PaRT: Enhancing Proactive Social Chatbots with Personalized Real-Time Retrieval
This addresses the issue of diminished engagement in social chatbots for users in daily scenarios like emotional support, though it appears incremental as it builds on existing LLM and retrieval methods.
The paper tackles the problem of passive social chatbots that rely on users to initiate topics, leading to low engagement, by introducing PaRT, a framework for proactive dialogues using personalized real-time retrieval and generation, resulting in a 21.77% improvement in average dialogue duration.
Social chatbots have become essential intelligent companions in daily scenarios ranging from emotional support to personal interaction. However, conventional chatbots with passive response mechanisms usually rely on users to initiate or sustain dialogues by bringing up new topics, resulting in diminished engagement and shortened dialogue duration. In this paper, we present PaRT, a novel framework enabling context-aware proactive dialogues for social chatbots through personalized real-time retrieval and generation. Specifically, PaRT first integrates user profiles and dialogue context into a large language model (LLM), which is initially prompted to refine user queries and recognize their underlying intents for the upcoming conversation. Guided by refined intents, the LLM generates personalized dialogue topics, which then serve as targeted queries to retrieve relevant passages from RedNote. Finally, we prompt LLMs with summarized passages to generate knowledge-grounded and engagement-optimized responses. Our approach has been running stably in a real-world production environment for more than 30 days, achieving a 21.77\% improvement in the average duration of dialogues.