Introduction to Large Language Models
Fine-tuning language models is a process of adapting pre-trained language models to a specific task or domain. Pre-trained models are trained on large-scale datasets such as Wikipedia, and they have learned a lot about the structure of language. Fine-tuning is a transfer learning technique that uses a pre-trained model as a starting point and adapts it to a new task by training it on a smaller dataset. Fine-tuning has become a popular method in natural language processing due to its ability to achieve state-of-the-art performance with less data and training time.
Fine-tuning involves two steps. In the first step, the pre-trained model is frozen, and only the last layer is replaced with a new layer that is randomly initialized. The new layer is then trained on the task-specific dataset using backpropagation. The rest of the model remains unchanged during this process. In the second step, the entire model is fine-tuned on the task-specific dataset. This process updates all the weights in the model, including the pre-trained weights.
Fine-tuning can be used for a wide range of natural language processing tasks, such as:
For example, a pre-trained language model such as BERT can be fine-tuned on a dataset of product reviews to perform sentiment analysis. The resulting model can then be used to classify the sentiment of new product reviews.
Fine-tuning requires less data and training time than training a model from scratch, but it requires careful hyperparameter tuning and can be prone to overfitting. It is also important to choose a pre-trained model that is suitable for the task at hand. For example, a model that is pre-trained on a news corpus may not perform as well on a dataset of tweets due to differences in language use and style.
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