Few-shot Transfer Learning for Knowledge Base Question Answering: Fusing Supervised Models with In-Context Learning

• URL: http://arxiv.org/abs/2311.08894v3
• Date: Thu, 13 Jun 2024 12:06:27 GMT
• Title: Few-shot Transfer Learning for Knowledge Base Question Answering: Fusing Supervised Models with In-Context Learning
• Authors: Mayur Patidar, Riya Sawhney, Avinash Singh, Biswajit Chatterjee, Mausam, Indrajit Bhattacharya,
• Abstract summary: Existing Knowledge Base Question Answering (KBQA) architectures are hungry for annotated data. We introduce the problem of few-shot transfer learning for KBQA, where the target domain offers only a few labeled examples. We propose a novel KBQA architecture called FuSIC-KBQA that performs KB-retrieval using multiple source-trained retrievers.
• Score: 20.80841972133938
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing Knowledge Base Question Answering (KBQA) architectures are hungry for annotated data, which make them costly and time-consuming to deploy. We introduce the problem of few-shot transfer learning for KBQA, where the target domain offers only a few labeled examples, but a large labeled training dataset is available in a source domain. We propose a novel KBQA architecture called FuSIC-KBQA that performs KB-retrieval using multiple source-trained retrievers, re-ranks using an LLM and uses this as input for LLM few-shot in-context learning to generate logical forms. These are further refined using execution-guided feedback. Experiments over multiple source-target KBQA pairs of varying complexity show that FuSIC-KBQA significantly outperforms adaptations of SoTA KBQA models for this setting. Additional experiments show that FuSIC-KBQA also outperforms SoTA KBQA models in the in-domain setting when training data is limited.

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