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Fine-tuning large language models adapts pre-trained models to specific tasks or domains using tailored datasets, while Retrieval-Augmented Generation (RAG) combines retrieval systems with generative models to dynamically incorporate external, up-to-date knowledge into outputs.

RAG vs Fine-Tuning: Differences, Benefits, and Use Cases Explained

Researchers have found that AI large language models, like GPT-4, are better at predicting what comes next than what came before in a sentence. This “Arrow of Time” effect could reshape our understanding of the structure of natural language, and the way these models understand it.

Large Language Models feel the direction of time