神经元图中的因果关系，以及大语言模型中的因果推理测试——哲学未来的一瞥？
Causes in neuron diagrams, and testing causal reasoning in Large Language Models. A glimpse of the future of philosophy?
AI总结：
本研究基于因果哲学的神经元图提出AI抽象因果推理测试，在ChatGPT等大模型上验证其可正确识别学界热议案例的因果，还提出适用范围更广的神经元图因果定义，展现人机专业知识互动的哲学研究新方向。
摘要：
我们基于因果哲学领域的学术研究，尤其是D. Lewis推广的神经元图，提出了一种针对AI抽象因果推理能力的测试方法。我们在先进大语言模型（ChatGPT、DeepSeek和Gemini）上开展了该测试。值得注意的是，这些聊天机器人已能在学界热议的案例中正确识别因果关系。为评估这些大语言模型及未来专用AI的测试结果，我们提出了一个神经元图中的因果定义，其适用范围比现有已发表的定义更广，对“这类定义难以得出”的普遍观点构成挑战。我们认为，这些结果展现了未来哲学研究的可能发展方向：即人类与人工智能专业知识的互动协作。
英文摘要：
We propose a test for abstract causal reasoning in AI, based on scholarship in the philosophy of causation, in particular on the neuron diagrams popularized by D. Lewis. We illustrate the test on advanced Large Language Models (ChatGPT, DeepSeek and Gemini). Remarkably, these chatbots are already capable of correctly identifying causes in cases that are hotly debated in the literature. In order to assess the results of these LLMs and future dedicated AI, we propose a definition of cause in neuron diagrams with a wider validity than published hitherto, which challenges the widespread view that such a definition is elusive. We submit that these results are an illustration of how future philosophical research might evolve: as an interplay between human and artificial expertise.
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。
