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

Abstract
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.
Anthology ID:
2023.findings-emnlp.659
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2023
Month:
December
Year:
2023
Address:
Singapore
Editors:
Houda Bouamor, Juan Pino, Kalika Bali
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
9850–9867
Language:
URL:
https://aclanthology.org/2023.findings-emnlp.659
DOI:
10.18653/v1/2023.findings-emnlp.659
Bibkey:
Cite (ACL):
Zhihan Zhang, Shuohang Wang, Wenhao Yu, Yichong Xu, Dan Iter, Qingkai Zeng, Yang Liu, Chenguang Zhu, and Meng Jiang. 2023. Auto-Instruct: Automatic Instruction Generation and Ranking for Black-Box Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 9850–9867, Singapore. Association for Computational Linguistics.
Cite (Informal):
Auto-Instruct: Automatic Instruction Generation and Ranking for Black-Box Language Models (Zhang et al., Findings 2023)
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