In the rapidly evolving domain of Artificial Intelligence, Natural Language Processing (NLP), and Information Retrieval, the advent of advanced models like Retrieval Augmented Generation (RAG) has stirred considerable interest. Despite this, many data science experts advise against jumping into complex RAG models until the evaluation pipeline is fully reliable and robust.
Performing comprehensive assessments of RAG pipelines is crucial, but often neglected in the rush to implement advanced features. Researchers and practitioners are advised to prioritize enhancing their evaluation setup before attempting complex model enhancements.
Understanding the evaluation intricacies for RAG pipelines is crucial as these models rely on both generation capabilities and retrieval quality. These dimensions are categorized into two main groups: Retrieval Dimensions and Generation Dimensions.
Retrieval Dimensions include:
1. Context Precision: Determines the priority ranking of each ground-truth item in the context relative to other items.
2. Context Recall: Measures the degree of match between the ground-truth response and the recovered context. It is dependent on both the retrieved context and the ground truth.
3. Context Relevance: Assesses the relevance of the context that is provided.
4. Context Entity Recall: Calculates the recall of the retrieved context by comparing the number of entities present in the ground truths and contexts to the number present in the ground truths alone.
5. Noise Robustness: Evaluates the ability of the model to deal with irrelevant question-associated noise documents.
Generation Dimensions encompass:
1. Faithfulness: Examines the factual consistency of the generated response with the given context.
2. Answer Relevance: Calculates the appropriateness of the generated response to the given question by penalizing responses that contain redundant or missing information.
3. Negative Rejection: Assesses the ability of the model to refrain from responding when the retrieved documents lack sufficient information to answer a question.
4. Information Integration: Evaluates the model’s ability to amalgamate data from various documents to answer complex queries.
5. Counterfactual Robustness: Assesses the model’s ability to recognize and disregard known errors in documents, despite being aware of potential misinformation.
Researchers can access several frameworks incorporating these dimensions: Ragas, TruLens, ARES, DeepEval, Tonic Validate, and LangFuse.
Finally, it’s asserted that understanding and implementing these dimensions into the evaluation pipeline before delving into intricate model enhancements is essential. By doing this, researchers and practitioners can ensure the reliability and robustness of their models, thereby enhancing their efficacy and overall performance.
