  Fine-tuning has been proven to be a simple and effective technique to
transfer the learned knowledge of Pre-trained Language Models (PLMs) to
downstream tasks. However, vanilla fine-tuning easily overfits the target data
and degrades the generalization ability. Most existing studies attribute it to
catastrophic forgetting, and they retain the pre-trained knowledge
indiscriminately without identifying what knowledge is transferable. Motivated
by this, we frame fine-tuning into a causal graph and discover that the crux of
catastrophic forgetting lies in the missing causal effects from the pretrained
data. Based on the causal view, we propose a unified objective for fine-tuning
to retrieve the causality back. Intriguingly, the unified objective can be seen
as the sum of the vanilla fine-tuning objective, which learns new knowledge
from target data, and the causal objective, which preserves old knowledge from
PLMs. Therefore, our method is flexible and can mitigate negative transfer
while preserving knowledge. Since endowing models with commonsense is a
long-standing challenge, we implement our method on commonsense QA with a
proposed heuristic estimation to verify its effectiveness. In the experiments,
our method outperforms state-of-the-art fine-tuning methods on all six
commonsense QA datasets and can be implemented as a plug-in module to inflate
the performance of existing QA models.
