Summary: The paper presents the first attempt to use GPT-4 to generate instruction-following data for finetuning large language models (LLMs) and shows that the instruction-following data generated by GPT-4 leads to superior zero-shot performance on new tasks compared to previous state-of-the-art models.

Key insights and lessons learned:

  1. GPT-4 can be used to generate instruction-following data for finetuning LLMs, enabling superior zero-shot performance on new tasks.
  2. The instruction-following data generated by GPT-4 outperforms previous state-of-the-art models in terms of zero-shot capabilities.
  3. Feedback and comparison data from GPT-4 can be used for comprehensive evaluation and reward model training.
  4. The data generated using GPT-4 and the codebase are made publicly available for further research and development.

Questions for the authors:

  1. How did you collect feedback and comparison data from GPT-4 for evaluation and reward model training?
  2. What were the specific tasks on which the instruction-following data generated by GPT-4 showed superior zero-shot performance compared to previous models?
  3. Did you encounter any challenges or limitations in using GPT-4 for generating instruction-following data? If so, how did you address them?
  4. How do you envision the potential applications of instruction-tuning with GPT-4 in real-world scenarios?
  5. What are the implications of your findings for the field of Computation and Language and Artificial Intelligence research?

Suggestions for related topics or future research directions:

  1. Exploring the impact of using instruction-tuning with GPT-4 on different types of tasks, domains, and languages.
  2. Investigating the interpretability and explainability of the instructions generated by GPT-4 for fine-tuning LLMs.
  3. Studying the generalization and transfer learning capabilities of LLMs finetuned with instruction-following data generated by GPT-4.
  4. Investigating the potential ethical considerations and implications of using machine-generated instructions in real-world applications.
  5. Exploring the potential of combining instruction-tuning with other techniques such as transfer learning, reinforcement learning, or multi-modal learning for further improving the performance of LLMs on new tasks.

Relevant references:

  1. Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language models are unsupervised multitask learners. OpenAI.