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Wordflow: Social Prompt Engineering for Large Language Models (2401.14447v1)

Published 25 Jan 2024 in cs.HC, cs.AI, cs.CL, and cs.LG

Abstract: 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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"Wordflow: Social Prompt Engineering for LLMs" introduces a novel approach called social prompt engineering, aimed at making the process of designing prompts for LLMs accessible to non-experts. Prompt engineering is critical for eliciting the desired responses from LLMs, but it is often seen as complex and esoteric, necessitating a deep understanding of AI technologies. This paper addresses this problem by harnessing social computing techniques to facilitate collaborative prompt design.

Wordflow, an open-source social text editor developed as part of this research, serves as a practical tool that embodies this new paradigm. The platform is designed with the idea that laypeople should be able to easily create, test, share, and discover prompts for LLMs. Wordflow incorporates several key features that make it accessible and user-friendly:

  1. Collaborative Environment: Wordflow employs social computing to enable users to work together on prompt design. This collaborative aspect is essential for harnessing collective intelligence and democratizing the process of prompt engineering.
  2. Ease of Use: By leveraging a familiar text editing interface, Wordflow lowers the entry barrier for non-experts. Users can intuitively interact with the application without needing a deep understanding of the underlying AI technologies.
  3. Local Execution: One of the standout features of Wordflow is its ability to run LLMs locally and privately within the user's web browser. This is particularly significant as it addresses privacy concerns and eliminates the need for continuous internet connectivity, making the tool more versatile.

The paper also highlights two specific usage scenarios to demonstrate the efficacy of social prompt engineering and the Wordflow tool:

  • Scenario 1: Everyday users collaboratively designing prompts for a common task, such as drafting a business proposal. This scenario underscores how shared knowledge and diverse perspectives can lead to more effective prompts.
  • Scenario 2: Educators and students using Wordflow as an educational tool to explore and understand how LLMs interpret different prompts. This scenario illustrates the tool’s potential for enhancing learning and engagement in educational settings.

Ultimately, "Wordflow: Social Prompt Engineering for LLMs" makes a compelling case for leveraging social computing to democratize the process of prompt engineering. By making this process accessible to non-experts, it broadens the potential user base for LLMs and fosters innovative applications across various domains. Wordflow stands out not only for its technical capabilities but also for its focus on inclusivity and usability in AI interactions.

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Authors (4)
  1. Zijie J. Wang (39 papers)
  2. Aishwarya Chakravarthy (4 papers)
  3. David Munechika (6 papers)
  4. Duen Horng Chau (109 papers)
Citations (8)
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