LLMs Fail at Set, Reasoning Models Triumph
2025-02-19
An experiment tested the reasoning capabilities of Large Language Models (LLMs) in the card game Set. Set requires identifying sets of three cards from a layout of twelve, based on specific rules regarding shape, color, number, and shading. LLMs like GPT-4o, Sonnet-3.5, and Mistral failed to consistently identify correct sets, often suggesting invalid combinations or claiming no sets existed. However, newer reasoning models, DeepThink-R1 and o3-mini, successfully solved the problem, demonstrating superior logical reasoning abilities. This highlights a limitation of LLMs in complex logical tasks, even while excelling at natural language processing, while specialized reasoning models show a clear advantage.
AI
Set Game