FinAudio: A Benchmark for Audio Large Language Models in Financial Applications

Yupeng Cao, Haohang Li, Yangyang Yu, Shashidhar Reddy Javaji, Yueru He, Jimin Huang, Zining Zhu, Xie, Xiao-Yang Liu Yanglet, Koduvayur Subbalakshmi, Meikang Qiu, Sophia Ananiadou, Jian-Yun Nie

FinAudio: A Benchmark for Audio Large Language Models in Financial Applications: 19 upvotes on Hugging Face Daily Papers, #11 of 22 papers on 2025-03-28. Day-by-day upvote history.

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 page on Hugging Face · arXiv

Data: hysts-bot-data/daily-papers-stats and the Daily Papers API. Open data: tardellirs/paper-pulse-data. Sister project: Model Pulse, the download history of every model on the Hub.