August 25, 2023 — 4 min read
Introduction
In the ever-evolving landscape of natural language processing, the Retrieval Augmented Generation (RAG) approach has emerged as a powerful technique for generating contextually relevant and coherent responses. RAG pipelines integrate information retrieval and text generation, enabling AI models to provide more accurate and contextually appropriate answers. In this blog post, we delve into the crucial task of evaluating RAG pipelines and discuss the different dimensions that need to be considered for a comprehensive assessment.
One of the fundamental aspects of evaluating RAG pipelines is faithfulness, which assesses the consistency of generated answers with the provided context. The generated response should accurately reflect information present in the context and not introduce claims that cannot be deduced from it. Evaluating faithfulness involves comparing the generated response with the context to identify any inconsistencies or information distortions. Penalizing responses that deviate from the context's information ensures that the AI-generated answers maintain a high level of accuracy and reliability.
Context relevancy focuses on the appropriateness of retrieved contexts in relation to the given question. Ideally, the retrieved context should only contain information necessary to address the question effectively. Redundant information can lead to confusion and adversely affect the quality of the generated response. Evaluating context relevancy involves identifying instances of unnecessary or extraneous information in the retrieved context. By penalizing such instances, RAG pipelines can be optimized to provide concise and relevant information in their generated answers.
Context recall assesses the ability of RAG pipelines to retrieve relevant context information by comparing it to annotated answers, which act as proxies for ground truth context. Annotated answers are used as a benchmark for evaluating the completeness and accuracy of the retrieved context. Higher context recall indicates that the pipeline successfully captures context that aligns closely with the information needed to generate accurate responses. This evaluation dimension helps measure the effectiveness of the retrieval component within the RAG framework.
Answer relevancy gauges the extent to which the generated response directly addresses the given question or context. While not considering the answer's factual correctness, this dimension penalizes responses that include redundant or incomplete information. A relevant answer is one that aligns precisely with the question's requirements and avoids veering off-topic. This evaluation criterion ensures that the generated responses are not only contextually coherent but also directly applicable to the user's query.
In addition to the predefined evaluation dimensions, aspect critiques provide the flexibility to tailor the assessment to specific criteria. This dimension allows evaluators to define aspects such as harmlessness, correctness, or any other criteria that align with their specific needs. The output of aspect critiques is binary, simplifying the evaluation process by categorizing responses as meeting or not meeting the defined aspects. This customization empowers evaluators to assess RAG pipelines based on their unique requirements and objectives.
Evaluating Retrieval Augmented Generation (RAG) pipelines requires a multi-dimensional approach that considers faithfulness, context relevancy, context recall, answer relevancy, and aspect critiques. By systematically analyzing these dimensions, developers and researchers can fine-tune RAG pipelines to achieve optimal performance in generating contextually accurate and coherent responses. As AI continues to play a significant role in information dissemination and communication, robust evaluation methodologies like the ones discussed here are crucial for ensuring the reliability and effectiveness of RAG-based applications.
Here is the framework to implement it.