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arXiv · 2505.14654

Beyond Words: Multimodal LLM Knows When to Speak

Zikai Liao·Yi Ouyang·Yi-Lun Lee·Chen-Ping Yu·Yi-Hsuan Tsai·Zhaozheng Yin
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Abstract

Chatbots via large language models (LLMs) generate fluent responses but often struggle with when to speak, especially for brief, timely listener reactions during ongoing dialogue. We present a multimodal strategy for LLMs, which leverages synchronized video, audio, and text cues to improve conversational timing awareness. The strategy reformulates response timing as a dense response-type prediction task, enabling an agent to decide whether to remain silent, produce a short reaction, or start a full response under streaming constraints. Therefore, we introduce a curated multimodal dataset from real-world dyadic conversational videos with temporally aligned modalities and fine-grained reaction type annotations. Moreover, we design a multimodal strategy, MM-When2Speak, with a multimodal integration module on top of an LLM backbone. Experiments across various modality settings and strong LLM baselines show that MM-When2Speak achieves up to a 3x improvement in response type prediction performance, highlighting the importance of multimodal perception for natural and engaging conversational interaction.

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Zikai Liao, Yi Ouyang, Yi-Lun Lee, Chen-Ping Yu, Yi-Hsuan Tsai, Zhaozheng Yin. 2025-05-20. Beyond Words: Multimodal LLM Knows When to Speak. https://arxiv.org/abs/2505.14654

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