Large language models~(LLMs) are instruction followers, but it can be challenging to find the best instruction for different situations, especially for black-box LLMs on which backpropagation is forbidden. Instead of directly optimizing the discrete instruction, we optimize a low-dimensional soft prompt applied to an open-source LLM to generate the instruction for the black-box LLM. On each iteration of the proposed method, which we call InstructZero, a soft prompt is converted into an instruction using the open-source LLM, which is then submitted to the black-box LLM for zero-shot evaluation, and the performance is sent to Bayesian optimization to produce new soft prompts improving the zero-shot performance. We evaluate InstructZero on different combinations of open-source LLMs and APIs including Vicuna and ChatGPT. Our results show that InstructZero outperforms SOTA auto-instruction methods across a variety of downstream tasks. Our code and data are publicly available at https://github.com/Lichang-Chen/InstructZero.
@article{arxiv.2306.03082,
title = {InstructZero: Efficient Instruction Optimization for Black-Box Large Language Models},
author = {Lichang Chen and Jiuhai Chen and Tom Goldstein and Heng Huang and Tianyi Zhou},
journal= {arXiv preprint arXiv:2306.03082},
year = {2023}
}
Comments
15 pages; 9 figures; Our code is available at https://lichang-chen.github.io/InstructZero/