Evolution of Language Models
The discussion highlights the shift from encoder-decoder models to text generation models, emphasizing the importance of data curation and training phases over architectural changes. Key insights reveal that while the architecture remains largely unchanged, improvements in data quality and training methods can significantly enhance model performance. Understanding these elements is crucial for anyone looking to rebuild or innovate in the field of language modeling.In this clip
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Machine Learning Street Talk (MLST)
Jay Alammar on LLMs, RAG, and AI Engineering
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