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    <abstract>“Deploying tuning-free prompting is challenging in engineering practice: it not only requiresusers to engage in cumbersome trials and errors but is also extremely time-consuming,as even a slight change in wording and phrasing could have a huge impact on the finalperformance. To further investigate the impact of different prompts, in this work, weperform a systematic inspection of four factors in linguistics involved in prompt engineering:syntax, semantics, lexicon, and pragmatics. The empirical results quantify the sensitivityof the output to small textual perturbations in four linguistic factors of prompts. Basedon the analysis of these four factors, we present a series of design guidelines to helphuman users write effective prompts. Human evaluation on amateurs shows that usingthe proposed guidelines helps humans produce prompts with significant gains in zero-shotperformance in Pre-trained Language Models (PLMs) and hence validates the utility ofthe guidelines.”</abstract>
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%0 Conference Proceedings
%T Prompt Engineering 101 Prompt Engineering Guidelines from a Linguistic Perspective
%A Han, Wenjuan
%A Wei, Xiang
%A Cui, Xingyu
%A Cheng, Ning
%A Jiang, Guangyuan
%A Qian, Weinan
%A Zhang, Chi
%Y Maosong, Sun
%Y Jiye, Liang
%Y Xianpei, Han
%Y Zhiyuan, Liu
%Y Yulan, He
%S Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)
%D 2024
%8 July
%I Chinese Information Processing Society of China
%C Taiyuan, China
%G eng
%F wenjuan-etal-2024-prompt
%X “Deploying tuning-free prompting is challenging in engineering practice: it not only requiresusers to engage in cumbersome trials and errors but is also extremely time-consuming,as even a slight change in wording and phrasing could have a huge impact on the finalperformance. To further investigate the impact of different prompts, in this work, weperform a systematic inspection of four factors in linguistics involved in prompt engineering:syntax, semantics, lexicon, and pragmatics. The empirical results quantify the sensitivityof the output to small textual perturbations in four linguistic factors of prompts. Basedon the analysis of these four factors, we present a series of design guidelines to helphuman users write effective prompts. Human evaluation on amateurs shows that usingthe proposed guidelines helps humans produce prompts with significant gains in zero-shotperformance in Pre-trained Language Models (PLMs) and hence validates the utility ofthe guidelines.”
%U https://aclanthology.org/2024.ccl-1.108/
%P 1408-1426

Markdown (Informal)

[Prompt Engineering 101 Prompt Engineering Guidelines from a Linguistic Perspective](https://aclanthology.org/2024.ccl-1.108/) (Han et al., CCL 2024)

• Prompt Engineering 101 Prompt Engineering Guidelines from a Linguistic Perspective (Han et al., CCL 2024)

ACL

• Wenjuan Han, Xiang Wei, Xingyu Cui, Ning Cheng, Guangyuan Jiang, Weinan Qian, and Chi Zhang. 2024. Prompt Engineering 101 Prompt Engineering Guidelines from a Linguistic Perspective. In Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference), pages 1408–1426, Taiyuan, China. Chinese Information Processing Society of China.