mlsanityData to 14 Oct 2026

Wordflow: Social Prompt Engineering for Large Language Models

arXiv 2401.14447 ↗First posted 25 Jan 2024cs.HC cs.AI cs.CLcited by 9

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Abstract (arXiv)

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.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 21 of its 67 references that are in the map. First posted on arXiv 25 Jan 2024.

Builds on

21 of 67 references.

References · 21

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2022Training language models to follow instructions with human feedbackcited by 15,971
  3. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  4. 2022Large Language Models are Zero-Shot Reasonerscited by 4,374
  5. 2023Large Language Modelscited by 780
  6. 2024TinyLlama: An Open-Source Small Language Modelcited by 618
  7. 2022DiffusionDB: A Large-scale Prompt Gallery Dataset for Text-to-Image Generative Modelscited by 379
  8. 2023Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicinecited by 329
  9. 2022PromptSource: An Integrated Development Environment and Repository for Natural Language Promptscited by 289
  10. 2021AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Promptscited by 268
  11. 2023Efficient Guided Generation for Large Language Modelscited by 200
  12. 2023Sociotechnical Safety Evaluation of Generative AI Systemscited by 157
9 more
  1. 2022PromptChainer: Chaining Large Language Model Prompts through Visual Programmingcited by 134
  2. 2023ChainForge: A Visual Toolkit for Prompt Engineering and LLM Hypothesis Testingcited by 88
  3. 2022Interactive and Visual Prompt Engineering for Ad-hoc Task Adaptation with Large Language Modelscited by 70
  4. 2022A Taxonomy of Prompt Modifiers for Text-To-Image Generationcited by 55
  5. 2023Prompt Sapper: A LLM-Empowered Production Tool for Building AI Chainscited by 24
  6. 2023Model Compression in Practice: Lessons Learned from Practitioners Creating On-device Machine Learning Experiencescited by 21
  7. 2023PromptAid: Prompt Exploration, Perturbation, Testing and Iteration using Visual Analytics for Large Language Modelscited by 19
  8. 2023CoPrompt: Supporting Prompt Sharing and Referring in Collaborative Natural Language Programmingcited by 16
  9. 2023Revisiting Prompt Engineering via Declarative Crowdsourcingcited by 12

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Citing papers per year: 2024: 6; 2025: 3.

9 papers.