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(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 application, along with 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.††footnotetext:♠Zhejiang University,♣Shannon.AI,▲Nanyang Technological University,◆Amazon††footnotetext:Email: sy_zhang@zju.edu.cn††footnotetext:Project link:https://github.com/xiaoya-li/Instruction-Tuning-Survey