LoRA vs. Fine-Tuning LLMs

LoRA (Low-Rank Adaptation) and fine-tuning are two methods to adapt large language models (LLMs) to specific tasks or domains. LLMs are pre-trained on massive amounts of general domain data, such as GPT-3, RoBERTa, and DeBERTa, and have shown impressive performance on various natural language processing (NLP) tasks.

Why fine tune a LLM?

Fine-tuning of LLMs is the conventional method that retrains all model parameters for a specific task or domain. Fine-tuning a Large Language Model (LLM) is beneficial for several reasons:

Domain Specificity: General-purpose language models are trained on a wide variety of data and are not specialized in any particular domain. Fine-tuning allows you to adapt the model to specific industries, topics, or types of language, such as medical terminology, legal jargon, or technical language.
Improved Accuracy: Fine-tuning on a specific dataset can improve the model’s performance on tasks related to that data. This could mean more accurate classifications, better sentiment analysis, or more relevant generated text.
Resource Efficiency: Fine-tuning only a subset of the model’s parameters can be more computationally efficient than training a new model from scratch. This can be particularly important when computational resources are limited.
Data Privacy: If you have sensitive or proprietary data, fine-tuning a pre-trained model on your own infrastructure allows you to benefit from the capabilities of large language models without sharing your data externally.
Task Adaptation: General-purpose language models are not optimized for specific tasks like question-answering, summarization, or translation. Fine-tuning can adapt the model for these specialized tasks.
Contextual Understanding: Fine-tuning can help the model better understand the context in which it will be used, making it more effective at generating appropriate and useful responses.
Reduced Training Time: Starting with a pre-trained model and fine-tuning it for a specific task can be much faster than training a model from scratch.
Avoid Overfitting: When you have a small dataset, training a large model from scratch can lead to overfitting. Fine-tuning can mitigate this risk, as the model has already learned general language features from a large dataset and only needs to adapt to the specificities of the new data.
Leverage Pre-trained Features: Large language models trained on extensive datasets have already learned a wide array of features, from basic syntax and grammar to high-level semantic understanding. Fine-tuning allows you to leverage these features for your specific application.
Customization: Fine-tuning allows you to tailor the model’s behavior to specific requirements, such as generating text in a particular style, tone, or format.

In summary, fine-tuning a large language model allows you to customize its capabilities for specific tasks, domains, or datasets, improving its performance and making it more applicable to your particular needs.

LoRA

LoRA addresses some of the drawbacks of fine-tuning by freezing the pre-trained model weights and injecting trainable rank decomposition matrices into each layer of the Transformer architecture [1].

In traditional fine-tuning, all the parameters of the pre-trained model are updated during the training process on a new, specific task. Instead of updating the original model parameters, LoRA introduces new, trainable parameters in the form of “rank decomposition matrices.” These matrices are added to each layer of the Transformer architecture, the underlying model structure used in most LLMs.

What is a Rank Decomposition Matrix?

A rank decomposition matrix is essentially a way to approximate a larger matrix using the product of two smaller matrices. Mathematically, if you have a matrix $W$ with dimensions $d \times k$ , you can decompose it into two matrices $B$ and $A$ such that $W = B \cdot A$ . Here, $B$ has dimensions $d \times r$ and $A$ has dimensions $r \times k$ , where $r$ is much smaller than either $d$ or $k$ .

Why is this Useful?

The key advantage of this decomposition is that it significantly reduces the number of trainable parameters. Instead of training a large $d \times k$ matrix, you’re now training two smaller matrices with dimensions $d \times r$ and $r \times k$ . Because $r$ is much smaller than $d$ or $k$ , the total number of parameters in $B$ and $A$ is much less than in $W$ . This makes the training process faster and more memory-efficient, which is particularly beneficial when computational resources are limited.

Application in LoRA

By applying this rank decomposition technique to each layer of the Transformer architecture, LoRA manages to adapt the model to specific tasks with a much smaller computational footprint compared to traditional fine-tuning. This allows for quicker and more efficient adaptation of LLMs to new tasks, without sacrificing much in terms of performance.

LoRA vs Fine-tuning

LoRA is much faster than fine-tuning, as it only updates a small number of parameters rather than all of the parameters in the LLM [2] [3].
LoRA is more efficient than fine-tuning in terms of memory and storage requirements, as it only needs to store the rank decomposition matrices for each task, rather than the entire fine-tuned model [1] [2] [3].
LoRA performs on-par or better than fine-tuning in model quality on various LLMs such as RoBERTa, DeBERTa, GPT-2, and GPT-3, despite having fewer trainable parameters [1] [4].
LoRA introduces no inference latency, as the trainable matrices can be merged with the frozen weights during deployment, unlike adapters that add extra layers to the model [1] [4].
LoRA is an orthogonal method that can be combined with other fine-tuning techniques such as prefix-tuning [1].

The choice of LoRA vs Fine-tuning depends on the task, the data, and the resources available. Some general guidelines are:

Use LoRA when you have limited hardware resources, such as GPU memory or storage space, or when you need to deploy multiple fine-tuned models for different tasks or domains [1] [2] [3].
Use LoRA when you have a large-scale pre-trained model that is over-parametrized for your downstream task, such as GPT-3 175B, and when you can achieve good performance with low-rank matrices [1] [4].
Use fine-tuning when you have sufficient hardware resources, or when you need to optimize all model parameters for your downstream task, such as when you have a small or medium-sized pre-trained model that is under-parametrized for your task [1] [4].
Use fine-tuning when you have a large amount of task-specific data that can benefit from full parameter updates, or when you need to avoid catastrophic forgetting of pre-trained knowledge [1] [4].

In general, LoRA is more suitable for very large LLMs that need to be adapted to multiple tasks or domains with limited data and resources, while fine-tuning is more suitable for smaller or medium-sized LLMs that need to be adapted to specific tasks or domains with sufficient data and resources. However, empirical experiments and evaluations are needed to determine the best method for each case.

References

[1] Hu, Edward J., et al. “LoRA: Low-Rank Adaptation of Large Language Models.” arXiv preprint arXiv:2106.09685 (2021).
[2] Golgoon, Ashkan. “Understanding QLoRA & LoRA: Fine-tuning of LLMs.” Medium (2023).
[3] Accubits. “Unlocking Affordable Brilliance: Fine-Tuning LLMs with LoRA for Maximum Cost-Effectiveness.” Accubits Blog (2023).
[4] TechTalks. “The complete guide to LLM fine-tuning.” TechTalks (2023).