GENIUS: Can GPT-4 Perform Neural Architecture Search?
Neural Architecture Search (NAS) is a challenging task in machine learning, involving the design of effective neural architectures. Traditional NAS methods require significant computational resources and domain expertise to explore and optimize the search space. However, recent advances in natural language processing have shown the potential of language models such as GPT-4 to perform optimization tasks through their generative capabilities.
In this study, the authors propose a novel approach called GENIUS (GPT-4 Enhanced Neural archItectUre Search) that leverages GPT-4’s black-box optimization capabilities to quickly navigate the architecture search space, pinpoint promising candidates, and iteratively refine them to improve performance.
Their objective is to explore GPT-4’s potential as an optimization tool for NAS, rather than targeting state-of-the-art performance.
To assess GENIUS, they conducted experiments on several benchmarks and compared it with existing state-of-the-art NAS techniques. The results show that GENIUS outperforms existing methods in terms of computational efficiency and sample efficiency. Moreover, their approach requires relatively limited domain expertise, making it accessible to a wider audience.
However, it is important to note that the study has limitations, and they highlight implications for AI safety. For instance, there is a risk that GPT-4 may generate architectures that are not interpretable or lack transparency, making it difficult to explain how decisions are made. Further research is needed to address such concerns.
This study suggests that language models like GPT-4 have the potential to assist research in challenging technical problems through a simple prompting scheme that requires limited domain expertise.
This approach opens new avenues for exploring general-purpose language models for diverse optimization tasks.
In conclusion, this study demonstrates the feasibility of using GPT-4 as a tool for NAS and highlights its potential to improve computational efficiency and sample efficiency.