Summary: The paper introduces OpenAGI, an open-source AGI research platform that leverages Large Language Models (LLMs) to select, synthesize, and execute domain-specific expert models for solving complex tasks, formulated as natural language queries.

Key insights and lessons learned:

  1. Human intelligence's ability to assemble basic skills into complex ones is crucial for AGI development.
  2. Recent developments in LLMs have shown promising learning and reasoning abilities for complex task-solving.
  3. OpenAGI provides a platform for integrating domain-specific expert models with LLMs for addressing complex tasks.
  4. The formulation of complex tasks as natural language queries allows for seamless interaction with LLMs and external models.

Questions for the authors:

  1. What are the potential applications of OpenAGI in real-world scenarios?
  2. How did you design the task-specific datasets and evaluation metrics for OpenAGI?
  3. Can you provide examples of domain-specific expert models that can be integrated with OpenAGI?
  4. How does OpenAGI handle uncertainty and ambiguity in natural language queries for complex tasks?
  5. What are the limitations and challenges of using LLMs and domain-specific expert models in OpenAGI?

Suggestions for related topics or future research directions:

  1. Exploring reinforcement learning approaches for training LLMs to improve their capability to select and synthesize external models.
  2. Investigating methods for incorporating user feedback into the model selection and synthesis process in OpenAGI.
  3. Studying the interpretability and explainability of the decision-making process of LLMs in selecting and synthesizing external models.
  4. Extending OpenAGI to support multi-modal inputs, such as incorporating vision and audio-based information for addressing complex tasks.
  5. Researching the ethical implications of using OpenAGI, including issues related to bias, fairness, and accountability.

Relevant references:

  1. Radford, A., et al. (2019). Language models are unsupervised multitask learners. arXiv preprint arXiv:1910.05855.