  Pretrained language models (PLMs) trained on large-scale unlabeled corpus are
typically fine-tuned on task-specific downstream datasets, which have produced
state-of-the-art results on various NLP tasks. However, the data discrepancy
issue in domain and scale makes fine-tuning fail to efficiently capture
task-specific patterns, especially in the low data regime. To address this
issue, we propose Task-guided Disentangled Tuning (TDT) for PLMs, which
enhances the generalization of representations by disentangling task-relevant
signals from the entangled representations. For a given task, we introduce a
learnable confidence model to detect indicative guidance from context, and
further propose a disentangled regularization to mitigate the over-reliance
problem. Experimental results on GLUE and CLUE benchmarks show that TDT gives
consistently better results than fine-tuning with different PLMs, and extensive
analysis demonstrates the effectiveness and robustness of our method. Code is
available at https://github.com/lemon0830/TDT.
