Audio Question Answering (AQA) constitutes a pivotal task in which machines
analyze both audio signals and natural language questions to produce precise
natural language answers. The significance of possessing high-quality, diverse,
and extensive AQA datasets cannot be overstated when aiming for the precision
of an AQA system. While there has been notable focus on developing accurate and
efficient AQA models, the creation of high-quality, diverse, and extensive
datasets for the specific task at hand has not garnered considerable attention.
To address this challenge, this work makes several contributions. We introduce
a scalable AQA data generation pipeline, denoted as the AQUALLM framework,
which relies on Large Language Models (LLMs). This framework utilizes existing
audio-caption annotations and incorporates state-of-the-art LLMs to generate
expansive, high-quality AQA datasets. Additionally, we present three extensive
and high-quality benchmark datasets for AQA, contributing significantly to the
progression of AQA research. AQA models trained on the proposed datasets set
superior benchmarks compared to the existing state-of-the-art. Moreover, models
trained on our datasets demonstrate enhanced generalizability when compared to
models trained using human-annotated AQA data. Code and datasets will be
accessible on GitHub~\footnote{https://github.com/swarupbehera/AQUALLM}.

这篇研究论文介绍了一种基于大型语言模型的可伸缩的 AQA 数据生成流程 (AQUALLM 框架)，利用现有的音频描述注释并结合最先进的语言模型生成了丰富、高质量的 AQA 数据集，同时提供了三个广泛和高质量的 AQA 基准数据集，该框架和数据集推动了 AQA 研究的进展，并且所训练的模型在性能上优于现有的最先进模型，而且相比人工注释的训练数据，本数据集上训练的模型表现出更强的泛化能力。