@inproceedings{ishii-etal-2025-fine,
    title = "Fine-grained Confidence Estimation for Spurious Correctness Detection in Large Language Models",
    author = "Ishii, Ai  and
      Inoue, Naoya  and
      Suzuki, Hisami  and
      Sekine, Satoshi",
    editor = "Inui, Kentaro  and
      Sakti, Sakriani  and
      Wang, Haofen  and
      Wong, Derek F.  and
      Bhattacharyya, Pushpak  and
      Banerjee, Biplab  and
      Ekbal, Asif  and
      Chakraborty, Tanmoy  and
      Singh, Dhirendra Pratap",
    booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.ijcnlp-long.68/",
    pages = "1238--1257",
    ISBN = "979-8-89176-298-5",
    abstract = "In the deployment of Large Language Models (LLMs), ``spurious correctness''{---}where answers are correct but reasoning contains errors{---}poses a critical risk by creating an illusion of reliability. While prior work on LLM confidence estimation focuses on answer-level or entire reasoning path confidence, these coarse-grained approaches fail to identify which specific parts of the reasoning contain errors. We propose a fine-grained confidence estimation framework that computes confidence scores for individual evidence triplets within reasoning chains, enabling precise localization of errors. Using carefully designed prompts, we generate answers, evidence in triplet format, and their respective confidence scores simultaneously, allowing automatic detection of spurious correctness patterns where partial evidence contains factual errors. Evaluated on both Japanese and English multi-hop QA benchmarks across multiple models from three model families representing different architectures and training approaches, we show that our approach exhibits superior calibration performance for evidence confidence and demonstrates effective ability to detect spurious correct answers (up to 0.84 on our primary discrimination metric). The consistent improvements across languages demonstrate the generalizability of our method. As a secondary benefit, joint generation of confidence scores improves answer confidence calibration by up to 43{\%}. This prompt-based approach requires no model retraining and is immediately applicable to existing LLMs."
}