How does the diversity of neural architecture search spaces in AutoML systems affect cross-domain generalizati

Abstract

Abstract: The integration of Large Language Models (LLMs) with Neural Architecture Search (NAS) has introduced new possibilities for automating the design of neural architectures. However, most existing methods face critical limitations, including architectural invalidity, computational inefficiency, and inferior performance compared to traditional NAS. In this work, we present Collaborative LLM-based NAS (CoLLM-NAS), a two-stage NAS framework with knowledge-guided search driven by two complementary LLMs. Specifically, we propose a stateful Navigator LLM to guide search direction, a stateless Generator

Research Question

How does the diversity of neural architecture search spaces in AutoML systems affect cross-domain generalization on language model benchmarks compared to fixed transformer architectures?

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