arXiv:2605.27752v1 Announce Type: new Abstract: LLM confidence calibration is often evaluated by comparing two signals: token-probability scores and verbalized confidence. These signals are sometimes treated as direct readouts of model uncertainty, but their comparison depends on measurement choices that are rarely made explicit. In the main analysis, we hold the verbalized-confidence elicitation fixed: a single prompt template, probability scale, and output format. We then vary the measurement axes that define the verbalized-vs-token comparison: which answer string receives the token-probabil
The proliferation of LLMs necessitates robust methods for assessing their reliability and understanding their internal 'confidence,' making this research timely.
Improving the calibration of LLM confidence is crucial for deploying these models in high-stakes environments where trust and accuracy are paramount.
This research highlights the sensitivity of confidence calibration to measurement choices, suggesting that current evaluation methods may be less robust than assumed.
- · AI researchers
- · LLM developers focused on reliability
- · Ethical AI advocates
- · Uncalibrated LLM applications
- · Users relying on superficial LLM confidence metrics
More rigorous standards for evaluating and reporting LLM confidence will emerge.
Improved confidence calibration could lead to safer and more reliable deployments of AI agents in critical applications.
Increased transparency in LLM 'thinking' might accelerate trust and adoption, but also expose new limitations.
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Read at arXiv cs.AI