An LLM seeds and slots a task-specific architecture search space that conventional NAS then explores, reaching SOTA on 11 of 17 diverse NAS benchmarks.
arXiv preprint arXiv:2402.18443 (2024)
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abstract
Building efficient neural network architectures can be a time-consuming task requiring extensive expert knowledge. This task becomes particularly challenging for edge devices because one has to consider parameters such as power consumption during inferencing, model size, inferencing speed, and CO2 emissions. In this article, we introduce a novel framework designed to automatically discover new neural network architectures based on user-defined parameters, an expert system, and an LLM trained on a large amount of open-domain knowledge. The introduced framework (LeMo-NADe) is tailored to be used by non-AI experts, does not require a predetermined neural architecture search space, and considers a large set of edge device-specific parameters. We implement and validate this proposed neural architecture discovery framework using CIFAR-10, CIFAR-100, and ImageNet16-120 datasets while using GPT-4 Turbo and Gemini as the LLM component. We observe that the proposed framework can rapidly (within hours) discover intricate neural network models that perform extremely well across a diverse set of application settings defined by the user.
years
2026 3representative citing papers
Authors structure architectural design knowledge with LLMs to create an open-ended NAS space and introduce FairNAD, which finds architectures improving 0.84, 2.17, and 2.35 points over SOTA on CIFAR-10, CIFAR-100, and ImageNet16-120.
LLMasTool improves neural architecture search by evolving code-mined hierarchical trees with diversity-guided Bayesian planning and targeted LLM assistance, reporting gains of 0.69, 1.83, and 2.68 points on CIFAR-10, CIFAR-100, and ImageNet16-120.
citing papers explorer
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Agentic Neural Architecture Search
An LLM seeds and slots a task-specific architecture search space that conventional NAS then explores, reaching SOTA on 11 of 17 diverse NAS benchmarks.
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Structuring Open-Ended NAS: Semi-Automated Design Knowledge Structuring with LLMs for Efficient Neural Architecture Search
Authors structure architectural design knowledge with LLMs to create an open-ended NAS space and introduce FairNAD, which finds architectures improving 0.84, 2.17, and 2.35 points over SOTA on CIFAR-10, CIFAR-100, and ImageNet16-120.
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LLM as a Tool, Not an Agent: Code-Mined Tree Transformations for Neural Architecture Search
LLMasTool improves neural architecture search by evolving code-mined hierarchical trees with diversity-guided Bayesian planning and targeted LLM assistance, reporting gains of 0.69, 1.83, and 2.68 points on CIFAR-10, CIFAR-100, and ImageNet16-120.