NExT-Search：重构生成式AI搜索的用户反馈生态系统

NExT-Search: Rebuilding User Feedback Ecosystem for Generative AI Search

May 20, 2025

作者: Sunhao Dai, Wenjie Wang, Liang Pang, Jun Xu, See-Kiong Ng, Ji-Rong Wen, Tat-Seng Chua

cs.AI

摘要

生成式AI搜索正在重塑信息检索领域，它能够为复杂查询提供端到端的答案，减少了用户手动浏览和总结多个网页的需求。然而，尽管这一范式提升了便利性，它却打破了传统网络搜索赖以进化的反馈驱动改进循环。网络搜索通过收集大规模、细粒度的文档级用户反馈（如点击、停留时间）来持续优化其排序模型。相比之下，生成式AI搜索则通过一个更长的搜索流程运作，涵盖查询分解、文档检索和答案生成，但通常仅能获得对最终答案的粗粒度反馈。这导致了反馈循环的脱节，即用户对最终输出的反馈无法有效映射回具体系统组件，使得改进每个中间阶段和维持反馈循环变得困难。本文中，我们展望了NExT-Search，这一下一代范式旨在将细粒度的过程级反馈重新引入生成式AI搜索。NExT-Search整合了两种互补模式：用户调试模式，允许积极参与的用户在关键阶段进行干预；以及影子用户模式，其中个性化用户代理模拟用户偏好，为互动较少的用户提供AI辅助的反馈。此外，我们设想了如何通过在线适应（实时精炼当前搜索输出）和离线更新（汇总交互日志以定期微调查询分解、检索和生成模型）来利用这些反馈信号。通过恢复人类对生成式AI搜索流程关键阶段的控制，我们相信NExT-Search为构建能够随人类反馈持续进化的反馈丰富的AI搜索系统指明了一个充满希望的方向。

English

Generative AI search is reshaping information retrieval by offering end-to-end answers to complex queries, reducing users' reliance on manually browsing and summarizing multiple web pages. However, while this paradigm enhances convenience, it disrupts the feedback-driven improvement loop that has historically powered the evolution of traditional Web search. Web search can continuously improve their ranking models by collecting large-scale, fine-grained user feedback (e.g., clicks, dwell time) at the document level. In contrast, generative AI search operates through a much longer search pipeline, spanning query decomposition, document retrieval, and answer generation, yet typically receives only coarse-grained feedback on the final answer. This introduces a feedback loop disconnect, where user feedback for the final output cannot be effectively mapped back to specific system components, making it difficult to improve each intermediate stage and sustain the feedback loop. In this paper, we envision NExT-Search, a next-generation paradigm designed to reintroduce fine-grained, process-level feedback into generative AI search. NExT-Search integrates two complementary modes: User Debug Mode, which allows engaged users to intervene at key stages; and Shadow User Mode, where a personalized user agent simulates user preferences and provides AI-assisted feedback for less interactive users. Furthermore, we envision how these feedback signals can be leveraged through online adaptation, which refines current search outputs in real-time, and offline update, which aggregates interaction logs to periodically fine-tune query decomposition, retrieval, and generation models. By restoring human control over key stages of the generative AI search pipeline, we believe NExT-Search offers a promising direction for building feedback-rich AI search systems that can evolve continuously alongside human feedback.