With the continuous development of deep learning technology, large-scale pre-trained models have become an important tool for many tasks. However, finetuning these large models to fit specific tasks is a complex and computationally intensive process. This article will focus on five different fine-tuning methods: LoRA, Adapter, Prefix-tuning, P-tuning and Prompt-tuning, and summarize them.
LoRA (Learned Representations for Finetuning) LoRA is a new fine-tuning method that aims to solve two major problems existing in the fine-tuning process of pre-trained models: over-reliance on the initial model during the model adjustment process and over-fitting problems during the fine-tuning process. . LoRA works by introducing an additional linear layer in the pre-trained model and fine-tuning this linear layer using task-specific training data. This approach enables the model to be better adapted to specific tasks while reducing overreliance on the initial model.
Adapter Adapter is a simple yet effective fine-tuning method that adapts to a specific task by adding a learnable additional layer on top of a specific layer of a pre-trained model. This additional layer can be a linear layer, a non-linear layer, or another type of layer whose purpose is to fine-tune the output of the pre-trained model to better suit the specific task. Adapters have low computational cost and good performance, making them ideal for working with small data sets.
Prefix-tuning The Prefix-tuning method works by fine-tuning a specific part of a pre-trained model (called a "prefix") to fit a specific task. This approach only fine-tunes the prefixes rather than the entire model, thereby reducing computational cost and risk of overfitting. The performance of prefix-tuning is generally better than traditional fine-tuning methods, but not as good as full model fine-tuning.
P-tuning P-tuning is an improved fine-tuning method that adjusts the weights of the pre-trained model by introducing a parametric transformation matrix. This matrix can learn to change the weight distribution of the pre-trained model to better adapt it to the specific task. P-tuning reduces over-reliance on the initial model during fine-tuning while maintaining good performance.
Prompt-tuning Prompt-tuning is a novel fine-tuning method that utilizes prompting technology in the field of natural language processing in recent years. This method adapts the input of the pre-trained model to a specific task so that it takes into account the specific needs of the task at the input stage. Prompt-tuning can significantly improve model performance while reducing over-reliance on the initial model and the risk of over-fitting.
Summary: These five fine-tuning methods all have their own advantages and applicable scenarios in handling large pre-trained models to adapt to specific tasks. LoRA reduces over-reliance on the initial model and over-fitting problems by introducing additional linear layers; Adapter has lower computational cost and better performance, and is suitable for small data sets; Prefix-tuning only fine-tunes the prefix of the pre-trained model , reducing computational costs and the risk of over-fitting; P-tuning adjusts the weight of the pre-training model by introducing a parametric transformation matrix, reducing over-reliance; Prompt-tuning uses prompting technology to modify the input of the pre-training model, significantly improving performance and reduce the risk of overdependence and overfitting. In practical applications, appropriate fine-tuning methods should be selected based on specific tasks and data sets.
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