What is the difference between instruction fine-tuning, full fine-tuning and supervised fine-tuning (SFT)?
https://www.coursera.org/learn/generative-ai-with-llms/lecture/cTZRI/fine-tuning-on-a-single-task lecture tells about full fine-tuning.
Is every instruction fine-tuning a full fine-tuning i.e., all the model weights are updated or there can also be instruction fine-tuning where all model weights are not updated?
Can you please clarify these.
Sure, I’d be happy to clarify the differences between instruction fine-tuning, full fine-tuning, and supervised fine-tuning (SFT).
Instruction Fine-Tuning: This approach involves fine-tuning a language model on a specific task or set of tasks by providing task-specific instructions or examples. The fine-tuning process focuses on updating the model’s parameters to adapt it to the new task(s) while minimizing changes to the pre-trained weights. This allows the model to specialize in performing the new task(s) without significantly altering its pre-trained knowledge.
Full Fine-Tuning: This refers to the process of fine-tuning a language model on a specific task by updating all of the model’s parameters, including both the pre-trained weights and the task-specific weights. This approach allows for more extensive adaptation to the new task(s) but may also carry a higher risk of overfitting to the training data.
Supervised Fine-Tuning (SFT): It involves fine-tuning a language model on a specific task using labeled training data. The model is trained in a supervised learning setting, where it learns to make predictions based on input-output pairs provided in the training data. This approach is commonly used for tasks such as text classification, named entity recognition, and other supervised learning tasks.
Regarding your second question, not every instruction fine-tuning is a full fine-tuning. In instruction fine-tuning, it is possible to update only a subset of the model’s weights while keeping other weights fixed. This allows for more targeted adaptation to the new task without significantly altering the model’s pre-trained knowledge. The decision to update all model weights or only a subset of weights during instruction fine-tuning depends on the specific requirements of the task and the desired balance between task specialization and preservation of pre-trained knowledge.
I hope this clarifies the differences between these fine-tuning approaches.