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

• URL: http://arxiv.org/abs/2306.03082v2
• Date: Tue, 8 Aug 2023 17:33:54 GMT
• Title: InstructZero: Efficient Instruction Optimization for Black-Box Large Language Models
• Authors: Lichang Chen, Jiuhai Chen, Tom Goldstein, Heng Huang, Tianyi Zhou
• Abstract summary: Large language models(LLMs) are instruction followers, but it can be challenging to find the best instruction for different situations. We optimize a low-dimensional soft prompt applied to an open-source LLM to generate the instruction for the black-box LLM. Our results show that InstructZero outperforms SOTA auto-instruction methods across a variety of downstream tasks.
• Score: 117.92988284226765
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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