HELP ME THINK: A Simple Prompting Strategy for Non-experts to Create Customized Content with Models

Swaroop Mishra, Elnaz Nouri


Abstract
Controlling the text generated by language models and customizing the content has been a long-standing challenge. Existing prompting techniques proposed in pursuit of providing control are task-specific and lack generality; this provides overwhelming choices for non-expert users to find a suitable method for their task. The effort associated with those techniques, such as in writing examples, explanations, instructions, etc. further limits their adoption among non-expert users. In this paper, we propose a simple prompting strategy Help Me Think where we encourage largelanguage models (such as GPT3 and ChatGPT) to help non-expert users by asking a set of relevant questions and leveraging user answers to execute the task. We demonstrate the efficacy of our technique Help Me Think on a variety of tasks. Specifically, we focus on tasks that are hard for average humans and require significant thinking to perform. We hope our work will encourage the development of unconventional ways to harness the power of large language models.
Anthology ID:
2023.findings-acl.751
Volume:
Findings of the Association for Computational Linguistics: ACL 2023
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
11834–11890
Language:
URL:
https://aclanthology.org/2023.findings-acl.751
DOI:
10.18653/v1/2023.findings-acl.751
Bibkey:
Cite (ACL):
Swaroop Mishra and Elnaz Nouri. 2023. HELP ME THINK: A Simple Prompting Strategy for Non-experts to Create Customized Content with Models. In Findings of the Association for Computational Linguistics: ACL 2023, pages 11834–11890, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
HELP ME THINK: A Simple Prompting Strategy for Non-experts to Create Customized Content with Models (Mishra & Nouri, Findings 2023)
Copy Citation:
PDF:
https://aclanthology.org/2023.findings-acl.751.pdf