LLM-Driven Neural Network Generation with Same-Family Architecture Guidance: Disentangling Transfer and Adaptation
arXiv:2607.05704v1 Announce Type: new Abstract: Large language models (LLMs) can generate neural-network modifications, but unrestricted generation is often invalid or harmful. This paper studies a narrower setting: improving a weak target model using a stronger same-family source model from a neural-network database. We propose a source-guided candidate-generation protocol with non-source controls, source-conditioned candidates, and a no-LLM hp_copy ablation under equal evaluation budgets. The protocol reports validity separately from accuracy and selects the best valid candidate only when it
The proliferation of Large Language Models (LLMs) and the increasing complexity of neural network architectures necessitate improved, automated generation and optimization methods.
This work indicates a more effective method for LLMs to generate valid and performant neural network modifications, potentially accelerating AI development and optimization significantly.
The ability of LLMs to generate specialized and improved neural network architectures is enhanced by incorporating guidance from similar, stronger models, reducing invalid outputs.
- · AI researchers and developers
- · Companies with large neural network databases
- · Industries relying on advanced AI models
- · Manual neural network design processes
- · Inefficient AI development cycles
LLMs can reliably generate more effective neural network architectures for specific tasks.
This could lead to a faster pace of innovation in AI model design and application across various sectors.
Automated, LLM-guided architecture generation might democratize advanced AI development, making it accessible to a broader range of practitioners.
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Read at arXiv cs.LG