Large Language Models to Enhance Bayesian Optimization (2402.03921v2)

Abstract: Bayesian optimization (BO) is a powerful approach for optimizing complex and expensive-to-evaluate black-box functions. Its importance is underscored in many applications, notably including hyperparameter tuning, but its efficacy depends on efficiently balancing exploration and exploitation. While there has been substantial progress in BO methods, striking this balance remains a delicate process. In this light, we present LLAMBO, a novel approach that integrates the capabilities of LLMs (LLM) within BO. At a high level, we frame the BO problem in natural language, enabling LLMs to iteratively propose and evaluate promising solutions conditioned on historical evaluations. More specifically, we explore how combining contextual understanding, few-shot learning proficiency, and domain knowledge of LLMs can improve model-based BO. Our findings illustrate that LLAMBO is effective at zero-shot warmstarting, and enhances surrogate modeling and candidate sampling, especially in the early stages of search when observations are sparse. Our approach is performed in context and does not require LLM finetuning. Additionally, it is modular by design, allowing individual components to be integrated into existing BO frameworks, or function cohesively as an end-to-end method. We empirically validate LLAMBO's efficacy on the problem of hyperparameter tuning, highlighting strong empirical performance across a range of diverse benchmarks, proprietary, and synthetic tasks.

• The paper introduces LLAMBO, a method that uses large language models for zero-shot warmstarting to boost the initialization of Bayesian Optimization.
• The paper details an iterative context learning approach that enhances surrogate model accuracy and efficiently predicts outcomes in hyperparameter tuning.
• The paper validates a novel candidate sampling strategy that outperforms traditional methods on diverse benchmarks with limited observations.

Enhancing Bayesian Optimization with LLMs

Introduction to LLAMBO and Its Motivation

Bayesian Optimization (BO) is a critical technique in the optimization of complex, black-box functions, often applied in hyperparameter tuning (HPT) across various fields. Despite its widespread application, BO faces challenges in efficient search due to the delicate balance required between exploration and exploitation, and the construction of accurate surrogate models with limited observations. Addressing these challenges, this paper introduces LLAMBO, a novel approach that leverages the capabilities of LLMs to improve model-based BO through zero-shot warmstarting, enhancement of surrogate modeling, and efficient candidate sampling. LLAMBO’s modular architecture allows seamless integration into existing BO frameworks, providing an end-to-end method that utilizes the inherent strengths of LLMs without the need for finetuning.

Key Components and Methodology

Warmstarting with Zero-Shot Prompting

LLAMBO employs zero-shot prompting to generate initial points for the BO process, effectively leveraging LLM's prior knowledge to begin optimization from promising regions of the search space. This technique outperforms traditional random initialization methods in early search stages by utilizing problem-specific information provided in natural language.

Enhancing Surrogate Models through Iterative Context Learning (ICL)

Surrogate modeling is critical for predicting the performance of untested candidates. LLAMBO introduces two strategies for leveraging LLMs in surrogate modeling:

• A discriminative approach for regression-based prediction with uncertainty.
• A generative approach for generating candidates based on binary classification, mimicking techniques like TPE but with direct conditioning on desired objective values.

These methods capitalize on LLMs' proficiency in few-shot learning and contextual reasoning, enabling accurate predictions and efficient exploration of the search space with sparse initial data.

Efficient Candidate Sampling

LLAMBO proposes a novel sampling strategy that directly generates candidates by conditioning on specific target objective values. This approach surpasses traditional methods in identifying high-potential points by leveraging the contextual understanding and generative capabilities of LLMs, tailored towards the optimization objective.

Experimental Validation and Findings

The paper provides an extensive empirical analysis of LLAMBO, focusing on the domain of HPT. The evaluation demonstrates LLAMBO’s superior performance in initializing the optimization process, improving surrogate model accuracy, and efficiently generating promising candidate points, especially with limited observations. Notably, across diverse benchmarks, LLAMBO outperformed established BO baselines, showcasing its efficacy as a cohesive, stand-alone BO method.

Implications and Future Directions

The integration of LLMs into BO opens new avenues for optimizing complex black-box functions more efficiently. LLAMBO’s performance gains highlight the potential of LLMs to transform BO by enhancing its core components. However, the computational demands of leveraging LLMs call for further investigation into balancing computational costs with optimization efficiency. Future work could explore hybrid approaches, integrating LLAMBO with more computationally efficient algorithms, or adapting LLAMBO to domains with sparse LLM expertise through domain-specific finetuning.

Ethics and Reproducibility

The research adheres to ethical guidelines, particularly in the handling of private datasets, and commits to reproducibility by outlining detailed experimental procedures and offering to release the code upon acceptance.

Conclusion

LLAMBO represents a significant step forward in the application of LLMs to enhance BO. By leveraging the contextual understanding, in-context learning capabilities, and generative prowess of LLMs, LLAMBO addresses key challenges in BO, setting a new benchmark for performance in HPT and potentially other optimization tasks within AI research.