Retrieval-Enhanced Machine Learning (REML)
Topics: AI (Deep Learning), Retrieval Augmented Generation (RAG)
The document describes Retrieval-Enhanced Machine Learning (REML), a framework designed to improve machine learning models by integrating information retrieval (IR) techniques. Traditional ML models store knowledge within their parameters, requiring large-scale models for better accuracy. However, REML proposes an alternative approach where models retrieve relevant information dynamically, reducing the need for excessive parameters. This enables better generalization, scalability, robustness, and interpretability. The framework introduces core principles such as querying, retrieval, response utilization, and optional storage and feedback mechanisms, aiming to make ML models more efficient and adaptable.
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