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

• URL: http://arxiv.org/abs/2603.09881v1
• Date: Tue, 10 Mar 2026 16:39:46 GMT
• Title: Do What I Say: A Spoken Prompt Dataset for Instruction-Following
• Abstract summary: DoWhatISay (DOWIS) is a dataset of human-recorded spoken and written prompts.<n>It provides 10 prompt variants per task-language pair, across five styles.<n>Using DOWIS, we benchmark state-of-the-art SLLMs, analyzing the interplay between prompt modality, style, language, and task type.
• Score: 64.72383858691795
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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