We analyze the storage and recall of factual associations in autoregressive transformer language models, finding evidence that these associations correspond to localized, directly-editable computations. We first develop a causal intervention for identifying neuron activations that are decisive in a model's factual predictions. This reveals a distinct set of steps in middle-layer feed-forward modules that mediate factual predictions while processing subject tokens. To test our hypothesis that these computations correspond to factual association recall, we modify feed-forward weights to update specific factual associations using Rank-One Model Editing (ROME). We find that ROME is effective on a standard zero-shot relation extraction (zsRE) model-editing task, comparable to existing methods. To perform a more sensitive evaluation, we also evaluate ROME on a new dataset of counterfactual assertions, on which it simultaneously maintains both specificity and generalization, whereas other methods sacrifice one or another. Our results confirm an important role for mid-layer feed-forward modules in storing factual associations and suggest that direct manipulation of computational mechanisms may be a feasible approach for model editing. The code, dataset, visualizations, and an interactive demo notebook are available at https://rome.baulab.info/

翻译：我们分析自动递减变压器语言模型中事实关联的存储和回顾情况,找到这些关联符合本地、直接编辑计算的证据。我们首先开发因果干预,以识别在模型事实预测中具有决定性意义的神经激活。这揭示了中层进化前方向模块中一套截然不同的步骤,在其中对事实预测进行调解,同时处理主题符号。为了检验我们的假设,即这些计算与事实关联相对应,我们修改进向前加权,以利用标准“一号模型编辑”更新具体的事实关联。我们发现,ROME在标准零点关系提取(zRE)模型编辑任务上是有效的,可以与现有方法相比。为了进行更敏感的评估,我们还评估了一套反事实指控的新数据集的ROME,同时保持了特性和概括性,而其他方法则牺牲了一种或另一种。我们的结果证实了中层进向前进模块在存储事实关联中的重要作用,并建议直接调整计算机制可能是模式编辑的一种可行方法。代码、数据设置、可视像化和互动的演示式,在 https_drobredudealations。