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

Yawei Li, Jiazheng Li, David Rügamer, Bernd Bischl, Yulan He, Mina Rezaei · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.29832 · cited by 0 works (OpenAlex) · open-access PDF

German

Die Kalibrierung von großen Sprachmodellen (LLMs) in der Open-End-Generation ist einzigartig herausfordernd, weil viele verschiedene Token-Sequenzen die gleiche zugrunde liegende Bedeutung ausdrücken können. Vorherige Kalibrierungstechniken zielen entweder auf die feste Klassifizierungssicherheit oder die binäre Korrektheit einzelner Stränge ab, wodurch sie inhärent für die dynamische, multi-String-Natur der offenen terminalen semantischen Ergebnisse ungeeignet sind. Um diese Lücke zu überbrücken, führen wir S-EDL ein, eine neue Beweismethode, die LLMs für die semantische Kalibrierung nativ optimiert. Indem sie Beweise aus den Sequenzgleichheiten des Modells selbst hervorbringen, konstruiert S-ED

Machine translation (Meta M2M100); the English original follows.

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 either fixed-set classification confidence or the binary correctness of individual strings, making them inherently ill-suited for the dynamic, multi-string nature of open-ended semantic outcomes. To bridge this gap, we introduce S-EDL, a novel evidential method that natively optimizes LLMs for semantic calibration. By eliciting evidence from the sequence likelihoods of the model itself, S-EDL constructs a differentiable Dirichlet prior over prompt-specific semantic classes. Optimizing the evidential loss under this prior yields a calibration-aware training signal over variable semantic classes, completely bypassing the need for fixed multiple-choice options. Evaluations across four open-ended benchmarks and three models demonstrate that S-EDL substantially reduces semantic calibration error while improving accuracy. By aligning generative likelihoods with semantic correctness, this work establishes a framework for deploying trustworthy LLMs in complex, open-ended applications. Code is available at: https://github.com/sandylaker/s-edl.

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Topics: Computer science, Correctness, Artificial intelligence, Calibration, Sequence (biology), Natural language processing

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