Improving GenIR Systems Based on User Feedback

TL;DR

This paper explores methods to enhance Generative Information Retrieval (GenIR) systems by leveraging diverse user feedback, including alignment techniques and learning strategies, to improve system performance and adapt to evolving user interactions.

Contribution

It introduces innovative feedback utilization methods and learning strategies that advance GenIR system capabilities beyond traditional approaches.

Findings

Various feedback types and strategies are effective in improving GenIR.

Alignment techniques enhance system objectives and methods.

New learning approaches like continual and prompt learning are promising.

Abstract

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.