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#llm-confidence

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papersTODAY 04:00 UTC

arXiv Paper Proposes Model-Agnostic Correctness Predictors for LLM Confidence

A new arXiv preprint introduces generalized correctness models, a method for predicting whether an LLM's answer is correct using patterns learned from historical behavior. The approach aims to produce confidence estimates that are both calibrated and transferable across different models. The authors frame the work as addressing the difficulty of obtaining reliable confidence signals for high-stakes or user-facing deployments.

calibration/llm-confidence/research/arxiv