@inproceedings{li-etal-2025-ask,
    title = "Ask-Before-Detection: Identifying and Mitigating Conformity Bias in {LLM}-Powered Error Detector for Math Word Problem Solutions",
    author = "Li, Hang  and
      Xu, Tianlong  and
      Yang, Kaiqi  and
      Chu, Yucheng  and
      Chen, Yanling  and
      Song, Yichi  and
      Wen, Qingsong  and
      Liu, Hui",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.80/",
    pages = "1597--1609",
    ISBN = "979-8-89176-251-0",
    abstract = "The rise of large language models (LLMs) offers new opportunities for automatic error detection in education, particularly for math word problems (MWPs). While prior studies demonstrate the promise of LLMs as error detectors, they overlook the presence of multiple valid solutions for a single MWP. Our preliminary analysis reveals a significant performance gap between conventional and alternative solutions in MWPs, a phenomenon we term conformity bias in this work. To mitigate this bias, we introduce the Ask-Before-Detect (AskBD) framework, which generates adaptive reference solutions using LLMs to enhance error detection. Experiments on 200 examples of GSM8K show that AskBD effectively mitigates bias and improves performance, especially when combined with reasoning-enhancing techniques like chain-of-thought prompting."
}