@inproceedings{cao-etal-2026-clustered,
    title = "Clustered Self-Assessment: A Simple yet Effective Method for Uncertainty Quantification in Large Language Models",
    author = "Cao, Qi  and
      Kojima, Takeshi  and
      Gambardella, Andrew  and
      Peng, Helinyi  and
      Matsuo, Yutaka  and
      Iwasawa, Yusuke",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings 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://preview.aclanthology.org/ingest-acl/2026.findings-acl.1531/",
    pages = "30666--30680",
    ISBN = "979-8-89176-395-1",
    abstract = "Large language models (LLMs) demonstrate remarkable performance across diverse tasks, but they often generate responses that appear plausible while being factually incorrect. This problem is compounded by the lack of explicit uncertainty estimates, which makes it difficult for users to judge the reliability of model outputs. Existing uncertainty quantification methods typically rely on indirect signals, such as entropy across sampled generations. These signals can be difficult to interpret and do not fully leverage the model{'}s ability to assess its own uncertainty. We propose a simple and effective self-assessment method for uncertainty quantification in LLMs. Our approach groups sampled generations into semantically distinct clusters, converts them into answer options in a structured multiple-choice question, and uses the probability assigned by the LLM to each option as a confidence estimate. Experiments across multiple models and datasets show that our method consistently outperforms baseline approaches. Notably, it achieves competitive performance with as few as two additional samples, demonstrating both its effectiveness and efficiency."
}