新闻
PACE: Perceived-Latency-Aware Cascading Service Routing and Filler Control for QoE-Efficient Retrieval-Augmented Dialogue Serving
arXiv cs.AI · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- PACE framework optimizes retrieval-augmented dialogue serving by routing queries to appropriate answer sources and controlling filler content during response delays.
- 为何重要
- Matters for engineers building conversational AI systems where response latency affects user experience, especially in customer service or robotics applications.
- 注意
- Results demonstrated on specific CarQA dataset with humanoid robot deployment; generalization to other dialogue domains and real-world conditions remains unclear.
- retrieval
- serving
- latency
- eval
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