Enhancing Temporal Understanding in Audio Question Answering for Large Audio Language Models
The Audio Question Answering task includes audio event classification, audio captioning, and open ended reasoning. Recently, Audio Question Answering has garnered attention due to the advent of Large Audio Language Models. Current literature focuses on constructing LALMs by integrating audio encoder...
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Main Authors | , , |
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Format | Journal Article |
Language | English |
Published |
10.09.2024
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Subjects | |
Online Access | Get full text |
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Summary: | The Audio Question Answering task includes audio event classification, audio
captioning, and open ended reasoning. Recently, Audio Question Answering has
garnered attention due to the advent of Large Audio Language Models. Current
literature focuses on constructing LALMs by integrating audio encoders with
text only Large Language Models through a projection module. While Large Audio
Language Models excel in general audio understanding, they are limited in
temporal reasoning which may hinder their commercial applications and on device
deployment. This paper addresses these challenges and limitations in audio
temporal reasoning. First, we introduce a data augmentation technique for
generating reliable audio temporal questions and answers using an LLM. Second,
we propose a continued finetuning curriculum learning strategy to specialize in
temporal reasoning without compromising performance on finetuned tasks.
Finally, we develop a reliable and transparent automated metric, assisted by an
LLM, to measure the correlation between Large Audio Language Model responses
and ground truth data intelligently. We demonstrate the effectiveness of our
proposed techniques using SOTA LALMs on public audio benchmark datasets. |
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DOI: | 10.48550/arxiv.2409.06223 |