Evaluating Low-Rank Adaptation in Model Training

Low-Rank Adaptation, or LoRA, is a method used to finetune large language models (LLMs) while saving memory. This method only trains a small number of additional parts, called adapters, instead of changing the entire model. This can help in various tasks like programming and math. However, recent studies show that while LoRA can save memory, it often does not perform as well as full finetuning.

In this article, we will look at how LoRA compares to full finetuning in different tasks. We will also explore how well LoRA maintains performance on tasks outside of the target domain.

#Memory Efficiency in Finetuning

Finetuning large models can be very demanding on computer memory. The traditional way involves adjusting the whole model, which can take a lot of resources. In contrast, LoRA focuses on a few adjustments, which makes it lighter in memory use. By only changing certain components, LoRA allows for efficient training, using less memory compared to full finetuning.

#Comparing Performance in Programming and Math

We conducted tests to see how LoRA performs against full finetuning in two main areas: programming and mathematics. For our tests, we used two types of training data: instruction finetuning (IFT) and continued pretraining (CPT). IFT uses many question-answer pairs, while CPT focuses on large amounts of unstructured data.

Our findings show that LoRA often does not perform as well as full finetuning. In programming tasks, the gap in performance was noticeable. However, in math tasks, LoRA's results were closer to those of full finetuning.

#The Role of Regularization

LoRA has been noted for its ability to maintain the performance of the base model on unrelated tasks. This is referred to as regularization. Regularization is important because it prevents the model from forgetting what it learned before it was adapted to a new task.

In our study, we found that LoRA provides a form of regularization that is stronger than other common methods. For example, it performs better than techniques like weight decay and dropout, which are used to control overfitting.

#Learning and Forgetting Effects

When finetuning models, there is often a tradeoff between learning new tasks and retaining previous knowledge, known as the learning-forgetting tradeoff. In our tests, we observed that while LoRA learns less for new tasks, it also forgets less about earlier tasks.

This indicates that while LoRA might be less effective for learning new information, it does a better job at preserving knowledge from earlier training.

#Sensitivity to Hyperparameters

The performance of both LoRA and full finetuning is greatly influenced by hyperparameters, which are settings used to control the training process. For LoRA, we found it to be more sensitive to the choice of learning rate and which parts of the model are targeted for finetuning.

For our study, we discovered that carefully selecting these hyperparameters could lead to better results with LoRA, although it still struggled against full finetuning.

#Practical Recommendations for Using LoRA

From our findings, we recommend using LoRA mainly for instruction finetuning instead of continued pretraining. It's essential to choose the right learning rate, target all modules, and keep the rank low to achieve a good balance between performance and memory use. Training for at least four epochs tends to yield beneficial results.

#Conclusion

LoRA offers memory efficiency and prevents forgetting, making it a valuable tool for training large models, especially when memory is a concern. However, full finetuning still outperforms LoRA in many tasks, particularly in programming. Understanding the tradeoffs, effectiveness, and best practices for using LoRA can help in making informed decisions in the realm of model training. As model sizes continue to grow, understanding these methods will become increasingly important for researchers and developers alike.