# 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/2308.10792v5/

## Key points

- Instruction tuning (IT) is a crucial technique for improving large language models (LLMs)
- IT involves training LLMs on a dataset of (instruction, output) pairs in a supervised manner
- Various aspects of IT are covered in the literature, including methodology, dataset construction, model training, and applications across different modalities and domains
- Factors influencing IT outcomes include instruction output generation and dataset size
- Potential pitfalls and criticisms against IT are discussed, along with deficiencies in existing strategies
- Suggestions for future research focus on synthetic data generation methods like Distillation to transfer knowledge from a capable teacher model to a less complex student model
- Researchers are exploring methods like Alpaca and WizardLM/Evol-Instruct to leverage current LLM capabilities
- Examples from Dolly V1 demonstrate how instructions are used for question generation tasks involving commonsense understanding
- Fine-tuned models like LLaMA-7B can achieve performance comparable to or surpassing larger models like GPT-3 through distillation techniques

## Summary

This paper presents a thorough review of research in the field of instruction tuning (IT), a crucial technique for improving the capabilities and controllability of large language models (LLMs). IT involves training LLMs on a dataset consisting of \textsc{(instruction, output)} pairs in a supervised manner to bridge the gap between the next-word prediction objective of LLMs and users' desire for adherence to human instructions. The literature covers various aspects of IT, including methodology, dataset construction, model training, and applications across different modalities, domains, and use cases. It also delves into factors influencing IT outcomes such as instruction output generation and dataset size. Potential pitfalls and criticisms against IT are discussed while highlighting current deficiencies in existing strategies. Suggestions for future research are provided to effectively address these shortcomings. One key focus is on synthetic data generation methods like Distillation where knowledge from a highly capable teacher model is transferred to a less complex student model to improve response quality and computational efficiency. Researchers are exploring intricate queries to leverage the capabilities of current LLMs through methods like Alpaca and WizardLM/Evol-Instruct. Additionally, examples from Dolly V1 demonstrate how instructions are used in practice for question generation tasks involving commonsense understanding. The study showcases how fine-tuned models like LLaMA-7B can achieve performance comparable to or even surpassing that of larger models like GPT-3 through distillation techniques.