Description: A fine-tuned model is a pre-trained model that has been further trained on a specific dataset to adapt it to particular tasks. This fine-tuning process allows the model to leverage the general knowledge acquired during pre-training and tailor it to specific contexts or domains, thereby improving its performance on concrete tasks. Large language models, such as GPT-3 or BERT, are examples of models that can be fine-tuned. This approach is especially valuable in the field of natural language processing (NLP), where variations in language and context can significantly influence text interpretation and generation. Fine-tuning involves modifying the model’s weights through an additional training process, using a dataset that reflects the characteristics and needs of the specific task. This not only optimizes the model’s accuracy but also allows it to adapt to different language styles, jargon, or technical terminologies, resulting in superior performance in applications such as machine translation, sentiment analysis, or domain-specific text generation.
