Year: 2025
Type: conference-paper
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 1
Authors: Riya Sawhney, Samrat Yadav, Indrajit Bhattacharya, Mausam Mausam
Institution: Indian Institute of Technology Delhi
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4412944620