Auto-Instruct: Automatic Instruction Generation and Ranking for
  Black-Box Language Models
Zhihan Zhang Shuohang Wang Wenhao Yu Yichong Xu Dan Iter Qingkai Zeng Yang Liu Chenguang Zhu Meng Jiang
Large language models (LLMs) can perform a wide range of tasks by following
natural language instructions, without the necessity of task-specific
fine-tuning. Unfortunately, the performance of LLMs is greatly influenced by
the quality of these instructions, and manually writing effective instructions
for each task is a laborious and subjective process. In this paper, we
introduce Auto-Instruct, a novel method to automatically improve the quality of
instructions provided to LLMs. Our method leverages the inherent generative
ability of LLMs to produce diverse candidate instructions for a given task, and
then ranks them using a scoring model trained on a variety of 575 existing NLP
tasks. In experiments on 118 out-of-domain tasks, Auto-Instruct surpasses both
human-written instructions and existing baselines of LLM-generated
instructions. Furthermore, our method exhibits notable generalizability even
with other LLMs that are not incorporated into its training process.