@inproceedings{chen-etal-2026-uncertainty-quantification,
    title = "Uncertainty Quantification of Large Language Models through Multiple Uncertainty Sources",
    author = "Chen, Tiejin  and
      Liu, Xiaoou  and
      Da, Longchao  and
      Chen, Jia  and
      Papalexakis, Evangelos E.  and
      Wei, Hua",
    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.535/",
    pages = "11015--11029",
    ISBN = "979-8-89176-395-1",
    abstract = "Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse domains. However, the reliability of responses from LLMs remains a question. Uncertainty quantification (UQ) of LLMs is crucial for ensuring their reliability, especially in areas such as healthcare. Existing UQ methods, often designed around a single resource such as Natural Language Inference (NLI) or graph-based metrics, fail to capture the multifaceted nature of uncertainty in natural language generation. In this work, we propose MS-UQ, a novel Multi-Resource Uncertainty Quantification framework that integrates heterogeneous uncertainty signals into a unified measure. Our approach concatenates matrices from diverse resources and employs tensor decomposition to orthogonally disentangle unique and shared information. To ensure scalability, we construct an adaptive ensemble of outputs from different decomposition methods, enabling the incorporation of new uncertainty sources. Experiments on CoQA, NQ{\_}Open, and HotpotQA demonstrate that MS-UQ consistently outperforms existing methods, offering a comprehensive and scalable solution for uncertainty estimation in black-box LLMs and a more robust framework for enhancing LLM reliability in high-stakes applications. Our code can be accessed at https://anonymous.4open.science/r/MDUQ-First-202E/README.md."
}