Instruction tuning

Instruction tuning is a technique in natural language processing designed to improve the accuracy and naturalness of zero-shot prompting interactions. With instruction tuning, a language model is trained on many examples of tasks formulated as natural language instructions, along with appropriate responses. The goal is to enable the language model to generate more natural and accurate responses to new instructions, without the need for explicit training on each specific task.

Background

A pre-trained language model can generate text completions based on the distribution of text it was trained on. However, a naive language model may generate inaccurate or irrelevant completions when given a specific prompt. For example, a language model given the prompt "Write an essay about the main themes of Hamlet" may generate an irrelevant completion such as "I have been working as an executive assistant for 15 years. My previous employer was very demanding but rewarded me well financially..." Instruction tuning addresses this issue by training the language model on many examples of tasks formulated as natural language instructions, along with appropriate responses.

Techniques

Various techniques for instruction tuning have been developed and applied in practice. One such technique is OpenAI's InstructGPT protocol, which involves supervised fine-tuning on a dataset of human-generated (prompt, response) pairs, followed by reinforcement learning from human feedback (RLHF), in which a reward function is learned based on a dataset of human preferences. Another technique, known as "self-instruct," fine-tunes the language model on a training set of examples that are themselves generated by a language model (bootstrapped from a small initial set of human-generated examples).

Applications

Instruction tuning has been applied in a variety of applications, including virtual assistants, chatbots, and language translation systems. By improving the accuracy and naturalness of zero-shot prompting interactions, instruction tuning can make these systems more user-friendly and effective.