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arXiv · 2508.14685

SSA: Improving Performance With a Better Scoring Function

Omar Naim·Swarnadeep Bhar·Jérôme Bolte·Nicholas Asher
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Abstract

While transformer models exhibit strong in-context learning (ICL) abilities, they often fail to generalize under simple distribution shifts. We analyze these failures and identify Softmax, the scoring function in the attention mechanism, as a contributing factor. We propose \textbf{Scaled Signed Averaging (SSA)}, a novel attention scoring function that mitigates these failures. SSA significantly improves performance on our ICL tasks and outperforms transformer models with Softmax on several NLP benchmarks and linguistic probing tasks, in both decoder-only and encoder-only architectures.

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Omar Naim, Swarnadeep Bhar, Jérôme Bolte, Nicholas Asher. 2025-08-20. SSA: Improving Performance With a Better Scoring Function. https://arxiv.org/abs/2508.14685

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