- The paper introduces the Embed-then-Regress framework that integrates language model embeddings into Bayesian Optimization.
- It leverages a Transformer-based regression model to make uncertainty-aware predictions from string inputs.
- Empirical results demonstrate competitive performance across synthetic, combinatorial, and hyperparameter optimization tasks.
Predicting from Strings: LLM Embeddings for Bayesian Optimization
This paper presents a novel approach to Bayesian Optimization (BO), introducing the Embed-then-Regress framework that leverages LLM embeddings for general-purpose regression over string inputs. This method aims to expand the applicability of BO beyond traditional regression models limited to fixed search spaces, enabling it to handle diverse domains such as combinatorial and hyperparameter optimization.
Methodology
The Embed-then-Regress approach utilizes LLMs to generate embeddings of string representations of inputs, which are subsequently used in a regression context. A significant advantage of this strategy is its flexibility in representing various data types, overcoming the limitations of traditional GP-based methods, which often require task-specific adaptations.
The paper outlines the use of a pre-trained T5 model to encode strings and leverage these embeddings within a Transformer-based regression model. This regression model, through in-context learning (ICL), processes historical evaluation data to make uncertainty-aware predictions about unseen objective functions.
Key Contributions
- Framework Implementation: The Embed-then-Regress framework facilitates the integration of free-form string representations in BO, embedding trial inputs via LLMs for regression tasks.
- Transformer-based Regressor: Pretraining a Transformer over extensive offline evaluation data enables the model to perform competitive numeric predictions across unfamiliar objective functions.
- Optimization Efficacy: The paper reports that this framework achieves competitive results across a range of tasks including synthetic, combinatorial, and hyperparameter optimization.
Empirical Evaluation
The paper evaluates its framework on benchmarks covering synthetic, combinatorial, and hyperparameter optimization tasks. The results demonstrate that the Embed-then-Regress method performs comparably to industry-standard GP methods. Specifically, it shows notable performance in permutation and combination tasks, which are traditionally challenging for regression models due to the high-dimensional search spaces involved.
Implications and Future Directions
The integration of LLMs in BO as proposed in this paper holds significant implications for the expansion of BO methods into new and complex domains without the need for heavily engineered feature spaces. The framework's flexibility suggests potential applications in universal optimization settings where tasks are diverse and high-dimensional.
Looking forward, the paper suggests exploration into more efficient Transformer architectures and embedding techniques. Additionally, the potential development of string-based Gaussian Processes could further enhance the robustness and applicability of BO, particularly around uncertainty estimation.
Conclusion
The Embed-then-Regress paradigm illustrates an innovative direction for extending Bayesian Optimization capabilities using LLM embeddings. By bridging the gap between LLMs and BO, this work sets the stage for broader and more adaptable solution strategies in optimization, potentially inspiring a new wave of research efforts aimed at exploiting the synergies between language processing and optimization tasks.