@inproceedings{carrino-etal-2026-quantifying,
title = "Quantifying Uncertainty Through Coverage in Latent Semantic Space",
author = "Carrino, Gabriele and
Ebouky, Brown and
Hose, Katja and
Rigotti, Mattia",
editor = "Zhang, Yue and
Xu, Ruifeng and
Purwarianti, Ayu and
Rong, Tong and
Suzuki, Jun",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {IJCNLP}-{AACL} 2026",
month = nov,
year = "2026",
address = "Hengqin, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-aacl.125/",
pages = "2449--2465",
ISBN = "979-8-89176-444-6",
abstract = "Large language models often generate plausible yet unreliable answers, making uncertainty quantification essential for detecting potential hallucinations. Prior work has shown that the information needed to detect hallucinations and quantify a model{'}s confidence in its generations may already be present in its internal states, but how to extract this information effectively remains an open problem. We introduce Semantic Coverage, a white-box, representation-level spectral uncertainty estimator inspired by coverage in reinforcement learning. Given multiple sampled generations for a prompt, Semantic Coverage quantifies uncertainty by measuring how broadly their internal representations span the model{'}s latent semantic space. Across three open-weights language models and four short-form question-answering benchmarks, Semantic Coverage achieves the strongest overall performance among the evaluated methods and consistently outperforms prior representation-level methods in quantifying LLM uncertainty."
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<abstract>Large language models often generate plausible yet unreliable answers, making uncertainty quantification essential for detecting potential hallucinations. Prior work has shown that the information needed to detect hallucinations and quantify a model’s confidence in its generations may already be present in its internal states, but how to extract this information effectively remains an open problem. We introduce Semantic Coverage, a white-box, representation-level spectral uncertainty estimator inspired by coverage in reinforcement learning. Given multiple sampled generations for a prompt, Semantic Coverage quantifies uncertainty by measuring how broadly their internal representations span the model’s latent semantic space. Across three open-weights language models and four short-form question-answering benchmarks, Semantic Coverage achieves the strongest overall performance among the evaluated methods and consistently outperforms prior representation-level methods in quantifying LLM uncertainty.</abstract>
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%0 Conference Proceedings
%T Quantifying Uncertainty Through Coverage in Latent Semantic Space
%A Carrino, Gabriele
%A Ebouky, Brown
%A Hose, Katja
%A Rigotti, Mattia
%Y Zhang, Yue
%Y Xu, Ruifeng
%Y Purwarianti, Ayu
%Y Rong, Tong
%Y Suzuki, Jun
%S Findings of the Association for Computational Linguistics: IJCNLP-AACL 2026
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-444-6
%F carrino-etal-2026-quantifying
%X Large language models often generate plausible yet unreliable answers, making uncertainty quantification essential for detecting potential hallucinations. Prior work has shown that the information needed to detect hallucinations and quantify a model’s confidence in its generations may already be present in its internal states, but how to extract this information effectively remains an open problem. We introduce Semantic Coverage, a white-box, representation-level spectral uncertainty estimator inspired by coverage in reinforcement learning. Given multiple sampled generations for a prompt, Semantic Coverage quantifies uncertainty by measuring how broadly their internal representations span the model’s latent semantic space. Across three open-weights language models and four short-form question-answering benchmarks, Semantic Coverage achieves the strongest overall performance among the evaluated methods and consistently outperforms prior representation-level methods in quantifying LLM uncertainty.
%U https://aclanthology.org/2026.findings-aacl.125/
%P 2449-2465
Markdown (Informal)
[Quantifying Uncertainty Through Coverage in Latent Semantic Space](https://aclanthology.org/2026.findings-aacl.125/) (Carrino et al., Findings 2026)
ACL