title: FinAudio: A Benchmark for Audio Large Language Models in Financial
    Applications
summary: 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.
paper_authors._id: 67e626a9cb305c5a3ee11fac
paper_authors.name: Yupeng Cao
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paper_authors.fullname: Yupeng Cao
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paper_authors.name: Haohang Li
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paper_authors.fullname: Haohang Li
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paper_authors.user: Acatsama
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paper_authors.name: Yangyang Yu
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paper_authors.fullname: Yangyang Yu
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paper_authors.user: ShirleyY
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paper_authors.name: Shashidhar Reddy Javaji
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paper_authors.fullname: Shashidhar Reddy Javaji
paper_authors.type: user
paper_authors.user: Shashidhar
paper_authors._id: 67e626a9cb305c5a3ee11fb0
paper_authors.name: Yueru He
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paper_authors.fullname: Yueru He
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paper_authors.user: Yueru1
paper_authors._id: 67e626a9cb305c5a3ee11fb1
paper_authors.name: Jimin Huang
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paper_authors.fullname: Jimin Huang
paper_authors.type: user
paper_authors.user: jiminHuang
paper_authors._id: 67e626a9cb305c5a3ee11fb2
paper_authors.name: Zining Zhu
paper_authors.status: admin_assigned
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paper_authors._id: 62d63a9dc5ada8ef841b4787
paper_authors.avatarUrl: /avatars/a79a4ac07984d9a8623c99bdce9add54.svg
paper_authors.fullname: Zining Zhu
paper_authors.type: user
paper_authors.user: ZiningZhu
paper_authors._id: 67e626a9cb305c5a3ee11fb3
paper_authors.name: Qianqian Xie
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paper_authors.fullname: Xie
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paper_authors.user: QianqianXie1994
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paper_authors.name: Xiao-yang Liu
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paper_authors.avatarUrl: /avatars/01ebcb41b89b1da557abdb9cf867331f.svg
paper_authors.fullname: Xiao-Yang Liu Yanglet
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paper_authors.user: yanglet
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paper_authors.name: Koduvayur Subbalakshmi
paper_authors._id: 67e626a9cb305c5a3ee11fb6
paper_authors.name: Meikang Qiu
paper_authors._id: 67e626a9cb305c5a3ee11fb7
paper_authors.name: Sophia Ananiadou
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-28T09:12:56.712Z
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paper_authors.avatarUrl: /avatars/0a70c94072bc5e1d018cf12da0904ff0.svg
paper_authors.fullname: Sophia Ananiadou
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paper_authors.name: Jian-Yun Nie
paper_title: FinAudio: A Benchmark for Audio Large Language Models in Financial
    Applications
paper_summary: 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.
paper_ai_summary: FinAudio is introduced as the first benchmark to evaluate AudioLLMs for financial audio tasks, including short and long ASR, and summarization, highlighting their limitations.
paper_ai_keywords: AudioLLMs ASR audio summarization benchmark earnings conference calls CEO speeches financial analysis investment decisions