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.

对于指令调优（IT）领域的研究进行了概述，它是增强和可控大型语言模型（LLMs）能力的关键技术。该研究系统回顾了IT的一般方法论、IT数据集的构建、IT模型的训练以及不同模态、领域和应用的应用，并分析了影响IT结果的因素（例如，指令输出的生成、指令数据集的大小等）。还审查了IT存在的潜在问题以及对其的批评，指出了现有策略的不足之处，并提出了一些有益的研究方向。

大型语言模型的指导调整：一项调研