人类级推理：大型语言模型在逻辑与抽象推理上的比较研究
Human-Level Reasoning: A Comparative Study of Large Language Models on Logical and Abstract Reasoning
AI总结：
本文通过八个定制推理问题比较了多个LLM在逻辑和抽象推理能力上的表现，揭示了模型在推理任务上的差异及不足。
摘要：
评估大型语言模型（LLMs）的推理能力对于推动人工智能发展至关重要，因为它超越了单纯的语言任务表现。它涉及理解这些模型是否真正理解信息、能否进行推理并以逻辑有效的方式得出结论。本研究通过一组八个定制设计的推理问题，比较了多个LLM在逻辑和抽象推理能力上的表现，包括GPT、Claude、DeepSeek、Gemini、Grok、Llama、Mistral、Perplexity和Sabiá。LLM的结果与人类在相同任务上的表现进行基准测试，揭示了显著差异，并指出了LLM在推理方面存在的不足。
英文摘要：
Evaluating reasoning ability in Large Language Models (LLMs) is important for advancing artificial intelligence, as it transcends mere linguistic task performance. It involves understanding whether these models truly understand information, perform inferences, and are able to draw conclusions in a logical and valid way. This study compare logical and abstract reasoning skills of several LLMs - including GPT, Claude, DeepSeek, Gemini, Grok, Llama, Mistral, Perplexity, and Sabiá - using a set of eight custom-designed reasoning questions. The LLM results are benchmarked against human performance on the same tasks, revealing significant differences and indicating areas where LLMs struggle with deduction.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
