A team from the University of Washington and Google Research recently open-sourced Distilling Step-by-Step, a technique for fine-tuning smaller language models. Distilling Step-by-Step requires less training data than standard fine-tuning and results in smaller models that can outperform few-shot prompted large language models (LLMs) that have 700x the parameters.
By Anthony Alford
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