LLAMBO integrates Large Language Models (LLMs) into Bayesian Optimization (BO) to enhance its efficiency and performance. The paper introduces LLAMBO, a novel approach that leverages LLMs to iteratively propose and evaluate promising solutions in BO. By framing the BO problem in natural language, LLMs can utilize their contextual understanding, few-shot learning, and domain knowledge to improve surrogate modeling and candidate sampling. LLAMBO is effective at zero-shot warmstarting and enhances BO performance, especially in the early stages of search when observations are sparse. It is modular and does not require LLM fine-tuning, allowing individual components to be integrated into existing BO frameworks or used as an end-to-end method. The paper validates LLAMBO's efficacy on hyperparameter tuning across diverse benchmarks, proprietary, and synthetic tasks. The study explores how LLMs can enhance key BO components, including surrogate models and candidate point samplers, through in-context learning. LLAMBO demonstrates improved performance in warmstarting, surrogate modeling, and candidate sampling. The paper also investigates the role of prior knowledge in LLAMBO's few-shot performance and shows that LLAMBO can achieve better prediction performance and uncertainty calibration. The study highlights the potential of integrating LLMs into BO for efficient search and exploration, particularly in scenarios with limited data. LLAMBO is shown to be an effective end-to-end BO method, with sample-efficient search and modularity that allows for integration into existing frameworks. The paper discusses the limitations of LLAMBO, including its computational complexity, and suggests future directions for integrating LLMs into more computationally efficient methods. The study emphasizes the importance of prior knowledge in enhancing BO performance and highlights the potential of LLMs in expanding BO applications to higher-dimensional tasks. The paper also addresses ethical and reproducibility considerations, ensuring that private datasets are de-identified and that the research is reproducible through provided code and repositories. Overall, the study demonstrates the effectiveness of integrating LLMs into BO for improving search efficiency and performance in complex optimization tasks.LLAMBO integrates Large Language Models (LLMs) into Bayesian Optimization (BO) to enhance its efficiency and performance. The paper introduces LLAMBO, a novel approach that leverages LLMs to iteratively propose and evaluate promising solutions in BO. By framing the BO problem in natural language, LLMs can utilize their contextual understanding, few-shot learning, and domain knowledge to improve surrogate modeling and candidate sampling. LLAMBO is effective at zero-shot warmstarting and enhances BO performance, especially in the early stages of search when observations are sparse. It is modular and does not require LLM fine-tuning, allowing individual components to be integrated into existing BO frameworks or used as an end-to-end method. The paper validates LLAMBO's efficacy on hyperparameter tuning across diverse benchmarks, proprietary, and synthetic tasks. The study explores how LLMs can enhance key BO components, including surrogate models and candidate point samplers, through in-context learning. LLAMBO demonstrates improved performance in warmstarting, surrogate modeling, and candidate sampling. The paper also investigates the role of prior knowledge in LLAMBO's few-shot performance and shows that LLAMBO can achieve better prediction performance and uncertainty calibration. The study highlights the potential of integrating LLMs into BO for efficient search and exploration, particularly in scenarios with limited data. LLAMBO is shown to be an effective end-to-end BO method, with sample-efficient search and modularity that allows for integration into existing frameworks. The paper discusses the limitations of LLAMBO, including its computational complexity, and suggests future directions for integrating LLMs into more computationally efficient methods. The study emphasizes the importance of prior knowledge in enhancing BO performance and highlights the potential of LLMs in expanding BO applications to higher-dimensional tasks. The paper also addresses ethical and reproducibility considerations, ensuring that private datasets are de-identified and that the research is reproducible through provided code and repositories. Overall, the study demonstrates the effectiveness of integrating LLMs into BO for improving search efficiency and performance in complex optimization tasks.