Generative artificial intelligence (GenAI) is expected to gain traction in the enterprise this year, with retrieval-augmented generation (RAG) being a key methodology. However, RAG is still an emerging technology with challenges. To address this, researchers at Amazon’s AWS have proposed a benchmarking process to evaluate how well RAG can answer domain-specific questions. Their paper, “Automated Evaluation of Retrieval-Augmented Language Models with Task-Specific Exam Generation,” outlines a strategy to select optimal components for RAG systems. The paper will be presented at the 41st International Conference on Machine Learning. The authors highlight the lack of standardized evaluation methods for RAG systems and introduce an automated approach using question-answer pairs from various domains. They test open-source language models Mistral and Llama in different scenarios, including closed book, Oracle, and classical retrieval forms. The results suggest that better RAG algorithms can enhance language model performance more than simply increasing model size. Additionally, a poorly aligned retriever component in the RAG algorithm can lead to decreased accuracy compared to a basic language model. This research sheds light on the importance of choosing the right retrieval method for optimal performance in generative AI systems.
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Source link: https://www.zdnet.com/article/amazon-proposes-a-new-ai-benchmark-to-measure-rag/
Amazon introduces new AI benchmark to measure RAG performance. #AIbenchmark
