@inproceedings{boonsanong-etal-2025-facts,
    title = "{FACTS}{\&}{EVIDENCE}: An Interactive Tool for Transparent Fine-Grained Factual Verification of Machine-Generated Text",
    author = "Boonsanong, Varich  and
      Balachandran, Vidhisha  and
      Han, Xiaochuang  and
      Feng, Shangbin  and
      Wang, Lucy Lu  and
      Tsvetkov, Yulia",
    editor = "Dziri, Nouha  and
      Ren, Sean (Xiang)  and
      Diao, Shizhe",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (System Demonstrations)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.naacl-demo.35/",
    pages = "437--448",
    ISBN = "979-8-89176-191-9",
    abstract = "With the widespread consumption of AI-generated content, there has been an increased focus on developing automated tools to verify the factual accuracy of such content. However, prior research and tools developed for fact verification treat it as a binary classification or a linear regression problem. Although this is a useful mechanism as part of automatic guardrails in systems, we argue that such tools lack transparency in the prediction reasoning and diversity in source evidence to provide a trustworthy user experience.We develop FACTS{\&}EVIDENCE{---}an interactive and transparent tool for user-driven verification of complex text. The tool facilitates the intricate decision-making involved in fact-verification, presenting its users a breakdown of complex input texts to visualize the credibility of individual claims along with explanation of model decisions and attribution to multiple, diverse evidence sources. FACTS{\&}EVIDENCE aims to empower consumers of machine-generated text and give them agency to understand, verify, selectively trust and use such text."
}