@inproceedings{sawhney-etal-2025-iterative,
    title = "Iterative Repair with Weak Verifiers for Few-shot Transfer in {KBQA} with Unanswerability",
    author = "Sawhney, Riya  and
      Yadav, Samrat  and
      Bhattacharya, Indrajit  and
      Mausam",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/nschneid-patch-1/2025.findings-acl.1262/",
    doi = "10.18653/v1/2025.findings-acl.1262",
    pages = "24578--24596",
    ISBN = "979-8-89176-256-5",
    abstract = "Real-world applications of KBQA require models to detect different types of unanswerable questions with a limited volume of in-domain labeled training data. We propose the novel task of few-shot transfer for KBQA with unanswerable questions. The state-of-the-art KBQA few-shot transfer model (FuSIC-KBQA) uses an iterative repair strategy that assumes that all questions are answerable. As a remedy, we present FUn-FuSIC {--} a novel solution for our task that extends FuSIC-KBQA with Feedback for Unanswerability (FUn), which is an iterative repair strategy for answerable as well as unanswerable questions. FUn uses feedback from a suite of strong and weak verifiers, and an adaptation of self-consistency for unanswerability for assessing answerability of questions. Our experiments show that FUn-FuSIC significantly outperforms suitable adaptations of multiple LLM-based and supervised SoTA models on our task, while establishing a new SoTA performance for answerable few-shot transfer as well. We have made datasets and other resources publicly available at https://github.com/dair-iitd/funfusic/."
}