Daily Arxiv

This is a page that curates AI-related papers published worldwide.
All content here is summarized using Google Gemini and operated on a non-profit basis.
Copyright for each paper belongs to the authors and their institutions; please make sure to credit the source when sharing.

Language-Based Bayesian Optimization Research Assistant (BORA)

Created by
  • Haebom

Author

Abdoulatif Ciss e, Xenophon Evangelopoulos, Vladimir V. Gusev, Andrew I. Cooper

Outline

This paper proposes a hybrid Bayesian optimization (BO) framework leveraging large-scale language models (LLMs) to address multivariate optimization problems coupled with slow and arduous experimental measurements. To address the problem of local minima in nonconvex optimization environments, the LLM efficiently combines probabilistic inference and domain knowledge by providing insights based on domain knowledge to suggest promising regions of the search space. This approach enhances user engagement by providing real-time commentary on the optimization process, and demonstrates performance improvements on synthetic benchmarks with up to 15 independent variables and four real-world experimental tasks.

Takeaways, Limitations

•
Takeaways:
◦
A novel method for improving the efficiency of Bayesian optimization by utilizing LLM is presented.
◦
Combining the knowledge of domain experts with the insights of LLMs to improve the optimization process.
◦
Increase user engagement and improve understanding of the optimization process through real-time feedback.
◦
Demonstrated performance improvements on both synthetic and real experimental data.
•
Limitations:
◦
The performance of an LLM may depend on the quality of the LLM itself and the training data.
◦
Further research is needed on the interpretability and reliability of the LLM.
◦
Further verification of the proposed method's generalization performance and applicability to various problem types is needed.
◦
The computational cost of LLM can affect the optimization process.
👍
Made with Slashpage