InstructZero: Efficient Instruction Optimization for Black-Box Large Language Models

Lichang Chen, Jiuhai Chen, Tom Goldstein, Heng Huang, Tianyi Zhou
Computer Science, Artificial Intelligence, Artificial Intelligence (cs.AI)
2023-06-04 16:00:00
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
PDF: InstructZero: Efficient Instruction Optimization for Black-Box Large Language Models.pdf
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