Marker Internal Confidence (MIC)

Systematic Study of LLM Linguistic Uncertainty Markers and Intrinsic Confidence Calibration

This paper introduces 'marker internal confidence' (MIC) as a formalization of the intrinsic confidence a model associates with epistemic markers (e.g., 'it is likely...') in a given task domain. The authors present 7 metrics to evaluate MIC stability within and across distributions, finding that LLMs remain miscalibrated even under model-centric interpretation of marker meanings. Models struggle to differentiate markers by internal confidence across distributions, though they preserve a somewhat consistent ranking order across tasks. The work provides complementary evidence toward understanding faithful calibration in LLMs and highlights the need for more stable, aligned marker use.