通过用户反馈优化生成式信息检索系统，提升交互智能水平。
Improving GenIR Systems Based on User Feedback
- 引入多类型用户反馈机制，增强系统理解能力。
- 结合对话上下文实现持续学习与提示优化。
- 适合关注交互式AI系统进化的研究者与开发者。
本章探讨如何基于用户反馈改进生成式信息检索（GenIR）系统。随着交互发展，用户概念已扩展至多种反馈形式。文中介绍了不同类型的反馈信息与策略，并重点分析了对齐技术的目标与方法。随后阐述了在GenIR中利用用户反馈的多种学习方式，包括持续学习、对话上下文中的学习与排序、以及提示学习。研究表明，这些创新方法已超越传统反馈使用模式，显著推动了GenIR在新阶段的发展。最后总结了若干待深入研究的挑战性课题与未来方向。
原文摘要 · Abstract (English)
In this chapter, we discuss how to improve the GenIR systems based on user feedback. Before describing the approaches, it is necessary to be aware that the concept of "user" has been extended in the interactions with the GenIR systems. Different types of feedback information and strategies are also provided. Then the alignment techniques are highlighted in terms of objectives and methods. Following this, various ways of learning from user feedback in GenIR are presented, including continual learning, learning and ranking in the conversational context, and prompt learning. Through this comprehensive exploration, it becomes evident that innovative techniques are being proposed beyond traditional methods of utilizing user feedback, and contribute significantly to the evolution of GenIR in the new era. We also summarize some challenging topics and future directions that require further investigation.
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