Language Model Efficiency
Andrew discusses the growing context sizes of language models and the inefficiencies of extracting data from large documents. He critiques the reliance on prompting, suggesting that it often leads to imprecise results. Additionally, he advocates for the use of smaller language models for specific tasks, arguing that larger models are overkill for minute details. Timothy adds to the conversation by questioning the lessons learned from previous ML Ops waves and their relevance in the current landscape.In this clip
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