Efficient Learning with Distilling Step-by-Step

In an era where data is abundant yet precious, a new technique (“Distilling Step-by-Step”)  transforms Large Language Models (LLMs) from mere label predictors to reasoning agents that provide intermediate rationales, bridging the gap between inputs and final answers. This mechanism enables the crafting of efficient task-specific models that require less data, less computational cost, andContinue reading "Efficient Learning with Distilling Step-by-Step"

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agents computational cost data gap intermediate language language models large language large language models llms reasoning