Supervised Fine-Tuning Strategies
Last updated
Last updated
As we discussed in the previous chapter, supervised fine-tuning is a method used to adapt a pre-trained Language Model for specific tasks using labeled data. Unlike unsupervised techniques, supervised fine-tuning uses a dataset of pre-validated responses, which is its key distinguishing feature. During supervised fine-tuning, the model's weights are adjusted using supervised learning techniques. The adjustment process uses task-specific loss calculations, which measure how much the model's output differs from the known correct answers (ground truth labels). This helps the model learn the specific patterns and nuances needed for its intended task.
Consider a pre-trained LLM responding to "Can you summarize this research paper for me?" with a basic response like "The paper discusses the effects of caffeine on sleep patterns and finds negative correlations between evening coffee consumption and sleep quality." While technically accurate, this response might not meet the standards for an academic writing assistant, which typically requires proper structure, methodology highlights, and key findings in context.
This is where supervised fine-tuning becomes valuable. By training the model on a set of validated examples that demonstrate proper academic summaries, the model learns to provide more comprehensive and appropriate responses. These examples might include structured sections (introduction, methodology, results, conclusions), critical analysis of the methodology, limitations of the study, and connections to related research – all following academic writing conventions.
Through supervised fine-tuning with validated training examples, the model learns to enhance its responses while maintaining accuracy, effectively adapting its output style to match the requirements of academic writing and research analysis.
In this section, we will be discussing useful supervised fine-tuning techniques that can help you effectively adapt pre-trained language models for your specific use cases. These techniques will enable you to achieve better performance and more consistent outputs aligned with your requirements.
