提出Deepchecks框架，系统评估RAG模型的可靠性与相关性。
Deepchecks: Evaluating Retrieval-Augmented Generation (RAG)
- 设计多维度评估体系，覆盖检索与生成环节
- 支持根因分析与生产环境持续监控
- 适合医疗、金融等高可靠需求场景
大型语言模型结合检索增强生成（RAG）技术正推动医疗、金融和客户服务等多个领域的应用革新。尽管潜力巨大，但由于生成结果的随机性以及检索与生成组件之间的复杂交互，RAG系统的评估仍具挑战性。本文提出Deepchecks，一个专为RAG应用设计的综合性评估框架。该框架通过多维度评估、根因分析与生产监控，确保系统符合具体应用需求，为RAG系统的可靠性、相关性和用户满意度提供坚实评估基础。
原文摘要 · Abstract (English)
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.
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