Do Language Models Follow Occam's Razor? An Evaluation of Parsimony in Inductive and Abductive Reasoning

The paper investigates whether large language models (LLMs) follow Occam's Razor when performing inductive and abductive reasoning. It introduces a synthetic framework for generating questions that require both types of reasoning and a new automated metric to evaluate the simplicity and correctness of generated hypotheses. Experiments show that while LLMs can handle simple scenarios, they struggle with complex world models and producing high‑quality, simplest hypotheses, even when using advanced reasoning techniques.

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• llms
• agents
• benchmarks