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Does the DIFF Transformer make a Diff?
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Introducing a novel transformer architecture, Differential Transformer, designed to improve the performance of large language models. The key innovation lies in its differential attention mechanism, which calculates attention scores as the difference between two separate softmax attention maps. This subtraction effectively cancels out irrelevant context (attention noise), enabling the model to focus on crucial information. The authors demonstrate that Differential Transformer outperforms traditional transformers in various tasks, including long-context modeling, key information retrieval, and hallucination mitigation. Furthermore, Differential Transformer exhibits greater robustness to order permutations in in-context learning and reduces activation outliers, paving the way for more efficient quantization. These advantages position Differential Transformer as a promising foundation architecture for future large language model development.
Read the research here: https://arxiv.org/pdf/2410.05258
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