FinAudio: A Benchmark for Audio Large Language Models in Financial
  Applications
Audio Large Language Models (AudioLLMs) have received widespread attention
and have significantly improved performance on audio tasks such as
conversation, audio understanding, and automatic speech recognition (ASR).
Despite these advancements, there is an absence of a benchmark for assessing
AudioLLMs in financial scenarios, where audio data, such as earnings conference
calls and CEO speeches, are crucial resources for financial analysis and
investment decisions. In this paper, we introduce FinAudio, the first
benchmark designed to evaluate the capacity of AudioLLMs in the financial
domain. We first define three tasks based on the unique characteristics of the
financial domain: 1) ASR for short financial audio, 2) ASR for long financial
audio, and 3) summarization of long financial audio. Then, we curate two short
and two long audio datasets, respectively, and develop a novel dataset for
financial audio summarization, comprising the FinAudio benchmark.
Then, we evaluate seven prevalent AudioLLMs on FinAudio. Our
evaluation reveals the limitations of existing AudioLLMs in the financial
domain and offers insights for improving AudioLLMs. All datasets and codes will
be released.
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Yupeng Cao
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2025-03-28T09:11:18.739Z
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Yupeng Cao
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Haohang Li
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2025-03-28T09:11:24.664Z
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Haohang Li
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Yangyang Yu
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2025-03-28T09:11:30.611Z
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Yangyang Yu
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Shashidhar Reddy Javaji
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2025-03-28T09:11:36.871Z
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Shashidhar Reddy Javaji
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Yueru He
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2025-03-28T09:12:00.574Z
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Yueru He
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Jimin Huang
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2025-03-28T09:12:09.888Z
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Jimin Huang
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Zining Zhu
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2025-03-28T09:12:18.401Z
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Zining Zhu
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Qianqian Xie
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2025-03-28T09:12:33.144Z
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Xie
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Xiao-yang Liu
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2025-03-28T09:12:40.590Z
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Xiao-Yang Liu Yanglet
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yanglet
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Koduvayur Subbalakshmi
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Meikang Qiu
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Sophia Ananiadou
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2025-03-28T09:12:56.712Z
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Sophia Ananiadou
user
Effoula
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Jian-Yun Nie
FinAudio: A Benchmark for Audio Large Language Models in Financial
  Applications
Audio Large Language Models (AudioLLMs) have received widespread attention
and have significantly improved performance on audio tasks such as
conversation, audio understanding, and automatic speech recognition (ASR).
Despite these advancements, there is an absence of a benchmark for assessing
AudioLLMs in financial scenarios, where audio data, such as earnings conference
calls and CEO speeches, are crucial resources for financial analysis and
investment decisions. In this paper, we introduce FinAudio, the first
benchmark designed to evaluate the capacity of AudioLLMs in the financial
domain. We first define three tasks based on the unique characteristics of the
financial domain: 1) ASR for short financial audio, 2) ASR for long financial
audio, and 3) summarization of long financial audio. Then, we curate two short
and two long audio datasets, respectively, and develop a novel dataset for
financial audio summarization, comprising the FinAudio benchmark.
Then, we evaluate seven prevalent AudioLLMs on FinAudio. Our
evaluation reveals the limitations of existing AudioLLMs in the financial
domain and offers insights for improving AudioLLMs. All datasets and codes will
be released.
FinAudio is introduced as the first benchmark to evaluate AudioLLMs for financial audio tasks, including short and long ASR, and summarization, highlighting their limitations.
AudioLLMs ASR audio summarization benchmark earnings conference calls CEO speeches financial analysis investment decisions