用哲学中的因果图测试大模型，发现它们能准确判断复杂因果关系。
Causes in neuron diagrams, and testing causal reasoning in Large Language Models. A glimpse of the future of philosophy?
- 基于哲学因果理论设计因果推理测试
- ChatGPT等模型在争议性案例中正确识别原因
- 展示人机协作推动未来哲学研究的可能
我们提出一种基于因果哲学（特别是D. Lewis推广的神经元图）的抽象因果推理测试方法，应用于ChatGPT、DeepSeek和Gemini等先进大语言模型。令人惊讶的是，这些聊天机器人在文献中存在激烈争论的案例中，已能正确识别因果关系。为评估模型结果及未来专用AI的表现，我们提出了一个比现有定义更具普适性的神经元图中“原因”定义，挑战了学界普遍认为此类定义难以确立的观点。我们认为，这些成果预示着未来哲学研究的发展方向：人类与人工智能专家之间的协同演化。
原文摘要 · Abstract (English)
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.
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