Puzzle Solving using Reasoning of Large Language Models: A Survey
<|reference_start|>Puzzle Solving using Reasoning of Large Language Models: A Survey: Exploring the capabilities of Large Language Models (LLMs) in puzzle solving unveils critical insights into their potential and challenges in AI, marking a significant step towards understanding their applicability in complex reasoning tasks. This survey leverages a unique taxonomy -- dividing puzzles into rule-based and rule-less categories -- to critically assess LLMs through various methodologies, including prompting techniques, neuro-symbolic approaches, and fine-tuning. Through a critical review of relevant datasets and benchmarks, we assess LLMs' performance, identifying significant challenges in complex puzzle scenarios. Our findings highlight the disparity between LLM capabilities and human-like reasoning, particularly in those requiring advanced logical inference. The survey underscores the necessity for novel strategies and richer datasets to advance LLMs' puzzle-solving proficiency and contribute to AI's logical reasoning and creative problem-solving advancements.<|reference_end|>
@article{giadikiaroglou2024puzzle,
    title={Puzzle Solving using Reasoning of Large Language Models: A Survey},
    author={Panagiotis Giadikiaroglou, Maria Lymperaiou, Giorgos Filandrianos,
  Giorgos Stamou},
    journal={arXiv preprint arXiv:2402.11291},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.11291},
    primaryClass={cs.CL cs.AI}
}