Summary of Sequential Large Language Model-based Hyper-parameter Optimization, by Kanan Mahammadli et al.
Sequential Large Language Model-Based Hyper-parameter Optimization
by Kanan Mahammadli, Seyda Ertekin
First submitted to arxiv on: 27 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 This study introduces SLLMBO, a novel framework that leverages large language models (LLMs) for hyperparameter optimization (HPO), addressing limitations in recent fully LLM-based methods and traditional Bayesian optimization (BO). The framework incorporates dynamic search space adaptability, enhanced parameter space exploitation, and a novel LLM-tree-structured Parzen estimator (LLM-TPE) sampler. Multiple LLMs are benchmarked, including GPT-3.5-Turbo, GPT-4o, Claude-Sonnet-3.5, and Gemini-1.5-Flash. The study demonstrates the effectiveness of SLLMBO in achieving more robust optimization, reducing API costs, and mitigating premature early stoppings for more effective parameter searches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SLLMBO is a new way to use large language models (LLMs) to find the best settings for machine learning models. This method combines the strengths of LLMs with another technique called TPE. The study shows that this combination works well and can even beat other methods like Bayesian optimization in many cases. |
Keywords
» Artificial intelligence » Claude » Gemini » Gpt » Hyperparameter » Machine learning » Optimization
