Towards Anytime Fine-tuning: Continually Pre-trained Language Models with Hypernetwork Prompt
This addresses the challenge of adapting pre-trained models to multiple domains without degrading performance on unseen ones, which is incremental as it builds on existing continual pre-training methods.
The paper tackled the problem of continual pre-training for language models to maintain performance on unseen domains while adapting to new ones, achieving improvements of 3.57% and 3.4% on two real-world datasets.
Continual pre-training has been urgent for adapting a pre-trained model to a multitude of domains and tasks in the fast-evolving world. In practice, a continually pre-trained model is expected to demonstrate not only greater capacity when fine-tuned on pre-trained domains but also a non-decreasing performance on unseen ones. In this work, we first investigate such anytime fine-tuning effectiveness of existing continual pre-training approaches, concluding with unanimously decreased performance on unseen domains. To this end, we propose a prompt-guided continual pre-training method, where we train a hypernetwork to generate domain-specific prompts by both agreement and disagreement losses. The agreement loss maximally preserves the generalization of a pre-trained model to new domains, and the disagreement one guards the exclusiveness of the generated hidden states for each domain. Remarkably, prompts by the hypernetwork alleviate the domain identity when fine-tuning and promote knowledge transfer across domains. Our method achieved improvements of 3.57% and 3.4% on two real-world datasets (including domain shift and temporal shift), respectively, demonstrating its efficacy.