Three-example few-shot prompting optimizes LLM-generated vision architectures while a whitespace-normalized hash provides 100x faster duplicate detection than AST parsing across seven benchmarks.
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Fine-tuned LLMs reach 80% accuracy predicting which dataset a neural network code performs better on, outperforming metadata prompts at 70%.
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Enhancing LLM-Based Neural Network Generation: Few-Shot Prompting and Efficient Validation for Automated Architecture Design
Three-example few-shot prompting optimizes LLM-generated vision architectures while a whitespace-normalized hash provides 100x faster duplicate detection than AST parsing across seven benchmarks.
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From Code to Prediction: Fine-Tuning LLMs for Neural Network Performance Classification in NNGPT
Fine-tuned LLMs reach 80% accuracy predicting which dataset a neural network code performs better on, outperforming metadata prompts at 70%.