Year: 2024
Type: conference-paper
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.
Institution: National Technical University of Athens
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4404782713