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    <abstract>Reliable confidence estimation is essential for large language model deployment. However, answer-level calibration remains challenging because generation errors are often localized: a response may be fluent and high-probability overall while still failing at a critical number, entity, or factual claim. Existing estimators compress token probabilities, sequence likelihoods, entropy, or beam statistics into a global score, which can dilute such local risk signals. We propose TRACE, a single-pass, decoded-answer-preserving confidence estimator that treats decoding-time uncertainty as a trajectory through three steps: (i) recording token-level surprisal and predictive entropy during decoding, (ii) applying local risk operators to preserve uncertainty spikes, and (iii) converting localized trace risk into answer-level confidence. TRACE produces a label-free risk score, while TRACE+ calibrates trace-only features into probabilities using a held-out split, without extra generations or external verifiers. We evaluate four tasks against 19 calibration baselines, and TRACE+ reduces Brier from 0.149 to 0.137 and improves AUROC from 0.758 to 0.792 over the strongest likelihood baseline. Across seven LLMs, TRACE+ improves over the best non-TRACE baseline pool from 0.136 to 0.120 Brier and from 0.764 to 0.817 AUROC. Results show localizing decoding-time risk provides a general approach to calibration.</abstract>
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%0 Conference Proceedings
%T TRACE: Single-Pass Decoding-Trace Risk Localization for Generation Calibration
%A Xu, Yuebin
%A Peng, Xuemei
%A Chen, Junlan
%A Chen, Zhiyi
%A Wen, Zeyi
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F xu-etal-2026-trace
%X Reliable confidence estimation is essential for large language model deployment. However, answer-level calibration remains challenging because generation errors are often localized: a response may be fluent and high-probability overall while still failing at a critical number, entity, or factual claim. Existing estimators compress token probabilities, sequence likelihoods, entropy, or beam statistics into a global score, which can dilute such local risk signals. We propose TRACE, a single-pass, decoded-answer-preserving confidence estimator that treats decoding-time uncertainty as a trajectory through three steps: (i) recording token-level surprisal and predictive entropy during decoding, (ii) applying local risk operators to preserve uncertainty spikes, and (iii) converting localized trace risk into answer-level confidence. TRACE produces a label-free risk score, while TRACE+ calibrates trace-only features into probabilities using a held-out split, without extra generations or external verifiers. We evaluate four tasks against 19 calibration baselines, and TRACE+ reduces Brier from 0.149 to 0.137 and improves AUROC from 0.758 to 0.792 over the strongest likelihood baseline. Across seven LLMs, TRACE+ improves over the best non-TRACE baseline pool from 0.136 to 0.120 Brier and from 0.764 to 0.817 AUROC. Results show localizing decoding-time risk provides a general approach to calibration.
%U https://aclanthology.org/2026.findings-emnlp.253/
%P 5318-5335

Markdown (Informal)

[TRACE: Single-Pass Decoding-Trace Risk Localization for Generation Calibration](https://aclanthology.org/2026.findings-emnlp.253/) (Xu et al., Findings 2026)

• TRACE: Single-Pass Decoding-Trace Risk Localization for Generation Calibration (Xu et al., Findings 2026)

ACL

• Yuebin Xu, Xuemei Peng, Junlan Chen, Zhiyi Chen, and Zeyi Wen. 2026. TRACE: Single-Pass Decoding-Trace Risk Localization for Generation Calibration. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 5318–5335, Budapest, Hungary. Association for Computational Linguistics.