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Arxiv
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Large Scale Multi-Task Bayesian Optimization with Large Language Models
- Researchers propose a novel approach using large language models (LLMs) for multi-task Bayesian optimization.
- The goal is to leverage experience from optimizing existing tasks to improve the efficiency of optimizing new tasks.
- Existing approaches with multi-task Gaussian processes or deep kernel transfer show limited performance improvement at scale.
- The new approach scales to about 1500 tasks and involves fine-tuning an LLM on high-quality solutions from Bayesian optimization.
- The fine-tuned LLM generates initialization points for new task searches, creating a feedback loop.
- This method was evaluated in database query optimization and antimicrobial peptide design domains.
- Results show that the LLM's initializations gradually improve, leading to better optimization performance.
- The LLM eventually generates solutions for new tasks with fewer oracle calls, surpassing solutions from Bayesian optimization starting from scratch.
- The approach forms a positive feedback loop, enhancing optimization efficiency over time.
- The research demonstrates the potential of LLMs in improving multi-task Bayesian optimization.
- The study showcases the benefits of leveraging LLMs to enhance optimization processes across various domains.
- The feedback loop with LLMs shows promise in accelerating optimization and achieving superior results with fewer resources.
- This research contributes to advancing optimization techniques through the integration of large language models.
- The study underscores the importance of leveraging existing optimization trajectories to enhance future task optimization.
- The proposed method aims to improve optimization efficiency and performance by utilizing large language models.
- Overall, the approach presents a promising strategy for enhancing multi-task Bayesian optimization using LLMs.
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