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About this paper
Thomas F Burns, Tomoki Fukai, Christopher J Earls
The value-residual Transformer adds a direct path carrying attention-head values between successive layers, and the authors test whether it helps models use examples in context. In a synthetic novel-pair classification task, it first exceeds 95% accuracy after a mean of 49.0 snapshots, versus 64.5 for the classic model. Broader language-task gains are modest and localized.
Summary by alphaXiv.
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