Are Language Models Efficient Reasoners? A Perspective from Logic Programming
accuracy declineaxiomsdeductive reasoningdomain-consistent distractionsempirical evaluationinference alignmentirrelevant informationlanguage modelslogic programmingmath word problemsmodel assessmentnatural language proofsproof detoursreasoning frameworksemantic overlap
Modern language models (LMs) exhibit strong deductive reasoning capabilities, yet standard evaluations emphasize correctness while overlooking a key aspect of human-like reasoning: *efficiency*. In real-world reasoning scenarios, much of the available information is irrelevant, and effective deductive inference requires identifying and ignoring such distractions. We propose a framework for assessing LM reasoning efficiency through the lens of logic programming, introducing a simple method to align proofs written in natural language