Analyzing and Reducing Catastrophic Forgetting in Parameter Efficient Tuning

arXiv:2402.18865v1 Announce Type: new
Abstract: Existing research has shown that large language models (LLMs) exhibit remarkable performance in language understanding and generation. However, when LLMs are continuously fine-tuned on complex and diverse domain-specific downstream tasks, the inference performance on historical tasks decreases dramatically, which is known as a catastrophic forgetting problem. A trade-off needs to be kept between learning plasticity and memory stability. Plenty of existing works have explored strategies like memory replay, regularization and parameter isolation, but little is …

arxiv catastrophic forgetting cs.ai cs.cl cs.lg type