  Changing how pre-trained models behave -- e.g., improving their performance
on a downstream task or mitigating biases learned during pre-training -- is a
common practice when developing machine learning systems. In this work, we
propose a new paradigm for steering the behavior of neural networks, centered
around \textit{task vectors}. A task vector specifies a direction in the weight
space of a pre-trained model, such that movement in that direction improves
performance on the task. We build task vectors by subtracting the weights of a
pre-trained model from the weights of the same model after fine-tuning on a
task. We show that these task vectors can be modified and combined together
through arithmetic operations such as negation and addition, and the behavior
of the resulting model is steered accordingly. Negating a task vector decreases
performance on the target task, with little change in model behavior on control
tasks. Moreover, adding task vectors together can improve performance on
multiple tasks at once. Finally, when tasks are linked by an analogy
relationship of the form ``A is to B as C is to D", combining task vectors from
three of the tasks can improve performance on the fourth, even when no data
from the fourth task is used for training. Overall, our experiments with
several models, modalities and tasks show that task arithmetic is a simple,
efficient and effective way of editing models.
