Archives for Google Introduces Big Bird
Google transformer-based models like BERTshowcased immense success with NLP tasks; however, came with a significant limitation of quadratic dependency in-memory storage for the sequence length. A lot of this could be attributed to its full attention mechanism for sequence lengths. Such a self-attention mechanism can create several challenges for processing longer text sequences. Thus to…
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Recently, Google Research introduced a new sparse attention mechanism that improves performance on a multitude of tasks that require long contexts known as BigBird. The researchers took inspiration from the graph sparsification methods. They understood where the proof for the expressiveness of Transformers breaks down when full-attention is relaxed to form the proposed attention pattern.…
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