@inproceedings{zhang-etal-2023-auto,
title = "Auto-Instruct: Automatic Instruction Generation and Ranking for Black-Box Language Models",
author = "Zhang, Zhihan and
Wang, Shuohang and
Yu, Wenhao and
Xu, Yichong and
Iter, Dan and
Zeng, Qingkai and
Liu, Yang and
Zhu, Chenguang and
Jiang, Meng",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.659",
doi = "10.18653/v1/2023.findings-emnlp.659",
pages = "9850--9867",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Auto-Instruct: Automatic Instruction Generation and Ranking for Black-Box Language Models
%A Zhang, Zhihan
%A Wang, Shuohang
%A Yu, Wenhao
%A Xu, Yichong
%A Iter, Dan
%A Zeng, Qingkai
%A Liu, Yang
%A Zhu, Chenguang
%A Jiang, Meng
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F zhang-etal-2023-auto
%X 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.
%R 10.18653/v1/2023.findings-emnlp.659
%U https://aclanthology.org/2023.findings-emnlp.659
%U https://doi.org/10.18653/v1/2023.findings-emnlp.659
%P 9850-9867
Markdown (Informal)
[Auto-Instruct: Automatic Instruction Generation and Ranking for Black-Box Language Models](https://aclanthology.org/2023.findings-emnlp.659) (Zhang et al., Findings 2023)
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.