@inproceedings{kunitomo-jacquin-etal-2025-role,
    title = "On the Role of Unobserved Sequences on Sample-based Uncertainty Quantification for {LLM}s",
    author = "Kunitomo-Jacquin, Lucie  and
      Marrese-Taylor, Edison  and
      Fukuda, Ken",
    editor = "Noidea, Noidea",
    booktitle = "Proceedings of the 2nd Workshop on Uncertainty-Aware NLP (UncertaiNLP 2025)",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.uncertainlp-main.15/",
    pages = "179--183",
    ISBN = "979-8-89176-349-4",
    abstract = "Quantifying uncertainty in large language models (LLMs) is important for safety-critical applications because it helps spot incorrect answers, known as hallucinations. One major trend of uncertainty quantification methods is based on estimating the entropy of the distribution of the LLM{'}s potential output sequences. This estimation is based on a set of output sequences and associated probabilities obtained by querying the LLM several times. In this paper, we advocate and experimentally and show that the probability of unobserved sequences plays a crucial role, and we recommend future research to integrate it to enhance such LLM uncertainty quantification methods."
}