# Instruction Tuning for Large Language Models: A Survey

**Authors:** Shengyu Zhang, Linfeng Dong, Xiaoya Li, Sen Zhang, Xiaofei Sun, Shuhe Wang, Jiwei Li, Runyi Hu, Tianwei Zhang, Fei Wu, Guoyin Wang  
**arXiv:** [2308.10792](https://arxiv.org/abs/2308.10792)  
**Date:** 2023-08-21  
**Summary page:** https://www.summarizepaper.com/en/paper/2308.10792v1/

## Key points

- Comprehensive survey of research works in the field of instruction tuning (IT)
- IT enhances capabilities and controllability of large language models (LLMs)
- IT involves training LLMs on a dataset of (instruction, output) pairs
- Bridging the gap between next-word prediction objective and users' objective
- Systematic review covering methodology, datasets, training, and applications
- Analysis of factors influencing IT outcome: instruction outputs and dataset size
- Discussion on potential pitfalls and criticisms against IT
- Emphasis on need for instinct or common sense in question creation for event duration tasks
- Positive and negative examples provided for question creation guidance
- Caution against explicit mentions of answers in text
- Specific task instances for generating questions related to event duration based on given sentences

## Summary

This paper provides a comprehensive survey of research works in the field of instruction tuning (IT), which is a crucial technique for enhancing the capabilities and controllability of large language models (LLMs). IT involves further training LLMs on a dataset consisting of \textsc{(instruction, output)} pairs in a supervised fashion, bridging the gap between the next-word prediction objective of LLMs and the users' objective of having LLMs adhere to human instructions. The authors systematically review the literature on IT, covering various aspects such as the general methodology of IT, construction of IT datasets, training of IT models, and applications to different modalities, domains, and tasks. They also analyze factors that influence the outcome of IT including generation of instruction outputs and size of the instruction dataset. Additionally, this paper discusses potential pitfalls and criticisms against IT. It highlights efforts that identify current deficiencies in existing strategies and suggests avenues for future research. The authors emphasize the need for instinct or common sense in creating questions that involve "event duration" for tasks like MC-TACO question generation. They provide positive and negative examples to guide question creation and caution against explicit mentions of answers in text. Furthermore, this paper includes specific task instances for generating questions related to event duration based on given sentences. These instances demonstrate how participants are expected to formulate questions using their understanding of how long events typically last. Overall, this expanded summary provides a detailed overview of the paper's content including its focus on instruction tuning in large language models; analysis of influencing factors and pitfalls; discussion on criticism and deficiencies in existing strategies; suggestions for fruitful research directions; as well as specific instructions for generating questions involving commonsense understanding of event duration.