Asking Is Not Enough: Protocol Sensitivity in LLM Confidence Calibration
📰 ArXiv cs.AI
LLM confidence calibration depends on protocol sensitivity, making comparison of token-probability scores and verbalized confidence complex
Action Steps
- Hold verbalized-confidence elicitation fixed using a single prompt template
- Vary measurement protocols to analyze sensitivity
- Compare token-probability scores and verbalized confidence using fixed and varied protocols
- Evaluate the impact of protocol sensitivity on LLM confidence calibration
- Apply findings to improve LLM confidence calibration in real-world applications
Who Needs to Know This
ML researchers and engineers working with LLMs need to understand protocol sensitivity to accurately calibrate confidence
Key Insight
💡 Protocol sensitivity affects comparison of token-probability scores and verbalized confidence in LLMs
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🚨 LLM confidence calibration depends on protocol sensitivity! 🤖
Key Takeaways
LLM confidence calibration depends on protocol sensitivity, making comparison of token-probability scores and verbalized confidence complex
Full Article
Title: Asking Is Not Enough: Protocol Sensitivity in LLM Confidence Calibration
Abstract:
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
Abstract:
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
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