KBQA Datasets

Knowledge Base Question Answering (KBQA) datasets are crucial for developing systems that can accurately answer complex questions using information from structured knowledge bases. Current research focuses on addressing challenges like handling incomplete knowledge, incorporating numerical reasoning and multi-hop reasoning, and improving the generalizability of models across different knowledge bases. This involves developing novel model architectures, such as retriever-reasoner pipelines and program induction methods, often leveraging large language models for improved performance and few-shot learning capabilities. The development and rigorous evaluation of these datasets are vital for advancing the field and enabling the creation of more robust and reliable question-answering systems with broad applications.