Puzzle Solving using Reasoning of Large Language Models: A Survey

• URL: http://arxiv.org/abs/2402.11291v3
• Date: Sat, 14 Sep 2024 06:12:36 GMT
• Title: Puzzle Solving using Reasoning of Large Language Models: A Survey
• Authors: Panagiotis Giadikiaroglou, Maria Lymperaiou, Giorgos Filandrianos, Giorgos Stamou,
• Abstract summary: This survey examines the capabilities of Large Language Models (LLMs) in puzzle solving. Our findings highlight the disparity between LLM capabilities and human-like reasoning. The survey underscores the necessity for novel strategies and richer datasets to advance LLMs' puzzle-solving proficiency.
• Score: 1.9939549451457024
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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