Reasoning as Pattern Matching: Shared Mechanisms in Human and LLM Reasoning (arxiv.org)

Recent research highlights intriguing parallels between human reasoning and the capabilities of large language models (LLMs), challenging the perception that LLMs merely engage in pattern matching as opposed to genuine reasoning. The study involved evaluating both human participants and 25 LLMs on common-sense reasoning tasks, revealing that both groups exhibited similar error patterns. This suggests that the failures seen in LLMs are not unique to artificial intelligence but are also reflected in human reasoning, pointing to shared underlying mechanisms. Significantly, the researchers identified specific attention heads within LLMs that influence their reasoning processes, effectively implementing a form of pattern matching. This insight not only sheds light on the nature of reasoning across both humans and LLMs but also raises questions about the reliance on abstract world models traditionally attributed to human thought. By drawing these connections, the study advocates for a reevaluation of how reasoning is understood in both AI and cognitive science, emphasizing that everyday causal reasoning may be less about abstract principles and more rooted in pattern-based processing.