What does fine-tuning a pre-trained language model achieve?

Fine-tuning a pre-trained language model primarily involves adapting it for a specific task by utilizing additional data that is relevant to that task. This process allows the model to leverage the extensive knowledge and language understanding it has gained from training on a large corpus and adjust its parameters to perform better on specific applications, such as sentiment analysis, question answering, or any tailored task.

By fine-tuning, the model can better capture the nuances and specific patterns relevant to the data it is being adapted to, thus improving its performance and accuracy in that particular context. The ability to generalize from a broader dataset and refine skills for specific applications is key in many machine learning tasks.

The other options do not accurately capture the core purpose of fine-tuning. While standardizing outputs, replacing the need for previous data, and enhancing training speed might be goals in certain contexts, they do not encompass the essential aim of fine-tuning, which is focused on task-specific adaptation.