Despite the recent success of artificial neural networks on a variety of
tasks, we have little knowledge or control over the exact solutions these
models implement. Instilling inductive biases -- preferences for some solutions
over others -- into these models is one promising path toward understanding and
controlling their behavior. Much work has been done to study the inherent
inductive biases of models and instill different inductive biases through
hand-designed architectures or carefully curated training regimens. In this
work, we explore a more mechanistic approach: Subtask Induction. Our method
discovers a functional subnetwork that implements a particular subtask within a
trained model and uses it to instill inductive biases towards solutions
utilizing that subtask. Subtask Induction is flexible and efficient, and we
demonstrate its effectiveness with two experiments. First, we show that Subtask
Induction significantly reduces the amount of training data required for a
model to adopt a specific, generalizable solution to a modular arithmetic task.
Second, we demonstrate that Subtask Induction successfully induces a human-like
shape bias while increasing data efficiency for convolutional and
transformer-based image classification models.

通过机械式子任务诱导方法，在训练模型中实现特定的子任务以引入归纳偏差，以减少训练数据量并成功诱导出人类类似的形状偏差。