S-EDL: Eliciting Self-Evidence from Sequence Likelihoods for Semantic Calibration of LLMs.

arXiv:2610.29832v1 Announce Type: cross Abstract: Calibrating large language models (LLMs) in open-ended generation is uniquely challenging because many distinct token sequences can express the same underlying meaning. Prior calibration techniques target eithe