drive the development of more sophisticated interpretability and confidence-calibration techniques for large language models across various industries.
Enhanced trust in AI systems could accelerate their adoption in highly regulated sectors, leading to significant societal impacts and efficiency gains.
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Read at arXiv cs.LG