With the development of deep learning, large model fine-tuning (finetune) has become a commonly used method, which can make pre-trained models show better performance on specific tasks. This article will focus on some common methods in large model fine-tuning, including LoRA, Adapter, Prefix-tuning, P-tuning and Prompt-tuning.
LoRA LoRA is a new fine-tuning method designed to leverage pre-trained language models (PTLM) for transfer learning to adapt to various natural language processing (NLP) tasks. The core idea of this method is to embed an adaptive language representation (ALR) module in PTLM, which can learn task-specific language representation and combine it with the general language representation in PTLM. In this way, LoRA can improve the performance of PTLM on specific tasks without losing its generality.
Adapter Adapter is a fine-tuning method that adapts to new tasks by adding some parameters to the pre-trained model. These parameters can be viewed as an "adapter" for adjusting the pre-trained model to new tasks. The Adapter method can fine-tune the model so that it can adapt to new tasks without modifying the original model parameters.
Prefix-tuning Prefix-tuning is a method based on pre-trained models that adapts to new tasks by adding some specific prefixes to the input sequence of the model. These prefixes can be task-specific lexical, grammatical, or semantic information that is used to guide the model to better handle new tasks. Prefix-tuning can improve the performance of the model on specific tasks without losing the knowledge of the pre-trained model.
P-tuning P-tuning is a fine-tuning method that adapts to new tasks by reparameterizing a pre-trained model. Specifically, P-tuning replaces the original model’s parameter matrix with a new parameter matrix that can be trained on task-specific data. This method can improve the performance of the model on specific tasks without losing the knowledge of the original model.
Prompt-tuning Prompt-tuning is a fine-tuning method based on pre-trained models that adapts to new tasks by using prompts. These cues can be task-specific lexical, grammatical, or semantic information that guides the model to better handle new tasks. Prompt-tuning can improve the performance of the model on specific tasks without losing the knowledge of the pre-trained model. At the same time, prompt-tuning can also be used as a meta-learning method to learn how to quickly adapt to new tasks.
Summary Fine-tuning of large models is a commonly used method to make pre-trained models perform better on specific tasks. This article introduces common fine-tuning methods such as LoRA, Adapter, Prefix-tuning, P-tuning and Prompt-tuning. These methods can improve the performance of the model on specific tasks without losing the knowledge of the pre-trained model. At the same time, these methods also have different advantages, disadvantages and scope of application, and it is necessary to choose the appropriate method according to the specific application scenario.
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