Instruction Tuning for Large Language Models: A Survey

TL;DR

This survey comprehensively reviews instruction tuning for large language models, covering methodologies, dataset construction, applications, challenges, and future research directions in this rapidly evolving field.

Contribution

It provides a systematic overview of instruction tuning techniques, datasets, applications, and critical analysis of current challenges and future research avenues.

Findings

Instruction tuning enhances LLM capabilities and controllability.

Dataset size and quality significantly impact SFT outcomes.

Current strategies have notable deficiencies and areas for improvement.

Abstract

This paper surveys research works in the quickly advancing field of instruction tuning (IT), which can also be referred to as supervised fine-tuning (SFT)\footnote{In this paper, unless specified otherwise, supervised fine-tuning (SFT) and instruction tuning (IT) are used interchangeably.}, a crucial technique to enhance the capabilities and controllability of large language models (LLMs). Instruction tuning refers to the process of further training LLMs on a dataset consisting of \textsc{(instruction, output)} pairs in a supervised fashion, which bridges the gap between the next-word prediction objective of LLMs and the users' objective of having LLMs adhere to human instructions. In this work, we make a systematic review of the literature, including the general methodology of SFT, the construction of SFT datasets, the training of SFT models, and applications to different modalities, domains and application, along with analysis on aspects that influence the outcome of SFT (e.g., generation of instruction outputs, size of the instruction dataset, etc). We also review the potential pitfalls of SFT along with criticism against it, along with efforts pointing out current deficiencies of existing strategies and suggest some avenues for fruitful research. Project Page: github.com/xiaoya-li/Instruction-Tuning-Survey