@inproceedings{hong-etal-2025-exploring,
    title = "Exploring Working Memory Capacity in {LLM}s: From Stressors to Human-Inspired Strategies",
    author = "Hong, Eunjin  and
      Cho, Sumin  and
      Kim, Juae",
    editor = "Inui, Kentaro  and
      Sakti, Sakriani  and
      Wang, Haofen  and
      Wong, Derek F.  and
      Bhattacharyya, Pushpak  and
      Banerjee, Biplab  and
      Ekbal, Asif  and
      Chakraborty, Tanmoy  and
      Singh, Dhirendra Pratap",
    booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.ijcnlp-long.93/",
    pages = "1727--1744",
    ISBN = "979-8-89176-298-5",
    abstract = "Large language models (LLMs) exhibit inherent limitations in working memory, which often affect their overall capabilities. However, prior studies have largely focused on describing such constraints without identifying their causes or providing practical strategies to cope with them. In this paper, we investigate the limited working memory capacity of LLMs through a series of empirical studies. Specifically, we examine the factors involved in the limited capacity and explore strategies to make more effective use of it. Our analysis shows that the number and difficulty of tasks in a single input largely strain the working memory of LLMs. In response, we design a cognitive marker consisting of simple token sequences theoretically grounded in cognitive science. Further analyses show that the cognitive marker reduces the overall prediction difficulty and uncertainty for the models to process the input, and its effectiveness is confirmed across various evaluation settings. Overall, our study incorporates cognitively motivated perspectives into the analysis of model behavior and highlights the need for deeper exploration of working memory in LLMs."
}