Computer Science > Computation and Language
arXiv:2603.09881 (cs)
[Submitted on 10 Mar 2026]
Title:Do What I Say: A Spoken Prompt Dataset for Instruction-Following
Authors: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.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2603.09881 [cs.CL] |
| (or arXiv:2603.09881v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2603.09881
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View a PDF of the paper titled Do What I Say: A Spoken Prompt Dataset for Instruction-Following, by Maike Z\"ufle and Sara Papi and Fabian Retkowski and Szymon Mazurek and Marek Kasztelnik and Alexander Waibel and Luisa Bentivogli and Jan Niehues
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