@inproceedings{naim-etal-2026-ssa,
    title = "{SSA}: Improving Performance With a Better Scoring Function",
    author = "Naim, Omar  and
      Bhar, Swarnadeep  and
      Bolte, Jerome  and
      Asher, Nicholas",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.1385/",
    pages = "30033--30051",
    ISBN = "979-8-89176-390-6",
    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 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."
}