发表机构
Intel Corporation（英特尔公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究在信息检索系统中，通过整合辅助反馈RAG系统及人工参与，利用人类反馈优化主RAG系统性能，经多数据集测试验证方法有效，强调了其变革潜力，为自适应信息检索技术研究树立了先例。
AI 中文摘要
在信息检索系统快速发展的背景下，通过用户反馈进行适应和改进的能力至关重要。本研究引入一种新方法，通过策略性整合辅助反馈检索增强生成（RAG）系统来优化主RAG系统性能。该方法系统利用人类生成的反馈，旨在提高响应的准确性、相关性和整体质量，推动系统自我提升。其核心是人工参与实现，持续收集、分类用户反馈并整合到推理工作流程中，使系统能迭代学习和进化。为验证该方法有效性，研究针对三个不同基准数据集，采用大语言模型作为评判的评估策略进行严格测试。这个全面框架不仅强调了反馈驱动增强在RAG系统中的变革潜力，也为自适应信息检索技术的未来研究树立了先例，标志着通过用户参与实现自主优化的重要一步。
英文摘要
In the rapidly evolving landscape of information retrieval systems, the ability to adapt and improve through user feedback is paramount. This study introduces a novel methodology for refining the performance of a primary Retrieval Augmented Generation (RAG) system by strategically integrating an auxiliary feedback RAG system. By systematically harnessing human-generated feedback, the approach aims to enhance the accuracy, relevance, and overall quality of responses, driving the system towards self-improvement. Central to this methodology is a human-in-the-loop implementation, where user feedback is continuously collected, classified, and integrated into the inference workflow, enabling the system to learn and evolve iteratively. To validate the effectiveness of this approach, the study employs rigorous testing against three diverse benchmark datasets focused on general and custom domain knowledge, utilizing a LLM-as-a-Judge evaluation strategy. This comprehensive framework not only underscores the transformative potential of feedback-driven enhancements in RAG systems but also sets a precedent for future research in adaptive information retrieval technologies, marking a significant step in the journey towards autonomous refinement and optimization through user engagement.
CommentsAIC 2025: The 10th International Workshop on Artificial Intelligence and Cognition (held as part of ECAI 2025). October 25-26, 2025. Bologna, Italy
