Summary of Large Language Models As Evolution Strategies, by Robert Tjarko Lange et al.
Large Language Models As Evolution Strategies
by Robert Tjarko Lange, Yingtao Tian, Yujin Tang
First submitted to arxiv on: 28 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large Transformer models have been shown to implement various in-context learning algorithms, including gradient descent, classification, sequence completion, transformation, and improvement. This work investigates whether large language models (LLMs) can be used for evolutionary optimization algorithms without explicit task specification. The researchers introduce a novel prompting strategy that allows LLMs to propose improvements to the mean statistic, effectively performing a type of black-box recombination operation. Empirically, they find that this approach, dubbed `EvoLLM’, outperforms baseline algorithms on synthetic BBOB functions and small neuroevolution tasks. The study also explores the impact of model size, prompt strategy, and context construction on EvoLLM’s performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are super smart computers that can do lots of things without being explicitly taught. This research shows that these models can be used to improve optimization algorithms, which are important for finding the best solution in complex problems. The researchers came up with a new way to ask the model questions that helps it make better decisions. They tested this approach and found that it works really well on some tricky math problems. This could lead to breakthroughs in many areas, including artificial intelligence. |
Keywords
* Artificial intelligence * Classification * Gradient descent * Optimization * Prompt * Prompting * Transformer
