Controlled Low-Rank Adaptation with Subspace Regularization for Continued Training on Large Language Models

ACL ARR 2025 February Submission8393 Authors

Abstract: Large language models (LLMs) exhibit remarkable capabilities in natural language processing but face catastrophic forgetting when learning new tasks, where adaptation to a new domain leads to a substantial decline in performance on previous tasks. In this paper, we propose Controlled LoRA (CLoRA), a subspace regularization method on LoRA structure. Aiming to reduce the scale of output change while introduce minimal constraint on model capacity, CLoRA imposes constraint on the direction of updating matrix’s null space. Experimental results on one-stage LLM finetuning tasks and continual learning settings highlight the superority of CLoRA as a effective parameter-efficient finetuning method with catastrophic forgetting mitigating. Further investigation for model parameters indicates that CLoRA effectively balances the trade-off between model capacity and degree of forgetting.

Paper Type: Long

Research Area: Efficient/Low-Resource Methods for NLP

Research Area Keywords: parameter-efficient-training

Contribution Types: NLP engineering experiment, Approaches low compute settings-efficiency

Languages Studied: english

Submission Number: 8393