Research Paper#Neural Architecture Search, Large Language Models, Computer Vision🔬 ResearchAnalyzed: Jan 3, 2026 15:53
LLM-Based Neural Network Architecture Design: Few-Shot Prompting and Efficient Validation
Published:Dec 30, 2025 10:01
•1 min read
•ArXiv
Analysis
This paper addresses the challenge of automated neural network architecture design in computer vision, leveraging Large Language Models (LLMs) as an alternative to computationally expensive Neural Architecture Search (NAS). The key contributions are a systematic study of few-shot prompting for architecture generation and a lightweight deduplication method for efficient validation. The work provides practical guidelines and evaluation practices, making automated design more accessible.
Key Takeaways
- •Introduces Few-Shot Architecture Prompting (FSAP) for LLM-based architecture generation.
- •Identifies n=3 examples as optimal for balancing diversity and context.
- •Presents Whitespace-Normalized Hash Validation for efficient deduplication.
- •Provides a dataset-balanced evaluation methodology for heterogeneous vision tasks.
- •Offers actionable guidelines for LLM-based architecture search in computer vision.
Reference
“Using n = 3 examples best balances architectural diversity and context focus for vision tasks.”