Quantifying Faithful Confidence Expression in Large Reasoning Models
Yale NLP
This Yale NLP paper (arXiv 2606.03969) investigates whether large reasoning models faithfully express their actual uncertainty. The authors compare linguistic confidence signals against three internal uncertainty measures: token probabilities, hidden states, and response sampling consistency. Key findings: (1) reasoning capability does not automatically improve calibration; (2) standard prompting techniques do not transfer to reasoning models; (3) different internal uncertainty measures yield conflicting results, revealing fragility in existing evaluation methodologies.
Почему это важно
As reasoning models are deployed in high-stakes settings, faithful uncertainty communication is safety-critical. The paper establishes that large reasoning models have a distinct, unresolved calibration problem separate from general LLMs.
Важность: 2/5
Verified arxiv paper from Yale NLP; addresses safety-critical calibration failures specific to reasoning models.