Domain
Natural Language Processing
Prior work has shown that finetuning large language models (LLMs) using machinegenerated instruction-following data enables such models to achieve remarkable zero-shot capabilities on new tasks, and no human-written instructions are needed.In this paper, we present the first attempt to use GPT-4 to generate instructionfollowing data for LLM finetuning.Our early experiments on instruction-tuned LLaMA models show that the 52K English and Chinese instruction-following data generated by GPT-4 leads to superior zero-shot performance on new tasks to the instruction-following data generated by previous state-of-the-art models.We also collect feedback and comparison data from GPT-4 to enable a comprehensive evaluation and reward model training.We make our data generated using GPT-4 as well as our codebase publicly available. 1* Equal Contribution 1 https://instruction-tuning-with-gpt-4.github.io/Note: This is a preliminary release, and we will continue to expand the dataset and will finetune larger models.
This paper presents the first attempt to use GPT-4 for generating instruction-following data to finetune large language models (LLMs) and assesses its effects on zero-shot performance tasks. The authors demonstrate that the instruction-following datasets, comprising 52K examples in English and Chinese, significantly improve performance compared to previous models. Their approach includes collecting GPT-4 feedback for evaluating model performance and training reward models. The empirical studies validate the effectiveness of GPT-4-generated data, suggesting best practices for building instruction-following LLMs. The paper also outlines plans for future work to expand data collection and enhance model training using reinforcement learning from human feedback.
This paper employs the following methods:
- Instruction-tuning
- Self-Instruct tuning
- Reinforcement Learning from Human Feedback (RLHF)
The following datasets were used in this research:
- GPT-4 data leads to superior zero-shot performance on new tasks
- LLaMA models trained with GPT-4 show comparable performance to GPT-4 itself
- GPT-4-generated instruction-following data enhances alignment with human values
The authors identified the following limitations:
- Current dataset size is limited to 52K instances; plans for expansion are suggested
- Only using reward models during decoding stage suggests lack of full integration in training
- Number of GPUs: None specified
- GPU Type: None specified
GPT-4
instruction tuning
LLMs
self-instruct
evaluation
reward models