This paper surveys research works in the quickly advancing field of instruction tuning (IT), 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 IT, the construction of IT datasets, the training of IT models, and applications to different modalities, domains and applications, along with an analysis on aspects that influence the outcome of IT (e.g., generation of instruction outputs, size of the instruction dataset, etc). We also review the potential pitfalls of IT 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

翻译：本文系统调研了指令微调（Instruction Tuning，简称IT）这一快速发展的研究领域。指令微调是一种关键的技术手段，旨在增强大语言模型（Large Language Models，简称LLMs）的能力与可控性。该方法通过在由（指令，输出）对构成的数据集上以监督学习方式进一步训练LLMs，从而弥合了LLMs的下一词预测目标与用户希望LLMs遵循人类指令的目标之间的差距。本研究对相关文献进行了系统性回顾，涵盖IT的通用方法论、IT数据集的构建、IT模型的训练、在不同模态与领域及应用程序中的应用，同时分析了影响IT效果的因素（如指令输出的生成方式、指令数据集的规模等）。此外，我们还探讨了IT的潜在缺陷及对其的批评，指出了现有策略中的不足之处，并提出了富有前景的研究方向。项目页面：github.com/xiaoya-li/Instruction-Tuning-Survey