Breaking the Chain: Simple Word Swaps Expose LLMs’ Reasoning Limits
Key Findings: Large Language Models (LLMs) exhibit significant limitations in handling sequentially dependent operations. Our simple word-swap experiment reveals that most models struggle to perform correctly beyond two consecutive word swap operations, highlighting a critical weakness in their sequential reasoning.
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