An Iterative Repair Approach for Few-shot Transfer in KBQA with Unanswerability

ACL ARR 2025 February Submission1205 Authors

Abstract: Real-world applications of KBQA require models to handle 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 and contribute two new datasets for performance evaluation. We present FUn-FuSIC -- a novel solution for our task that extends FuSIC-KBQA, the state-of-the-art few-shot transfer model for answerable-only KBQA. We first note that FuSIC-KBQA's iterative repair makes a strong assumption that all questions are answerable. As a remedy, we propose Feedback for Unanswerability (FUn), which uses iterative repair using feedback from a suite of strong and weak verifiers, and an adaptation of self-consistency for unanswerabilty to better assess the answerability of a question. 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 for answerable few-shot transfer as well.

Paper Type: Long

Research Area: Question Answering

Research Area Keywords: Question Answering, KBQA, Few shot in context learning

Contribution Types: NLP engineering experiment, Approaches to low-resource settings, Data resources

Languages Studied: English

Submission Number: 1205