CURLoRA: Stable LLM Continual Fine-Tuning and Catastrophic Forgetting
  Mitigation
Authors: name: Muhammad Fawi

This paper introduces CURLoRA, a novel approach to fine-tuning large language
models (LLMs) that leverages CUR matrix decomposition in the context of
Low-Rank Adaptation (LoRA). Our method addresses two critical challenges in LLM
fine-tuning: mitigating catastrophic forgetting during continual learning and
reducing the number of trainable parameters. We propose a unique modification
to the CUR decomposition process, utilizing inverted probabilities for column
and row selection which acts as an implicit regularization, and initializing
the U matrix as a zero matrix, and only fine-tuning it. We demonstrate
through experiments on multiple datasets that CURLoRA outperforms standard LoRA
in mitigating catastrophic forgetting. It maintains model stability and
performance across tasks while significantly reducing the number of trainable
parameters. Our results show that CURLoRA achieves very good and stable task
accuracy while maintaining base model's perplexity scores fixed compared to
LoRA upon continual fine-tuning, particularly in scenarios with limited data.