arXiv:2505.14654v2 Announce Type: replace-cross 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.
The rapid advancement of multimodal AI capabilities is enabling more sophisticated human-computer interaction models, addressing a critical limitation in current LLM-based conversational agents.
This breakthrough improves the naturalness and effectiveness of AI conversations, pushing towards more seamless integration of AI into daily interactions and professional workflows.
LLMs can now proactively manage conversational timing, moving beyond simply generating fluent text to understanding the opportune moment for response, reaction, or silence through multimodal cues.
- · AI developers
- · Customer service platforms
- · Virtual assistants
- · Monologue-based chatbots
- · Companies with primitive conversational AI
Multimodal LLMs will offer more nuanced and context-aware conversational experiences, reducing user frustration.
The improved interaction quality will accelerate the adoption of AI agents in roles requiring complex verbal communication.
As AI communication becomes indistinguishable from human conversation, the line between human and artificial presence in digital spaces will further blur.
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Read at arXiv cs.AI