AQUALLM: Audio Question Answering Data Generation Using Large Language
  Models
Swarup Ranjan Behera Krishna Mohan Injeti Jaya Sai Kiran Patibandla Praveen Kumar Pokala Balakrishna Reddy Pailla
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{\url{https://github.com/swarupbehera/AQUALLM}}.