@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://preview.aclanthology.org/ingest-acl/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."
}