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EvoPrompting: Language Models for Code-Level Neural Architecture Search

NeurIPS 2023 · Architectures and efficiency › Neural architecture search · approach: Bi-level differentiable NAS · 18 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
search with EvoPrompting to discover new triplet representations instead of the standard Triplet-GMPNNTriplet-GMPNN · CLRS Heapsort OODOOD accuracy: 31.0 -> 69.9Helped
search with EvoPrompting to discover new triplet representationsTriplet-GMPNN · CLRS Bubble Sort OODOOD accuracy: 67.7 -> 88.9Helped
search with EvoPrompting to discover new triplet representationsTriplet-GMPNN · CLRS BFS OODOOD accuracy: 99.7 -> 100.0No clear effect
remove prompt-tuning between rounds (ablation)EvoPrompting · MNIST-1Dmax fitness / Pareto frontierHurt
select parents randomly instead of by fitness (ablation)EvoPrompting · MNIST-1Dmax fitnessHurt
add prompt-tuning to random-parents prompting (ablation)naive few-shot prompting · MNIST-1DfitnessNo clear effect
evolutionary search with prompt-tuning over naive few-shot prompting (ablation)PaLM 62B · MNIST-1DPareto frontier of test error vs model sizeHelped

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