@inproceedings{clark-schuler-2026-evaluating,
    title = "Evaluating Humanlike Memory Effects in Transformers Using Item Recognition Tasks",
    author = "Clark, Christian  and
      Schuler, William",
    editor = "Bonial, Claire  and
      Berzak, Yevgeni",
    booktitle = "Proceedings of the 30th Conference on Computational Natural Language Learning",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.conll-main.1/",
    pages = "1--14",
    ISBN = "979-8-89176-410-1",
    abstract = "Recent studies examining cued recall in Transformers have observed that these language models remember information from the beginning or end of a passage more easily than information in the middle, a pattern which is evocative of serial position effects (primacy and recency) observed in human memory. However, while these effects have been documented in humans across a range of memory tasks (e.g., serial recall, free recall, item recognition), it is less clear whether they generalize beyond cued recall in Transformers.We address this limitation of previous work by performing novel behavioral evaluations on Transformers using a simple item recognition paradigm, which we compare against evaluations using cued recall. We find that Transformers show weak or absent recency effects in item recognition, a pattern which differs from human behavior and from Transformers' own behavior in cued recall. A subsequent experiment examines the role of Transformers' architectural biases in producing serial position effects in item recognition and cued recall."
}