@inproceedings{seleznyov-etal-2026-evolutionary,
title = "Evolutionary Search for Automated Design of Uncertainty Quantification Methods",
author = "Seleznyov, Mikhail and
Korbut, Daniil and
Moskvoretskii, Viktor and
Somov, Oleg and
Panchenko, Alexander and
Tutubalina, Elena",
editor = "T.Y.S.S., Santosh and
Rodriguez, Juan Diego and
de Gibert, Ona",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics ({ACL} 2026)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-srw.115/",
pages = "1280--1290",
ISBN = "979-8-89176-393-7",
abstract = "Uncertainty quantification (UQ) methods for large language models are predominantly designed by hand based on domain knowledge and heuristics, limiting their scalability and generality. We apply LLM-powered evolutionary search to automatically discover unsupervised UQ methods represented as Python programs. On the task of atomic claim verification, our evolved methods outperform strong manually-designed baselines, achieving up to 6.7{\%} relative ROC-AUC improvement across 9 datasets while generalizing robustly out-of-distribution. Qualitative analysis reveals that different LLMs employ qualitatively distinct evolutionary strategies: Claude models consistently design high-feature-count linear estimators, while Gpt-oss-120B gravitates toward simpler and more interpretable positional weighting schemes. Surprisingly, only Sonnet 4.5 and Opus 4.5 reliably leverage increased method complexity to improve performance{~}{--} Opus 4.6 shows an unexpected regression relative to its predecessor. Overall, our results hint that LLM-powered evolutionary search is a promising paradigm for automated, interpretable hallucination detector design."
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<abstract>Uncertainty quantification (UQ) methods for large language models are predominantly designed by hand based on domain knowledge and heuristics, limiting their scalability and generality. We apply LLM-powered evolutionary search to automatically discover unsupervised UQ methods represented as Python programs. On the task of atomic claim verification, our evolved methods outperform strong manually-designed baselines, achieving up to 6.7% relative ROC-AUC improvement across 9 datasets while generalizing robustly out-of-distribution. Qualitative analysis reveals that different LLMs employ qualitatively distinct evolutionary strategies: Claude models consistently design high-feature-count linear estimators, while Gpt-oss-120B gravitates toward simpler and more interpretable positional weighting schemes. Surprisingly, only Sonnet 4.5 and Opus 4.5 reliably leverage increased method complexity to improve performance – Opus 4.6 shows an unexpected regression relative to its predecessor. Overall, our results hint that LLM-powered evolutionary search is a promising paradigm for automated, interpretable hallucination detector design.</abstract>
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%0 Conference Proceedings
%T Evolutionary Search for Automated Design of Uncertainty Quantification Methods
%A Seleznyov, Mikhail
%A Korbut, Daniil
%A Moskvoretskii, Viktor
%A Somov, Oleg
%A Panchenko, Alexander
%A Tutubalina, Elena
%Y T.Y.S.S., Santosh
%Y Rodriguez, Juan Diego
%Y de Gibert, Ona
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-393-7
%F seleznyov-etal-2026-evolutionary
%X Uncertainty quantification (UQ) methods for large language models are predominantly designed by hand based on domain knowledge and heuristics, limiting their scalability and generality. We apply LLM-powered evolutionary search to automatically discover unsupervised UQ methods represented as Python programs. On the task of atomic claim verification, our evolved methods outperform strong manually-designed baselines, achieving up to 6.7% relative ROC-AUC improvement across 9 datasets while generalizing robustly out-of-distribution. Qualitative analysis reveals that different LLMs employ qualitatively distinct evolutionary strategies: Claude models consistently design high-feature-count linear estimators, while Gpt-oss-120B gravitates toward simpler and more interpretable positional weighting schemes. Surprisingly, only Sonnet 4.5 and Opus 4.5 reliably leverage increased method complexity to improve performance – Opus 4.6 shows an unexpected regression relative to its predecessor. Overall, our results hint that LLM-powered evolutionary search is a promising paradigm for automated, interpretable hallucination detector design.
%U https://aclanthology.org/2026.acl-srw.115/
%P 1280-1290
Markdown (Informal)
[Evolutionary Search for Automated Design of Uncertainty Quantification Methods](https://aclanthology.org/2026.acl-srw.115/) (Seleznyov et al., ACL 2026)
ACL
- Mikhail Seleznyov, Daniil Korbut, Viktor Moskvoretskii, Oleg Somov, Alexander Panchenko, and Elena Tutubalina. 2026. Evolutionary Search for Automated Design of Uncertainty Quantification Methods. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), pages 1280–1290, San Diego, California, United States. Association for Computational Linguistics.