@inproceedings{liu-etal-2025-open,
    title = "Open Domain Question Answering with Conflicting Contexts",
    author = "Liu, Siyi  and
      Ning, Qiang  and
      Halder, Kishaloy  and
      Qi, Zheng  and
      Xiao, Wei  and
      Htut, Phu Mon  and
      Zhang, Yi  and
      Anna John, Neha  and
      Min, Bonan  and
      Benajiba, Yassine  and
      Roth, Dan",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/corrections-2025-06/2025.findings-naacl.99/",
    doi = "10.18653/v1/2025.findings-naacl.99",
    pages = "1838--1854",
    ISBN = "979-8-89176-195-7",
    abstract = "Open domain question answering systems frequently rely on information retrieved from large collections of text (such as the Web) to answer questions. However, such collections of text often contain conflicting information, and indiscriminately depending on this information may result in untruthful and inaccurate answers. To understand the gravity of this problem, we collect a human-annotated dataset, Question Answering with Conflicting Contexts (QACC), and find that as much as 25{\%} of unambiguous, open domain questions can lead to conflicting contexts when retrieved using Google Search. We evaluate and benchmark three powerful Large Language Models (LLMs) with our dataset QACC and demonstrate their limitations in effectively addressing questions with conflicting information. To explore how humans reason through conflicting contexts, we request our annotators to provide explanations for their selections of correct answers. We demonstrate that by finetuning LLMs to explain their answers, we can introduce richer information into their training that guide them through the process of reasoning with conflicting contexts. We publicly release our dataset and code to promote research along this line."
}