@inproceedings{mouselinos-etal-2024-beyond,
    title = "Beyond Lines and Circles: Unveiling the Geometric Reasoning Gap in Large Language Models",
    author = "Mouselinos, Spyridon  and
      Michalewski, Henryk  and
      Malinowski, Mateusz",
    editor = "Al-Onaizan, Yaser  and
      Bansal, Mohit  and
      Chen, Yun-Nung",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.findings-emnlp.360/",
    doi = "10.18653/v1/2024.findings-emnlp.360",
    pages = "6192--6222",
    abstract = "Large Language Models (LLMs) demonstrate ever-increasing abilities in mathematical and algorithmic tasks, yet their geometric reasoning skills are underexplored. We investigate LLMs' abilities in constructive geometric problem-solving, {--} one of the most fundamental steps in developing human mathematical reasoning, revealing notable challenges in this domain. LLMs exhibit biases in variable names, struggle with 2D spatial relationships and planning, and hallucinate object placements. To this end, we introduce a framework that enhances LLMs' reasoning potential through a multi-agent system conducting internal dialogue. This work underscores LLMs' limitations in geometric reasoning and improves their capabilities through self-correction, collaboration, and diverse role specializations."
}