Evolving Token Representations
The discussion delves into how individual token representations in transformers evolve under different training objectives, such as machine translation and language modeling. The exploration reveals that as models progress through their layers, they refine their understanding by filtering out irrelevant information while retaining what is essential, ultimately impacting the relationships between tokens. This nuanced understanding of information flow highlights the importance of learning objectives in shaping model behavior.In this clip
From this podcast
Machine Learning Street Talk (MLST)
#039 - Lena Voita - NLP
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