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arXiv · 2603.09881

Do What I Say: A Spoken Prompt Dataset for Instruction-Following

Maike Züfle·Sara Papi·Fabian Retkowski·Szymon Mazurek·Marek Kasztelnik·Alexander Waibel·Luisa Bentivogli·Jan Niehues
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Abstract

Speech Large Language Models (SLLMs) have rapidly expanded, supporting a wide range of tasks. These models are typically evaluated using text prompts, which may not reflect real-world scenarios where users interact with speech. To address this gap, we introduce DoWhatISay (DOWIS), a multilingual dataset of human-recorded spoken and written prompts designed to pair with any existing benchmark for realistic evaluation of SLLMs under spoken instruction conditions. Spanning 9 tasks and 11 languages, it provides 10 prompt variants per task-language pair, across five styles. Using DOWIS, we benchmark state-of-the-art SLLMs, analyzing the interplay between prompt modality, style, language, and task type. Results show that text prompts consistently outperform spoken prompts, particularly for low-resource and cross-lingual settings. Only for tasks with speech output, spoken prompts do close the gap, highlighting the need for speech-based prompting in SLLM evaluation.

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Maike Züfle, Sara Papi, Fabian Retkowski, Szymon Mazurek, Marek Kasztelnik, Alexander Waibel, Luisa Bentivogli, Jan Niehues. 2026-03-10. Do What I Say: A Spoken Prompt Dataset for Instruction-Following. https://arxiv.org/abs/2603.09881

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