@inproceedings{sasaki-etal-2025-language,
    title = "Can Language Models Handle a Non-Gregorian Calendar? The Case of the {J}apanese wareki",
    author = "Sasaki, Mutsumi  and
      Kamoda, Go  and
      Takahashi, Ryosuke  and
      Sato, Kosuke  and
      Inui, Kentaro  and
      Sakaguchi, Keisuke  and
      Heinzerling, Benjamin",
    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-short.36/",
    pages = "444--463",
    ISBN = "979-8-89176-299-2",
    abstract = "Temporal reasoning and knowledge are essential capabilities for language models (LMs).While much prior work has analyzed and improved temporal reasoning in LMs, most studies have focused solely on the Gregorian calendar.However, many non-Gregorian systems, such as the Japanese, Hijri, and Hebrew calendars, are in active use and reflect culturally grounded conceptions of time.If and how well current LMs can accurately handle such non-Gregorian calendars has not been evaluated so far.Here, we present a systematic evaluation of how well language models handle one such non-Gregorian system: the Japanese *wareki*.We create datasets that require temporal knowledge and reasoning in using *wareki* dates. Evaluating open and closed LMs, we find that some models can perform calendar conversions, but GPT-4o, Deepseek V3, and even Japanese-centric models struggle with Japanese calendar arithmetic and knowledge involving *wareki* dates.Error analysis suggests corpus frequency of Japanese calendar expressions and a Gregorian bias in the model{'}s knowledge as possible explanations.Our results show the importance of developing LMs that are better equipped for culture-specific tasks such as calendar understanding."
}