@inproceedings{wang-etal-2024-wordflow,
title = "Wordflow: Social Prompt Engineering for Large Language Models",
author = "Wang, Zijie and
Chakravarthy, Aishwarya and
Munechika, David and
Chau, Duen Horng",
editor = "Cao, Yixin and
Feng, Yang and
Xiong, Deyi",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-demos.5",
doi = "10.18653/v1/2024.acl-demos.5",
pages = "42--50",
abstract = "Large language models (LLMs) require well-crafted prompts for effective use. Prompt engineering, the process of designing prompts, is challenging, particularly for non-experts who are less familiar with AI technologies. While researchers have proposed techniques and tools to assist LLM users in prompt design, these works primarily target AI application developers rather than non-experts. To address this research gap, we propose social prompt engineering, a novel paradigm that leverages social computing techniques to facilitate collaborative prompt design. To investigate social prompt engineering, we introduce Wordflow, an open-source and social text editor that enables everyday users to easily create, run, share, and discover LLM prompts. Additionally, by leveraging modern web technologies, Wordflow allows users to run LLMs locally and privately in their browsers. Two usage scenarios highlight how social prompt engineering and our tool can enhance laypeople{'}s interaction with LLMs. Wordflow is publicly accessible at https://poloclub.github.io/wordflow.",
}
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<abstract>Large language models (LLMs) require well-crafted prompts for effective use. Prompt engineering, the process of designing prompts, is challenging, particularly for non-experts who are less familiar with AI technologies. While researchers have proposed techniques and tools to assist LLM users in prompt design, these works primarily target AI application developers rather than non-experts. To address this research gap, we propose social prompt engineering, a novel paradigm that leverages social computing techniques to facilitate collaborative prompt design. To investigate social prompt engineering, we introduce Wordflow, an open-source and social text editor that enables everyday users to easily create, run, share, and discover LLM prompts. Additionally, by leveraging modern web technologies, Wordflow allows users to run LLMs locally and privately in their browsers. Two usage scenarios highlight how social prompt engineering and our tool can enhance laypeople’s interaction with LLMs. Wordflow is publicly accessible at https://poloclub.github.io/wordflow.</abstract>
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%0 Conference Proceedings
%T Wordflow: Social Prompt Engineering for Large Language Models
%A Wang, Zijie
%A Chakravarthy, Aishwarya
%A Munechika, David
%A Chau, Duen Horng
%Y Cao, Yixin
%Y Feng, Yang
%Y Xiong, Deyi
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F wang-etal-2024-wordflow
%X Large language models (LLMs) require well-crafted prompts for effective use. Prompt engineering, the process of designing prompts, is challenging, particularly for non-experts who are less familiar with AI technologies. While researchers have proposed techniques and tools to assist LLM users in prompt design, these works primarily target AI application developers rather than non-experts. To address this research gap, we propose social prompt engineering, a novel paradigm that leverages social computing techniques to facilitate collaborative prompt design. To investigate social prompt engineering, we introduce Wordflow, an open-source and social text editor that enables everyday users to easily create, run, share, and discover LLM prompts. Additionally, by leveraging modern web technologies, Wordflow allows users to run LLMs locally and privately in their browsers. Two usage scenarios highlight how social prompt engineering and our tool can enhance laypeople’s interaction with LLMs. Wordflow is publicly accessible at https://poloclub.github.io/wordflow.
%R 10.18653/v1/2024.acl-demos.5
%U https://aclanthology.org/2024.acl-demos.5
%U https://doi.org/10.18653/v1/2024.acl-demos.5
%P 42-50
Markdown (Informal)
[Wordflow: Social Prompt Engineering for Large Language Models](https://aclanthology.org/2024.acl-demos.5) (Wang et al., ACL 2024)
ACL
- Zijie Wang, Aishwarya Chakravarthy, David Munechika, and Duen Horng Chau. 2024. Wordflow: Social Prompt Engineering for Large Language Models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 42–50, Bangkok, Thailand. Association for Computational Linguistics.