Deepchecks: 评估检索增强生成（RAG）
Deepchecks: Evaluating Retrieval-Augmented Generation (RAG)
- Deepchecks, Ramat Gan, Israel（深检查，以色列拉马特甘）
- Ben-Gurion University, Beer Sheva, Israel（本· Gurion大学，以色列贝尔谢巴）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出Deepchecks框架，用于评估RAG系统，通过多维方法和根本原因分析，提升系统可靠性、相关性和用户满意度。
AI中文摘要：
大型语言模型（LLMs）结合检索增强生成（RAG）技术正在多个领域如医疗、金融和客户服务中革新应用。尽管潜力巨大，但评估RAG系统仍面临挑战，因生成输出的随机性和检索与生成组件的复杂交互。本文介绍Deepchecks，一种针对RAG应用的综合框架，通过多维方法和根本原因分析，确保与应用特定需求一致，为评估RAG系统的可靠性、相关性和用户满意度提供坚实基础。
英文摘要：
Large Language Models (LLMs) augmented with Retrieval-Augmented Generation (RAG) techniques are revolutionizing applications across multiple domains, such as healthcare, finance, and customer service. Despite their potential, evaluating RAG systems remains a complex challenge due to the stochastic nature of generated outputs and the intricate interplay between retrieval and generation components. This paper introduces Deepchecks, a comprehensive framework tailored for evaluating RAG applications. Deepchecks' evaluation framework addresses RAG applications evaluation through a multi-faceted approach, root cause analysis and production monitoring. By ensuring alignment with application-specific requirements, Deepchecks framework provides a robust foundation for assessing reliability, relevance, and user satisfaction in RAG systems.
