@inproceedings{vazhentsev-etal-2025-unconditional,
title = "Unconditional Truthfulness: Learning Unconditional Uncertainty of Large Language Models",
author = "Vazhentsev, Artem and
Fadeeva, Ekaterina and
Xing, Rui and
Kuzmin, Gleb and
Lazichny, Ivan and
Panchenko, Alexander and
Nakov, Preslav and
Baldwin, Timothy and
Panov, Maxim and
Shelmanov, Artem",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.1807/",
pages = "35661--35682",
ISBN = "979-8-89176-332-6",
abstract = "Uncertainty quantification (UQ) has emerged as a promising approach for detecting hallucinations and low-quality output of Large Language Models (LLMs). However, obtaining proper uncertainty scores is complicated by the conditional dependency between the generation steps of an autoregressive LLM, because it is hard to model it explicitly. Here, we propose to learn this dependency from attention-based features. In particular, we train a regression model that leverages LLM attention maps, probabilities on the current generation step, and recurrently computed uncertainty scores from previously generated tokens. To incorporate the recurrent features, we also suggest a two-staged training procedure. Our experimental evaluation on ten datasets and three LLMs shows that the proposed method is highly effective for selective generation, achieving substantial improvements over rivaling unsupervised and supervised approaches."
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<abstract>Uncertainty quantification (UQ) has emerged as a promising approach for detecting hallucinations and low-quality output of Large Language Models (LLMs). However, obtaining proper uncertainty scores is complicated by the conditional dependency between the generation steps of an autoregressive LLM, because it is hard to model it explicitly. Here, we propose to learn this dependency from attention-based features. In particular, we train a regression model that leverages LLM attention maps, probabilities on the current generation step, and recurrently computed uncertainty scores from previously generated tokens. To incorporate the recurrent features, we also suggest a two-staged training procedure. Our experimental evaluation on ten datasets and three LLMs shows that the proposed method is highly effective for selective generation, achieving substantial improvements over rivaling unsupervised and supervised approaches.</abstract>
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%0 Conference Proceedings
%T Unconditional Truthfulness: Learning Unconditional Uncertainty of Large Language Models
%A Vazhentsev, Artem
%A Fadeeva, Ekaterina
%A Xing, Rui
%A Kuzmin, Gleb
%A Lazichny, Ivan
%A Panchenko, Alexander
%A Nakov, Preslav
%A Baldwin, Timothy
%A Panov, Maxim
%A Shelmanov, Artem
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F vazhentsev-etal-2025-unconditional
%X Uncertainty quantification (UQ) has emerged as a promising approach for detecting hallucinations and low-quality output of Large Language Models (LLMs). However, obtaining proper uncertainty scores is complicated by the conditional dependency between the generation steps of an autoregressive LLM, because it is hard to model it explicitly. Here, we propose to learn this dependency from attention-based features. In particular, we train a regression model that leverages LLM attention maps, probabilities on the current generation step, and recurrently computed uncertainty scores from previously generated tokens. To incorporate the recurrent features, we also suggest a two-staged training procedure. Our experimental evaluation on ten datasets and three LLMs shows that the proposed method is highly effective for selective generation, achieving substantial improvements over rivaling unsupervised and supervised approaches.
%U https://aclanthology.org/2025.emnlp-main.1807/
%P 35661-35682
Markdown (Informal)
[Unconditional Truthfulness: Learning Unconditional Uncertainty of Large Language Models](https://aclanthology.org/2025.emnlp-main.1807/) (Vazhentsev et al., EMNLP 2025)
ACL
- Artem Vazhentsev, Ekaterina Fadeeva, Rui Xing, Gleb Kuzmin, Ivan Lazichny, Alexander Panchenko, Preslav Nakov, Timothy Baldwin, Maxim Panov, and Artem Shelmanov. 2025. Unconditional Truthfulness: Learning Unconditional Uncertainty of Large Language Models. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 35661–35682, Suzhou, China. Association for Computational Linguistics.