@inproceedings{luo-etal-2026-agsc,
title = "{AGSC}: Adaptive Granularity and Semantic Clustering for Uncertainty Quantification in Long-text Generation",
author = "Luo, Guanran and
Qiu, Wentao and
Zhao, Wanru and
Lv, Wenhan and
Jian, Zhongquan and
Wang, Meihong and
Wu, Qingqiang",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.434/",
pages = "9591--9605",
ISBN = "979-8-89176-390-6",
abstract = "Large Language Models (LLMs) have demonstrated impressive capabilities in long-form generation, yet their application is hindered by the hallucination problem. While Uncertainty Quantification (UQ) is essential for assessing reliability, the complex structure makes reliable aggregation across heterogeneous themes difficult, in addition, existing methods often overlook the nuance of neutral information and suffer from the high computational cost of fine-grained decomposition. To address these challenges, we propose **AGSC** (**A**daptive **G**ranularity and GMM-based **S**emantic **C**lustering), a UQ framework tailored for long-form generation. AGSC first uses NLI neutral probabilities as triggers to distinguish irrelevance from uncertainty, reducing unnecessary computation. It then applies Gaussian Mixture Model (GMM) soft clustering to model latent semantic themes and assign topic-aware weights for downstream aggregation. Experiments on BIO and LongFact show that AGSC achieves state-of-the-art correlation with factuality while reducing inference time by about 60{\%} compared to full atomic decomposition."
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<abstract>Large Language Models (LLMs) have demonstrated impressive capabilities in long-form generation, yet their application is hindered by the hallucination problem. While Uncertainty Quantification (UQ) is essential for assessing reliability, the complex structure makes reliable aggregation across heterogeneous themes difficult, in addition, existing methods often overlook the nuance of neutral information and suffer from the high computational cost of fine-grained decomposition. To address these challenges, we propose **AGSC** (**A**daptive **G**ranularity and GMM-based **S**emantic **C**lustering), a UQ framework tailored for long-form generation. AGSC first uses NLI neutral probabilities as triggers to distinguish irrelevance from uncertainty, reducing unnecessary computation. It then applies Gaussian Mixture Model (GMM) soft clustering to model latent semantic themes and assign topic-aware weights for downstream aggregation. Experiments on BIO and LongFact show that AGSC achieves state-of-the-art correlation with factuality while reducing inference time by about 60% compared to full atomic decomposition.</abstract>
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%0 Conference Proceedings
%T AGSC: Adaptive Granularity and Semantic Clustering for Uncertainty Quantification in Long-text Generation
%A Luo, Guanran
%A Qiu, Wentao
%A Zhao, Wanru
%A Lv, Wenhan
%A Jian, Zhongquan
%A Wang, Meihong
%A Wu, Qingqiang
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F luo-etal-2026-agsc
%X Large Language Models (LLMs) have demonstrated impressive capabilities in long-form generation, yet their application is hindered by the hallucination problem. While Uncertainty Quantification (UQ) is essential for assessing reliability, the complex structure makes reliable aggregation across heterogeneous themes difficult, in addition, existing methods often overlook the nuance of neutral information and suffer from the high computational cost of fine-grained decomposition. To address these challenges, we propose **AGSC** (**A**daptive **G**ranularity and GMM-based **S**emantic **C**lustering), a UQ framework tailored for long-form generation. AGSC first uses NLI neutral probabilities as triggers to distinguish irrelevance from uncertainty, reducing unnecessary computation. It then applies Gaussian Mixture Model (GMM) soft clustering to model latent semantic themes and assign topic-aware weights for downstream aggregation. Experiments on BIO and LongFact show that AGSC achieves state-of-the-art correlation with factuality while reducing inference time by about 60% compared to full atomic decomposition.
%U https://aclanthology.org/2026.acl-long.434/
%P 9591-9605
Markdown (Informal)
[AGSC: Adaptive Granularity and Semantic Clustering for Uncertainty Quantification in Long-text Generation](https://aclanthology.org/2026.acl-long.434/) (Luo et al., ACL 2026)
ACL
- Guanran Luo, Wentao Qiu, Wanru Zhao, Wenhan Lv, Zhongquan Jian, Meihong Wang, and Qingqiang Wu. 2026. AGSC: Adaptive Granularity and Semantic Clustering for Uncertainty Quantification in Long-text Generation. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 9591–9605, San Diego, California, United States. Association for Computational Linguistics.