@inproceedings{li-etal-2025-proactive,
    title = "Proactive Guidance of Multi-Turn Conversation in Industrial Search",
    author = "Li, Xiaoyu  and
      Li, Xiao  and
      Gao, Li  and
      Liu, Yiding  and
      Wang, Xiaoyang  and
      Wang, Shuaiqiang  and
      Wang, Junfeng  and
      Yin, Dawei",
    editor = "Rehm, Georg  and
      Li, Yunyao",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.acl-industry.50/",
    pages = "706--717",
    ISBN = "979-8-89176-288-6",
    abstract = "The evolution of Large Language Models (LLMs) has significantly advanced multi-turn conversation systems, emphasizing the need for proactive guidance to enhance users' interactions. However, these systems face challenges in dynamically adapting to shifts in users' goals and maintaining low latency for real-time interactions. In the Baidu Search AI assistant, an industrial-scale multi-turn search system, we propose a novel two-phase framework to provide proactive guidance. The first phase, Goal-adaptive Supervised Fine-Tuning (G-SFT), employs a goal adaptation agent that dynamically adapts to user goal shifts and provides goal-relevant contextual information. G-SFT also incorporates scalable knowledge transfer to distill insights from LLMs into a lightweight model for real-time interaction. The second phase, Click-oriented Reinforcement Learning (C-RL), adopts a generate-rank paradigm, systematically constructs preference pairs from user click signals, and proactively improves click-through rates through more engaging guidance. This dual-phase architecture achieves complementary objectives: G-SFT ensures accurate goal tracking, while C-RL optimizes interaction quality through click signal-driven reinforcement learning. Extensive experiments demonstrate that our framework achieves 86.10{\%} accuracy in offline evaluation (+23.95{\%} over baseline) and 25.28{\%} CTR in online deployment (149.06{\%} relative improvement), while reducing inference latency by 69.55{\%} through scalable knowledge distillation."
}