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The paper compares large language models and embedding models across 37 tasks, finding that while aggregate performance is similar, embedding models are far cheaper and faster, supporting a division of labor for cost-efficiency.
A discussion on using applications to enhance the effectiveness of smaller AI models on larger tasks, balancing efficiency and performance.