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.

翻译：现有知识库问答（KBQA）架构对标注数据需求巨大，导致其部署成本高昂且耗时。本文提出KBQA的小样本迁移学习问题，即目标领域仅提供少量标注样本，而源领域拥有大规模标注训练数据集。我们提出一种名为FuSIC-KBQA的新型KBQA架构，该架构利用多个基于源数据训练的检索器进行知识库检索，通过大语言模型（LLM）重排序，并将此结果作为LLM小样本上下文学习的输入以生成逻辑表达式，进而通过执行引导反馈进行精炼。在四个复杂度不同的源-目标KBQA配对实验表明，FuSIC-KBQA显著优于当前最优（SoTA）KBQA模型在此场景下的适配方法。额外领域内实验显示，当训练数据有限时，FuSIC-KBQA同样优于SoTA KBQA模型。