@inproceedings{han-etal-2025-analysis,
    title = "An Analysis of the Impact of Problem Paraphrasing on {LLM}-Based Mathematical Problem Solving",
    author = "Han, Yerim  and
      Seo, Hyein  and
      Namgoong, Hyuk  and
      Jung, Sangkeun",
    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.23/",
    pages = "383--395",
    ISBN = "979-8-89176-298-5",
    abstract = "Recent advances in large language models (LLMs) have significantly improved mathematical problem-solving. Among various techniques, paraphrasing problem statements has emerged as a promising strategy to enhance model understanding and accuracy.We define twelve paraphrasing types grounded in mathematics education theory and analyze their impact on LLM performance across different configurations. To automate selection, we propose a Paraphrase Type Selector that predicts effective paraphrases for each problem.Experiments on MATH-500, SVAMP, and AIME shows consistent performance gain from paraphrased problems. On MATH-500 with LLaMA 3.1-8B, combining the original with the best five paraphrased problems improves accuracy by +8.4{\%}, with the selector achieving an additional +1.33{\%} gain."
}