@inproceedings{khodja-etal-2025-factual,
    title = "Factual Knowledge in Language Models: Robustness and Anomalies under Simple Temporal Context Variations",
    author = "Khodja, Hichem Ammar  and
      Bechet, Frederic  and
      Brabant, Quentin  and
      Nasr, Alexis  and
      Lecorv{\'e}, Gw{\'e}nol{\'e}",
    editor = "Jia, Robin  and
      Wallace, Eric  and
      Huang, Yangsibo  and
      Pimentel, Tiago  and
      Maini, Pratyush  and
      Dankers, Verna  and
      Wei, Johnny  and
      Lesci, Pietro",
    booktitle = "Proceedings of the First Workshop on Large Language Model Memorization (L2M2)",
    month = aug,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.l2m2-1.1/",
    doi = "10.18653/v1/2025.l2m2-1.1",
    pages = "1--22",
    ISBN = "979-8-89176-278-7",
    abstract = "This paper explores the robustness of language models (LMs) to variations in the temporal context within factual knowledge. It examines whether LMs can correctly associate a temporal context with a past fact valid over a defined period, by asking them to differentiate correct from incorrect contexts. The LMs' ability to distinguish is analyzed along two dimensions: the distance of the incorrect context from the validity period and the granularity of the context. To this end, a dataset called TimeStress is introduced, enabling the evaluation of 18 diverse LMs. Results reveal that the best LM achieves a perfect distinction for only 11{\%} of the studied facts, with errors, certainly rare, but critical that humans would not make. This work highlights the limitations of current LMs in temporal representation."
}