@inproceedings{kessler-2025-dataset,
    title = "A Dataset of {A}ncient {C}hinese Math Word Problems and an Application for Research in Historic Mathematics",
    author = "Ke{\ss}ler, Florian",
    editor = "Anderson, Adam  and
      Gordin, Shai  and
      Li, Bin  and
      Liu, Yudong  and
      Passarotti, Marco C.  and
      Sprugnoli, Rachele",
    booktitle = "Proceedings of the Second Workshop on Ancient Language Processing",
    month = may,
    year = "2025",
    address = "The Albuquerque Convention Center, Laguna",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.alp-1.8/",
    doi = "10.18653/v1/2025.alp-1.8",
    pages = "59--70",
    ISBN = "979-8-89176-235-0",
    abstract = "Solving math word problems, i.e. mathemati-cal problems stated in natural language, has re-ceived much attention in the Artificial Intelli-gence (AI) community over the last years. Un-surprisingly, research has focused on problems stated in contemporary languages. In contrast to this, in this article, we introduce a dataset of math word problems that is extracted from an-cient Chinese mathematical texts. The dataset is made available.1 We report a baseline per-formance for GPT-4o solving the problems in the dataset using a Program-of-Thought paradigm that translates the mathematical pro-cedures in the original texts into Python code, giving acceptable performance but showing that the model often struggles with understand-ing the pre-modern language. Finally, we de-scribe how the generated code can be used for research into the history of mathematics, by of-fering a way to search the texts by abstract op-erations instead of specific lexemes."
}