RAGAS: Automated Evaluation of Retrieval Augmented Generation

• URL: http://arxiv.org/abs/2309.15217v1
• Date: Tue, 26 Sep 2023 19:23:54 GMT
• Title: RAGAS: Automated Evaluation of Retrieval Augmented Generation
• Authors: Shahul Es, Jithin James, Luis Espinosa-Anke, Steven Schockaert
• Abstract summary: RAGAs is a framework for evaluation of Retrieval Augmented Generation pipelines. RAG systems are composed of a retrieval and an LLM based generation module.
• Score: 25.402461447140823
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce RAGAs (Retrieval Augmented Generation Assessment), a framework for reference-free evaluation of Retrieval Augmented Generation (RAG) pipelines. RAG systems are composed of a retrieval and an LLM based generation module, and provide LLMs with knowledge from a reference textual database, which enables them to act as a natural language layer between a user and textual databases, reducing the risk of hallucinations. Evaluating RAG architectures is, however, challenging because there are several dimensions to consider: the ability of the retrieval system to identify relevant and focused context passages, the ability of the LLM to exploit such passages in a faithful way, or the quality of the generation itself. With RAGAs, we put forward a suite of metrics which can be used to evaluate these different dimensions \textit{without having to rely on ground truth human annotations}. We posit that such a framework can crucially contribute to faster evaluation cycles of RAG architectures, which is especially important given the fast adoption of LLMs.

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