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

Researchers evaluated whether large language models follow Occam's Razor principle when performing inductive and abductive reasoning, finding that while LLMs can handle simple scenarios, they struggle with complex world models and producing high-quality, simplified hypotheses. The study introduces a new framework for generating reasoning questions and an automated metric to assess hypothesis quality based on correctness and simplicity.

• →LLMs can perform basic inductive and abductive reasoning in simple scenarios but struggle with complex world models.
• →Current LLMs fail to consistently follow Occam's Razor principle of preferring simpler explanations.
• →Popular reasoning-enhancement techniques like in-context learning and RLVR show limited effectiveness for complex reasoning tasks.
• →Researchers developed a new framework to synthetically generate reasoning questions expressible in first-order logic.
• →A new automated metric was proposed to quantitatively assess whether AI-generated hypotheses adhere to Occam's Razor.