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This work proposes XConf (eXperiential Confidence): estimating confidence together with the model's accumulated experience, and sees experiential confidence estimation as a new paradigm for future general-purpose confidence estimation.
It is shown that raw global calibration metrics are not robust for cross-model comparison, and that fair calibration comparison requires accuracy-aware evaluation, and proposed ACE, an accuracy-controlled evaluation framework, is proposed.
This work conducts the first large-scale, systematic studies of multilingual calibration across six model families and over 100 languages, revealing that non-English languages suffer from systematically worse calibration.
For the coarse attributes the authors study, MLLMs encode the visual evidence but cannot reliably control their reliance on it, indicating that for the coarse attributes they study, MLLMs cannot reliably control their reliance on it.
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