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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BiSDG applies bi-level optimization with surrogate domains and a domain prompt encoder to achieve state-of-the-art results in single domain generalization.
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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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Bi-Level Optimization for Single Domain Generalization
BiSDG applies bi-level optimization with surrogate domains and a domain prompt encoder to achieve state-of-the-art results in single domain generalization.