Regularized Replay Improves Fine-Tuning of Large Language Models
Analysis
This paper addresses the issue of catastrophic forgetting during fine-tuning of large language models (LLMs) using parameter-efficient methods like LoRA. It highlights that naive fine-tuning can degrade model capabilities, even with small datasets. The core contribution is a regularized approximate replay approach that mitigates this problem by penalizing divergence from the initial model and incorporating data from a similar corpus. This is important because it offers a practical solution to a common problem in LLM fine-tuning, allowing for more effective adaptation to new tasks without losing existing knowledge.
Key Takeaways
- •Naive LoRA-based fine-tuning can lead to catastrophic forgetting.
- •Regularized approximate replay, penalizing KL divergence and incorporating data from a similar corpus, effectively mitigates this.
- •This approach preserves general knowledge while allowing for plasticity to new tasks.
- •The method adds only a modest amount of computational overhead.
Reference / Citation
View Original"The paper demonstrates that small tweaks to the training procedure with very little overhead can virtually eliminate the problem of catastrophic forgetting."