核心概念
Large language models can act as evolutionary optimization algorithms through a novel prompting strategy, EvoLLM, outperforming traditional baselines on various tasks.
要約
Large language models (LLMs) can implement evolution strategies for black-box optimization tasks. The EvoLLM prompt strategy enables LLMs to perform robustly on synthetic BBOB functions and small neuroevolution tasks. The performance of EvoLLM is influenced by factors such as context construction, solution representation, and fine-tuning with teacher algorithms.
The content discusses the capabilities of LLMs in implementing evolution strategies for optimization tasks. It introduces the EvoLLM prompt strategy and highlights its effectiveness in outperforming traditional baselines on various tasks. The importance of context construction, solution representation, and fine-tuning with teacher algorithms is emphasized to enhance EvoLLM's performance.
統計
Large Transformer models are capable of implementing a plethora of in-context learning algorithms.
LLM-based Evolution Strategies (green) outperform traditional baselines (blue).
Results are averaged over ten and five independent runs.
Larger LLM models tend to underperform compared to smaller ones.
Choosing a sufficient solution representation is critical for in-context BBO powered by LLMs.
引用
"Large language models can robustly perform zero-shot optimization on classic BBO and small neural network control tasks."
"EvoLLM successfully performs black-box optimization on synthetic BBOB test functions."
"LLMs can act as 'plug-in' in-context recombination operators."