Real-world KBQA applications require models that are (1) robust -- e.g., can differentiate between answerable and unanswerable questions, and (2) low-resource -- do not require large training data. Towards this goal, we propose the novel task of few-shot transfer for KBQA with unanswerable questions. We present FUn-FuSIC that extends the state-of-the-art (SoTA) few-shot transfer model for answerable-only KBQA to handle unanswerability. It iteratively prompts an LLM to generate logical forms for the question by providing feedback using a diverse suite of syntactic, semantic and execution guided checks, and adapts self-consistency to assess confidence of the LLM to decide answerability. Experiments over newly constructed datasets show that FUn-FuSIC outperforms suitable adaptations of the SoTA model for KBQA with unanswerability, and the SoTA model for answerable-only few-shot-transfer KBQA.

翻译：现实世界中的知识库问答应用需要满足以下条件的模型：(1) 鲁棒性——例如能够区分可回答与不可回答问题；(2) 低资源需求——无需大量训练数据。为实现此目标，我们提出了面向含不可回答问题知识库问答的少样本迁移新任务。我们提出了FUn-FuSIC方法，该方法将当前最先进的仅可回答问题知识库问答少样本迁移模型扩展至可处理不可回答性。该方法通过使用一套多样化的语法、语义及执行引导检查提供反馈，迭代提示大型语言模型为问题生成逻辑形式，并采用自一致性机制评估模型对问题可答性的置信度以做出决策。在新构建数据集上的实验表明，FUn-FuSIC在性能上优于针对含不可回答性知识库问答的最优模型适配方案，以及当前仅可回答问题少样本迁移知识库问答的最优模型。