This study explores the realm of knowledge-base question answering (KBQA).
KBQA is considered a challenging task, particularly in parsing intricate
questions into executable logical forms. Traditional semantic parsing
(SP)-based methods require extensive data annotations, which result in
significant costs. Recently, the advent of few-shot in-context learning,
powered by large language models (LLMs), has showcased promising capabilities.
Yet, fully leveraging LLMs to parse questions into logical forms in
low-resource scenarios poses a substantial challenge. To tackle these hurdles,
we introduce Interactive-KBQA, a framework designed to generate logical forms
through direct interaction with knowledge bases (KBs). Within this framework,
we have developed three generic APIs for KB interaction. For each category of
complex question, we devised exemplars to guide LLMs through the reasoning
processes. Our method achieves competitive results on the WebQuestionsSP,
ComplexWebQuestions, KQA Pro, and MetaQA datasets with a minimal number of
examples (shots). Importantly, our approach supports manual intervention,
allowing for the iterative refinement of LLM outputs. By annotating a dataset
with step-wise reasoning processes, we showcase our model's adaptability and
highlight its potential for contributing significant enhancements to the field.

通过与知识库进行直接交互生成逻辑形式，我们引入了 Interactive-KBQA 框架来解决知识库问答领域的问题，该框架包括三种通用 API 用于知识库交互，通过步骤推理过程注释数据集，展示了我们模型的适应性和潜力，并在低资源场景中以极少的示例达到了优秀的结果。