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Large language models (LLMs) have shown remarkable instruction-followingcapabilities and achieved impressive performances in various applications.However, the performances of LLMs depend heavily on the instructions given tothem, which are typically manually tuned with substantial human efforts. Recentwork has used the query-efficient Bayesian optimization (BO) algorithm toautomatically optimize the instructions given to black-box LLMs. However, BOusually falls short when optimizing highly sophisticated (e.g.,high-dimensional) objective functions, such as the functions mapping aninstruction to the performance of an LLM. This is mainly due to the limitedexpressive power of the Gaussian process (GP) model which is used by BO as asurrogate to model the objective function. Meanwhile, it has been repeatedlyshown that neural networks (NNs), especially pre-trained transformers, possessstrong expressive power and can model highly complex functions. So, we adopt aneural bandit algorithm which replaces the GP in BO by an NN surrogate tooptimize instructions for black-box LLMs. More importantly, the neural banditalgorithm allows us to naturally couple the NN surrogate with the hiddenrepresentation learned by a pre-trained transformer (i.e., an open-source LLM),which significantly boosts its performance. These motivate us to propose ourINSTruction optimization usIng Neural bandits Coupled with Transformers}(INSTINCT) algorithm. We perform instruction optimization for ChatGPT and useextensive experiments to show that our INSTINCT consistently outperforms theexisting methods in different tasks, such as in various instruction inductiontasks and the task of improving the zero-shot chain-of-thought instruction.

Use Your INSTINCT: INSTruction optimization usIng Neural bandits Coupled with Transformers