RAG-Check: Evaluating Multimodal Retrieval Augmented Generation Performance

• URL: http://arxiv.org/abs/2501.03995v1
• Date: Tue, 07 Jan 2025 18:52:05 GMT
• Title: RAG-Check: Evaluating Multimodal Retrieval Augmented Generation Performance
• Authors: Matin Mortaheb, Mohammad A. Amir Khojastepour, Srimat T. Chakradhar, Sennur Ulukus,
• Abstract summary: Retrieval-augmented generation (RAG) improves large language models by using external knowledge to guide response generation, reducing hallucinations. RAG can introduce new hallucination sources: (i) the retrieval process may select irrelevant pieces as raw context from the database, and (ii) retrieved images are processed into text-based context. We propose a novel framework to evaluate the reliability of multi-modal RAG using two performance measures: (i) the relevancy score (RS), assessing the relevance of retrieved entries to the query, and (ii) the correctness score (CS), evaluating the accuracy of the generated response
• Score: 28.63893944806149
• License:
• Abstract: Retrieval-augmented generation (RAG) improves large language models (LLMs) by using external knowledge to guide response generation, reducing hallucinations. However, RAG, particularly multi-modal RAG, can introduce new hallucination sources: (i) the retrieval process may select irrelevant pieces (e.g., documents, images) as raw context from the database, and (ii) retrieved images are processed into text-based context via vision-language models (VLMs) or directly used by multi-modal language models (MLLMs) like GPT-4o, which may hallucinate. To address this, we propose a novel framework to evaluate the reliability of multi-modal RAG using two performance measures: (i) the relevancy score (RS), assessing the relevance of retrieved entries to the query, and (ii) the correctness score (CS), evaluating the accuracy of the generated response. We train RS and CS models using a ChatGPT-derived database and human evaluator samples. Results show that both models achieve ~88% accuracy on test data. Additionally, we construct a 5000-sample human-annotated database evaluating the relevancy of retrieved pieces and the correctness of response statements. Our RS model aligns with human preferences 20% more often than CLIP in retrieval, and our CS model matches human preferences ~91% of the time. Finally, we assess various RAG systems' selection and generation performances using RS and CS.

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