IFEval-Audio: Benchmarking Instruction-Following Capability in Audio-based Large Language Models

• URL: http://arxiv.org/abs/2505.16774v1
• Date: Thu, 22 May 2025 15:15:29 GMT
• Title: IFEval-Audio: Benchmarking Instruction-Following Capability in Audio-based Large Language Models
• Authors: Yiming Gao, Bin Wang, Chengwei Wei, Shuo Sun, AiTi Aw,
• Abstract summary: IFEval-Audio contains 280 audio-instruction-answer triples across six diverse dimensions.<n>Each example pairs an audio input with a text instruction, requiring the model to generate an output that follows a specified structure.<n>We benchmark state-of-the-art audio LLMs on their ability to follow audio-involved instructions.
• Score: 18.11667976818302
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) have demonstrated strong instruction-following capabilities in text-based tasks. However, this ability often deteriorates in multimodal models after alignment with non-text modalities such as images or audio. While several recent efforts have investigated instruction-following performance in text and vision-language models, instruction-following in audio-based large language models remains largely unexplored. To bridge this gap, we introduce IFEval-Audio, a novel evaluation dataset designed to assess the ability to follow instructions in an audio LLM. IFEval-Audio contains 280 audio-instruction-answer triples across six diverse dimensions: Content, Capitalization, Symbol, List Structure, Length, and Format. Each example pairs an audio input with a text instruction, requiring the model to generate an output that follows a specified structure. We benchmark state-of-the-art audio LLMs on their ability to follow audio-involved instructions. The dataset is released publicly to support future research in this emerging area.

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