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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| Online-Zugang: | https://arxiv.org/abs/2311.08894 |
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| _version_ | 1866917691943550976 |
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| author | Patidar, Mayur Sawhney, Riya Singh, Avinash Chatterjee, Biswajit Mausam Bhattacharya, Indrajit |
| author_facet | Patidar, Mayur Sawhney, Riya Singh, Avinash Chatterjee, Biswajit Mausam Bhattacharya, Indrajit |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_08894 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Few-shot Transfer Learning for Knowledge Base Question Answering: Fusing Supervised Models with In-Context Learning Patidar, Mayur Sawhney, Riya Singh, Avinash Chatterjee, Biswajit Mausam Bhattacharya, Indrajit Computation and Language Artificial Intelligence 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. |
| title | Few-shot Transfer Learning for Knowledge Base Question Answering: Fusing Supervised Models with In-Context Learning |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2311.08894 |