Computer Science > Machine Learning
arXiv:2603.09078 (cs)
[Submitted on 10 Mar 2026]
Title:Exclusive Self Attention
Authors:Shuangfei Zhai
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Abstract:We introduce exclusive self attention (XSA), a simple modification of self attention (SA) that improves Transformer's sequence modeling performance. The key idea is to constrain attention to capture only information orthogonal to the token's own value vector (thus excluding information of self position), encouraging better context modeling. Evaluated on the standard language modeling task, XSA consistently outperforms SA across model sizes up to 2.7B parameters and shows increasingly larger gains as sequence length grows.
| Subjects: | Machine Learning (cs.LG); Computation and Language (cs.CL) |
| Cite as: | arXiv:2603.09078 [cs.LG] |
| (or arXiv:2603.09078v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2603.09078
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