Large Language Models (LLMs) have revolutionized the natural language processing by excelling in tasks such as text generation, translation, summarization and question answering. Despite their impressive capabilities, these models may not always be suitable for specific tasks or domains due to compatibility issues. Fine tuning allows the users to customize pre-trained language models for specialized tasks.
Data requirements for fine-tuning
Before fine-tuning an LLM, it is essential to understand data requirements to support training and validation.
Here are some guidelines:
Use a large dataset: The required size for a training and validation dataset depends on the complexity of the task and the model being fine-tuned. Larger models learn more with less data as illustrated in the examples in the figure below.
Use a representative dataset: The contents and format of the fine-tuning dataset should be representative of the data on which the model will be used. For example, if you are fine-tuning a model for sentiment analysis, you want to have data from different sources, genres, and domains. This data should also reflect the diversity and nuances of human emotions.
Use a sufficiently specified dataset: The dataset should contain enough information in the input to generate what you want to see in the output. For example, if you are fine-tuning a model for email generation, you want to provide clear and specific prompts that guide the model’s creativity and relevance. You also want to define the expected length, style, and tone of the email.
