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Robust Few-shot Transfer Learning for Knowledge Base Question Answering with Unanswerable Questions

2024-06-20 13:43:38
Riya Sawhney, Indrajit Bhattacharya, Mausam


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.

Abstract (translated)

真实世界的KBQA应用需要具有以下两个特点的模型:(1)健壮——例如,可以区分可回答问题和不可回答问题,(2)低资源——不需要大量训练数据。为了实现这一目标,我们提出了一个新颖的任务:用于处理不可回答问题的KBQA的少样本迁移。我们提出了FUn-FuSIC,将当前最先进的(SoTA)少样本迁移模型扩展到可回答问题-only KBQA,以处理不可回答问题。它通过使用一系列语法、语义和执行指导的检查来提供反馈,逐步提示LLM生成问题逻辑形式,并自适应评估LLM对答案的置信度来决定答案。在构建的新数据集上进行的实验证明,FUn-FuSIC超越了SoTA模型在可回答问题-only KBQA上的合适调整,同时也超越了SoTA模型在可回答问题KBQA上的表现。



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