Multi-Token Attention: Going Beyond Single-Token Focus in Transformers

"Multi-Token Attention (MTA) allows transformers to process and attend to groups of tokens simultaneously, enhancing the model's contextual understanding and performance."

"Existing single-token attention considers one token at a time, whereas MTA enables models to simultaneously evaluate multiple tokens, providing greater flexibility and a more nuanced understanding."

"MTA introduces significant benefits for tasks that involve complex relationships among multiple tokens, improving performance in language models, vision transformers, and multi-modal architectures."

"While MTA enhances attention capabilities, it also introduces trade-offs that need to be understood in relation to model complexity and computational efficiency."

Transformers have transformed natural language processing with their attention mechanisms that highlight relevant tokens. Yet, traditional attention analyzes only one token at a time, leaving gaps in contextual understanding. Multi-Token Attention (MTA) emerges as a solution, allowing simultaneous attention to multiple tokens, which improves the model's ability to capture complex relationships. The article discusses how MTA operates, its necessity compared to standard attention, and the trade-offs it presents for enhancing the capabilities of language models, vision transformers, and multi-modal architectures.

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