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    <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, which are further refined using execution-guided feedback. Experiments over four source-target KBQA pairs of varying complexity show that FuSIC-KBQA significantly outperforms adaptations of SoTA KBQA models for this setting. Additional experiments in the in-domain setting show that FuSIC-KBQA also outperforms SoTA KBQA models when training data is limited.</abstract>
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%0 Conference Proceedings
%T Few-shot Transfer Learning for Knowledge Base Question Answering: Fusing Supervised Models with In-Context Learning
%A Patidar, Mayur
%A Sawhney, Riya
%A Singh, Avinash
%A Chatterjee, Biswajit
%A ., Mausam
%A Bhattacharya, Indrajit
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F patidar-etal-2024-shot
%X 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, which are further refined using execution-guided feedback. Experiments over four source-target KBQA pairs of varying complexity show that FuSIC-KBQA significantly outperforms adaptations of SoTA KBQA models for this setting. Additional experiments in the in-domain setting show that FuSIC-KBQA also outperforms SoTA KBQA models when training data is limited.
%U https://aclanthology.org/2024.acl-long.495
%P 9147-9165

Markdown (Informal)

[Few-shot Transfer Learning for Knowledge Base Question Answering: Fusing Supervised Models with In-Context Learning](https://aclanthology.org/2024.acl-long.495) (Patidar et al., ACL 2024)

• Few-shot Transfer Learning for Knowledge Base Question Answering: Fusing Supervised Models with In-Context Learning (Patidar et al., ACL 2024)

ACL

• Mayur Patidar, Riya Sawhney, Avinash Singh, Biswajit Chatterjee, Mausam ., and Indrajit Bhattacharya. 2024. Few-shot Transfer Learning for Knowledge Base Question Answering: Fusing Supervised Models with In-Context Learning. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 9147–9165, Bangkok, Thailand. Association for Computational Linguistics.