Obtaining human-interpretable explanations of large, general-purpose language models is an urgent goal for AI safety. However, it is just as important that our interpretability methods are faithful to the causal dynamics underlying model behavior and able to robustly generalize to unseen inputs. Distributed Alignment Search (DAS) is a powerful gradient descent method grounded in a theory of causal abstraction that has uncovered perfect alignments between interpretable symbolic algorithms and small deep learning models fine-tuned for specific tasks. In the present paper, we scale DAS significantly by replacing the remaining brute-force search steps with learned parameters -- an approach we call Boundless DAS. This enables us to efficiently search for interpretable causal structure in large language models while they follow instructions. We apply Boundless DAS to the Alpaca model (7B parameters), which, off the shelf, solves a simple numerical reasoning problem. With Boundless DAS, we discover that Alpaca does this by implementing a causal model with two interpretable boolean variables. Furthermore, we find that the alignment of neural representations with these variables is robust to changes in inputs and instructions. These findings mark a first step toward faithfully understanding the inner-workings of our ever-growing and most widely deployed language models. Our tool is extensible to larger LLMs and is released publicly at `https://github.com/stanfordnlp/pyvene`.

翻译：获得大型通用语言模型的人类可解释性解释是AI安全的紧迫目标。然而，我们的可解释性方法同样需要忠实于模型行为背后的因果动态，并能够稳健地泛化到未见输入。基于因果抽象理论的有力梯度下降方法——分布式对齐搜索（DAS），已在可解释符号算法与针对特定任务微调的小型深度学习模型之间发现了完美对齐。在本文中，我们通过用学习参数取代剩余暴力搜索步骤，显著扩展了DAS，这一方法称为无界DAS。这使得我们能够在大型语言模型遵循指令时高效搜索其可解释因果结构。我们将无界DAS应用于Alpaca模型（7B参数），该模型在开箱状态下解决了一个简单数值推理问题。通过无界DAS，我们发现Alpaca通过实现一个包含两个可解释布尔变量的因果模型来执行此任务。此外，我们发现神经表征与这些变量的对齐对输入和指令的变化具有稳健性。这些发现标志着向忠实理解日益增长且广泛部署的语言模型内部机制迈出了第一步。我们的工具可扩展到更大的LLM，并已在`https://github.com/stanfordnlp/pyvene` 公开发布。