Trained on Tokens, Calibrated on Concepts: The Emergence of Semantic Calibration in LLMs — mentioned by andy12_

Sun, 01 Mar 2026 09:43:56 GMT

The model could report the confidence of its output distribution, but it isn't necessarily calibrated (that is, even if it tells you that it's 70% confident, it doesn't mean that it is right 70% of the time). Famously, pre-trained base models are calibrated, but they stop being calibrated when they are post-trained to be instruction-following chatbots [1]. Edit: There is also some other work that points out that chat models might not be calibrated at the token-level, but might be calibrated at the concept-level [2]. Which means that if you sample many answers, and group them by semantic…

Paper and discussion history. A mention is not an endorsement.

Trained on Tokens, Calibrated on Concepts: The Emergence of Semantic Calibration in LLMs · HackerPapers discussions

Trained on Tokens, Calibrated on Concepts: The Emergence of Semantic Calibration in LLMs — mentioned by andy12_