The LLAMBO framework integrates Large Language Models (LLMs) into Bayesian Optimization (BO) to improve search efficiency. It introduces zero-shot warmstarting, generative and discriminative surrogate models, and a candidate point sampler that can conditionally generate points based on desired objective values. Empirical evaluations demonstrate superior performance across diverse benchmarks, especially in early search stages with limited data.
Bayesian optimization is a powerful approach for optimizing complex functions without direct access to gradients. LLAMBO enhances this process by leveraging the strengths of LLMs in few-shot learning and contextual understanding. The integration of LLMs improves surrogate modeling, candidate sampling, and overall end-to-end performance in hyperparameter tuning tasks.
Key considerations include the ability of LLMs to generalize from sparse data efficiently and their capacity to exploit encoded priors for improved performance. The study showcases the effectiveness of LLAMBO in enhancing various components of BO and its potential applications beyond hyperparameter tuning tasks.
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arxiv.org
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