Feb. 28, 2024, 5:49 a.m. | Mayur Patidar, Riya Sawhney, Avinash Singh, Biswajit Chatterjee, Mausam, Indrajit Bhattacharya

cs.CL updates on arXiv.org arxiv.org

arXiv:2311.08894v2 Announce Type: replace
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 …

abstract annotated data architectures arxiv context cs.ai cs.cl data deploy domain examples few-shot in-context learning knowledge knowledge base question question answering them transfer transfer learning type

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