Generative AI with LLMs: Instruction Fine-Tuning vs In-Context Learning for LLMs

Learn the difference between instruction fine-tuning and in-context learning for large language models (LLMs), and how they enable zero-shot task generalization.

Question

Fill in the blanks: __________ involves using many prompt-completion examples as the labeled training dataset to continue training the model by updating its weights. This is different from _________ where you provide prompt-completion examples during inference.

A. Pre-training, Instruction fine-tuning
B. In-context learning, Instruction fine-tuning
C. Instruction fine-tuning, In-context learning
D. Prompt engineering, Pre-training

Answer

C. Instruction fine-tuning, In-context learning

Explanation

The correct answer is C. Instruction fine-tuning involves using many prompt-completion examples as the labeled training dataset to continue training the model by updating its weights. This is different from in-context learning where you provide prompt-completion examples during inference.

Instruction fine-tuning is a strategic extension of the traditional fine-tuning approach. Instead of training the model on conventional prompt-completion pairs, it is trained on examples of instructions and how the LLM should respond to those instructions. For example, the instruction could be “Write a summary of the following article” and the LLM should produce a summary as the completion. Instruction fine-tuning enables the LLM to generalize to new tasks that are specified by natural language instructions at inference time.

In-context learning is a technique that leverages the LLM’s ability to learn from the context of the input. By providing a few prompt-completion examples before the actual query, the LLM can infer the task and the desired output format from the examples. In-context learning does not require any additional training of the model, but it relies on the model’s pre-trained knowledge and reasoning skills. For example, the input could be a few examples of sentiment classification followed by a sentence to be classified, and the LLM should produce the correct label as the completion.

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