Bayesian Optimization for Instruction Generation Article Swipe
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· 2024
· Open Access
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· DOI: https://doi.org/10.3390/app142411865
· OA: W4405584528
The performance of Large Language Models (LLMs) strongly depends on the selection of the best instructions for different downstream tasks, especially in the case of black-box LLMs. This study introduces BOInG (Bayesian Optimization for Instruction Generation), a method leveraging Bayesian Optimization (BO) to efficiently generate instructions while addressing the combinatorial nature of instruction search. Over the last decade, BO has emerged as a highly effective optimization method in various domains due to its flexibility and sample efficiency. At its core, BOInG employs Bayesian search in a low-dimensional continuous space, projecting solutions into a high-dimensional token embedding space to retrieve discrete tokens. These tokens act as seeds for the generation of human-readable, task-relevant instructions. Experimental results demonstrate that BOInG achieves comparable or superior performance to state-of-the-art methods, such as InstructZero and Instinct, with substantially lower resource requirements while also enabling the use of both white-box and black-box models. This approach offers both theoretical and practical benefits without requiring specialized hardware.
