What is Instruction Tuning?
Instruction tuning is a fine-tuning technique that trains language models to follow natural language instructions by exposing them to diverse tasks framed as instruction-following problems. Rather than training on a single task, instruction-tuned models learn from datasets containing many different tasks, each presented with instructions describing what to do. This creates models that can generalize to new tasks specified through instructions, even tasks they weren't explicitly trained on.
The training process uses datasets like FLAN, T0, or custom instruction datasets where each example consists of an instruction, optional input, and desired output. For instance, examples might include "Translate this to French: [text]", "Summarize the following article: [text]", or "Answer this question: [question]". By training on this diverse mixture, models learn the general pattern of following instructions rather than memorizing specific task solutions.
Instruction tuning has been crucial to making LLMs more useful and aligned with user intentions. It's a key step in creating models like GPT-3.5, GPT-4, Claude, and other modern assistants that can understand and execute a wide variety of instructions. The technique bridges the gap between pre-trained models (which predict text continuations) and instruction-following assistants (which complete tasks specified in natural language), often serving as the first step before more sophisticated alignment techniques like RLHF.