ConvFill: Model Collaboration for Responsive Conversational Voice Agents
This addresses the problem of responsiveness and knowledge access for on-device conversational agents, representing an incremental improvement over existing methods.
The paper tackles the challenge of latency in conversational voice agents by proposing conversational infill, where a lightweight on-device model generates dialogue while incorporating knowledge from a backend model, achieving accuracy improvements of 36-42% over standalone small models with sub-200ms latency.
Deploying conversational voice agents with large language models faces a critical challenge: cloud-based foundation models provide deep reasoning and domain knowledge but introduce latency that disrupts natural conversation, while on-device models respond immediately but lack sophistication. We propose conversational infill, a task where a lightweight on-device model generates contextually appropriate dialogue while seamlessly incorporating streaming knowledge from a powerful backend model. This approach decouples response latency from model capability, enabling systems that feel responsive while accessing the full power of large-scale models. We present ConvFill, a 360M parameter model trained on synthetic multi-domain conversations. Evaluation across multiple backend models shows that conversational infill can be successfully learned, with ConvFill achieving accuracy improvements of 36-42% over standalone small models of the same size while consistently retaining sub-200ms response latencies. Our results demonstrate the promise of this approach for building on-device conversational agents that are both immediately responsive and knowledgeable.