@inproceedings{jo-etal-2025-large,
    title = "Large Language Models Exhibit Limited Reasoning Ability on Coding Problems",
    author = "Jo, Jinyoung  and
      Engelmann, Jonah  and
      Choi, Sean",
    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.13/",
    pages = "139--146",
    ISBN = "979-8-89176-299-2",
    abstract = "Claims that large language models (LLMs) have complex reasoning ability have stirred broad interests, and controversies, of academics and non-academics alike. A popular basis for such claims comes from LLMs' ability to solve coding problems, which involves understanding the problem statement and providing code that solves the problem. Although such abilities are remarkable feats worth praising, we argue that they come from memorization rather than reasoning. We first show that LLMs' problem-solving ability degrades with increased recency of the problem, likely due to the reduced amount of training data for more recent problems, regardless of the problem difficulty labeled by human experts. Additionally, we show that an LLM often fails to solve the problem when presented with reworded but equivalent problem statements, further suggesting their limited reasoning ability."
}