PaRT：通过个性化实时检索增强主动社交聊天机器人
PaRT: Enhancing Proactive Social Chatbots with Personalized Real-Time Retrieval
AI总结：
针对传统被动响应聊天机器人导致用户参与度低和对话时长缩短的问题，提出PaRT框架，通过整合用户画像与上下文、利用LLM优化查询意图并生成个性化话题，从RedNote检索知识以生成优化回复，在真实环境中使对话平均时长提升21.77%。
摘要：
社交聊天机器人已成为日常场景中从情感支持到个人互动的重要智能伴侣。然而，具有被动响应机制的传统聊天机器人通常依赖用户通过提出新话题来发起或维持对话，导致参与度下降和对话时长缩短。在本文中，我们提出了PaRT，这是一种新颖的框架，通过个性化实时检索和生成，为社交聊天机器人实现具备上下文感知的主动对话。具体而言，PaRT首先将用户画像和对话上下文整合到大型语言模型（LLM）中，该模型最初被提示以优化用户查询并识别其在即将到来的对话中的潜在意图。在优化后的意图指导下，LLM生成个性化对话话题，这些话题随后作为目标查询，从RedNote中检索相关段落。最后，我们用总结的段落提示LLM，以生成基于知识且优化参与度的回复。我们的方法已在真实的生产环境中稳定运行超过30天，实现了对话平均时长21.77%的提升。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
