The DIFF Transformer innovates by enhancing traditional attention mechanisms, refining context handling, and minimizing distractions, leading to improved performance in language tasks.
This new architecture leverages a differential attention mechanism by comparing two attention maps, allowing for more efficient focus on relevant input parts.
Experiments reveal that the DIFF Transformer excels in language modeling and information retrieval, demonstrating superior efficiency and accuracy compared to conventional transformers.
The DIFF Transformer is particularly advantageous for long-context modeling and key information retrieval, making it highly effective in low-resource environments.
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