Prefix Tuning vs. Fine-Tuning and other PEFT methods

Pre-trained language models (LLMs) need to be fine-tuned to turn them into models capable of performing specific tasks. However, this is a rather labor-intensive and time-consuming process that requires significant cloud storage capacities. As a result, data scientists have developed new methods such as prompt tuning and prefix tuning.

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What is Prefix-tuning?

Prefix tuning is a type of prompt tuning. The term "prefix" refers to a trainable module added to each transformer layer of the pre-trained large language model, consisting of sequences of continuous task-specific training vectors. This prefix is optimized during training to adapt the model to the concrete task requirements while the pre-trained model parameters stay the same.

These vectors used in the prefix module are not natural language words or sentences but abstract representations. They encode task-specific information in a format that can be effectively utilized by the model during generation.

Prefix-tuning is designed to be efficient, introducing a limited amount of trainable parameters compared to full fine-tuning methods. This helps reduce computational and storage costs, making it feasible to fine-tune a large language model (LLM) even with limited resources.

No matter which tuning method you choose for your model, the quality of your data remains paramount. Toloka excels in delivering top-tier data for LLMs. Reach out to us for high-quality data tailored to your domain-specific Large Language Model (LLM).

Prefix-tuning vs. LoRA

LoRA, which stands for Low-Rank Adaptation of Large Language Models, is a widely used lightweight training technique in the realm of machine learning. Its primary purpose is to drastically decrease the number of trainable parameters within a model. This is achieved by introducing a smaller set of new weights into the model, which are the only ones subjected to upgrading.

LoRA uses lower-rank matrices to make training models fast and efficient. Usually, large language models have tons of numbers stored in them. Storing all these numbers also called parameters or weights needs a huge amount of space.

When data scientists fine-tune a model, they tweak every single one of these numbers. But that's a lot of work and needs a ton of computing power, which can be extremely expensive. Instead of updating all of these numbers, LoRA only focuses on a smaller group of them, called lower-rank matrices. By doing this, LoRA slashes the amount of computing and memory needed.

The combination of LoRA and prefix-tuning results in a highly productive fine-tuning process. LoRA reduces the overall number of parameters, while prefix-tuning provides task-specific control, making the fine-tuning process computationally cheaper and more resource-efficient.

Prefix-funing vs. Prompt-tuning

Prompt-tuning employs soft prompts that are generated by AI. Such soft prompts consist of an embedding or a string of numbers that are unrecognizable for a human but are comprehensible for a large language model. They can be a substitute for additional training data and guide the model towards the desired output.

Prompt-tuning involves only fine-tuning the input prompt embeddings, resulting in fewer parameters being updated. This approach may be more parameter-efficient, requiring less computational resources for fine-tuning. While more productive, soft prompt-tuning may have limitations in adapting the model to complex tasks that require adjustments across multiple layers.

Prefix-tuning modifies more layers of the model by inserting task-specific prefixes, requiring more parameters to be fine-tuned. This may offer more flexibility in adapting the model to the target task but could lead to higher computational costs.

Difference Between Prefix-tuning and Fine-tuning

Prefix-tuning and fine-tuning are both methods used to adapt pre-trained language models to concrete tasks or domains, but they differ in their approach and scope of adjustment. Fine-tuning involves adjusting the entire model's parameters, often by training on task-specific data. During fine-tuning, the large language model learns to adapt its weights to the target task by updating weights across all layers of the network.

In prefix-tuning, task-specific information is incorporated by adding prefixes to the input data, more specifically to the transformer blocks of the large language models. These prefixes are composed of task-specific vectors added to the beginning of the input sequence.

Only the prefix parameters are updated during training, which incredibly speeds up the process. That way prefix-tuning tends to be more computationally efficient compared to fine-tuning because it focuses on adjusting a smaller subset of parameters in the prefix module rather than updating the entire model.

Fine-tuning is commonly used when extensive adaptation to the target task is required and when resources allow for training the entire model on specialized data. At the same time, prefix-tuning allows for more targeted adjustments to the model's behavior for the specific task without extensively modifying the model's parameters.

Benefits and Applications of Prefix-Tuning

Prefix-tuning plays a significant role in natural language processing (NLP). This is no coincidence, as huge language models require significant resources to work with, and prefix-tuning can make dealing with them much easier due to several advantages of this pre-trained model tuning method:

• Effective Parameter Usage. Compared to fine-tuning the entire model, prefix-tuning modifies only a subset of parameters (the prefix module), making it more efficient regarding computational resources. This efficiency is beneficial when computational resources are limited or when deploying models in resource-constrained environments;

• Improved Performance. Studies have shown that prefix-tuning can lead to improved performance compared to traditional fine-tuning approaches, especially in low-resource settings or when dealing with tasks requiring precise control over model behavior;

• Reduces Training Time and Data Requirements. Since prefix-tuning focuses on adjusting a smaller subset of parameters, it typically requires less time to train and data compared to fine-tuning the entire model.

Conclusion

All of the previously mentioned methods such as prefix-tuning, prompt-tuning, and LoRA are essentially simplified versions of traditional fine-tuning. The entire set can be categorized as parameter-efficient fine-tuning (PEFT) methods. By simplifying and optimizing the fine-tuning process, these approaches make more productive use of computing resources and reduce the need for large amounts of data and computing power.

One of their key advantages lies in their efficient parameter usage. In prefix-tuning only a subset of parameters is modified while preserving the knowledge encoded in pre-trained representations. It’s particularly well-suited for scenarios with limited data or computational resources. Its versatility allows it to be applied to a wide range of NLP tasks, from text generation and classification to summarization and beyond.

Read more about different methods of model tuning:

• Supervised Fine-Tuning: How to Customize Your LLM?

• Advantages of PEFT for your LLM application

• LoRA AI models: Low-Rank Adaptation for a More Efficient Fine Tuning