mlsanityData to 14 Oct 2026

How to Prompt? Opportunities and Challenges of Zero- and Few-Shot Learning for Human-AI Interaction in Creative Applications of Generative Models

arXiv 2209.01390 ↗First posted 3 Sep 2022cs.HC cs.CLcited by 44

Not on the map. Why?

Abstract (arXiv)

Deep generative models have the potential to fundamentally change the way we create high-fidelity digital content but are often hard to control. Prompting a generative model is a promising recent development that in principle enables end-users to creatively leverage zero-shot and few-shot learning to assign new tasks to an AI ad-hoc, simply by writing them down. However, for the majority of end-users writing effective prompts is currently largely a trial and error process. To address this, we discuss the key opportunities and challenges for interactive creative applications that use prompting as a new paradigm for Human-AI interaction. Based on our analysis, we propose four design goals for user interfaces that support prompting. We illustrate these with concrete UI design sketches, focusing on the use case of creative writing. The research community in HCI and AI can take these as starting points to develop adequate user interfaces for models capable of zero- and few-shot learning.

Key facts. Cited by 44 papers in the map (data through 14 Oct 2026). Builds on 17 of its 24 references that are in the map. First posted on arXiv 3 Sep 2022.

Builds on

17 of 24 references.

References · 17

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2021Evaluating Large Language Models Trained on Codecited by 8,320
  3. 2021Zero-Shot Text-to-Image Generationcited by 4,288
  4. 2021Prefix-Tuning: Optimizing Continuous Prompts for Generationcited by 3,915
  5. 2021Program Synthesis with Large Language Modelscited by 3,637
  6. 2020Longformer: The Long-Document Transformercited by 3,413
  7. 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,234
  8. 2021Multitask Prompted Training Enables Zero-Shot Task Generalizationcited by 1,470
  9. 2021What Makes Good In-Context Examples for GPT-3?cited by 1,195
  10. 2021Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivitycited by 1,135
  11. 2020Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inferencecited by 938
  12. 2021AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Promptscited by 268
5 more
  1. 2020AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Promptscited by 249
  2. 2018Sharp Nearby, Fuzzy Far Away: How Neural Language Models Use Contextcited by 113
  3. 2021What Context Features Can Transformer Language Models Use?cited by 50
  4. 2021SynthBio: A Case Study in Human-AI Collaborative Curation of Text Datasetscited by 18
  5. 2021Thinking Aloud: Dynamic Context Generation Improves Zero-Shot Reasoning Performance of GPT-2cited by 15

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 10; 2024: 14; 2025: 14; 2026: 5.

44 papers.

Sort:

By area:

  1. 2022Enabling Conversational Interaction with Mobile UI using Large Language Modelscited by 66
  2. 2023Mental-LLM: Leveraging Large Language Models for Mental Health Prediction via Online Text Datacited by 66
  3. 2024When Search Engine Services meet Large Language Models: Visions and Challengescited by 59
  4. 2023Rehearsal: Simulating Conflict to Teach Conflict Resolutioncited by 55
  5. 2023CoQuest: Exploring Research Question Co-Creation with an LLM-based Agentcited by 34
  6. 2024Bringing Generative AI to Adaptive Learning in Educationcited by 24
  7. 2023From Google Gemini to OpenAI Q* (Q-Star): A Survey of Reshaping the Generative Artificial Intelligence (AI) Research Landscapecited by 18
  8. 2023"It Felt Like Having a Second Mind": Investigating Human-AI Co-creativity in Prewriting with Large Language Modelscited by 17
  9. 2024LLM Whisperer: An Inconspicuous Attack to Bias LLM Responsescited by 15
  10. 2025Zero-shot and Few-shot Learning with Instruction-following LLMs for Claim Matching in Automated Fact-checkingcited by 12
  11. 2024Exploring Prompt Engineering Practices in the Enterprisecited by 11
  12. 2023An Evaluation of ChatGPT-4's Qualitative Spatial Reasoning Capabilities in RCC-8cited by 9
32 more
  1. 2024Laboratory-Scale AI: Open-Weight Models are Competitive with ChatGPT Even in Low-Resource Settingscited by 9
  2. 2024A Human-Computer Collaborative Tool for Training a Single Large Language Model Agent into a Network through Few Examplescited by 5
  3. 2024Can Large Language Models Reason about the Region Connection Calculus?cited by 5
  4. 2025Collaborative Document Editing with Multiple Users and AI Agentscited by 5
  5. 2024Large Language Models Meet User Interfaces: The Case of Provisioning Feedbackcited by 4
  6. 2024Large Language Model Agents for Improving Engagement with Behavior Change Interventions: Application to Digital Mindfulnesscited by 4
  7. 2023Trash to Treasure: Using text-to-image models to inform the design of physical artefactscited by 3
  8. 2023Exploring EFL students' prompt engineering in human-AI story writing: an Activity Theory perspectivecited by 3
  9. 2023Cases of EFL Secondary Students' Prompt Engineering Pathways to Complete a Writing Task with ChatGPTcited by 2
  10. 2025Use Me Wisely: AI-Driven Assessment for LLM Prompting Skills Developmentcited by 2
  11. 2025Large Language Models for Oral History Understanding with Text Classification and Sentiment Analysiscited by 2
  12. 2026"If You're Very Clever, No One Knows You've Used It": The Social Dynamics of Developing Generative AI Literacy in the Workplacecited by 2
  13. 2023Large Language Models Are Also Good Prototypical Commonsense Reasonerscited by 1
  14. 2024Offline Evaluation of Set-Based Text-to-Image Generationcited by 1
  15. 2024Generative AI in Medicinecited by 1
  16. 2025An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Promptscited by 1
  17. 2025Prompt Orchestration Markup Languagecited by 1
  18. 2025Synthetic Cognitive Walkthrough: Aligning Large Language Model Performance with Human Cognitive Walkthroughcited by 1
  19. 2024Selection of Prompt Engineering Techniques for Code Generation through Predicting Code Complexitycited by 0
  20. 2024Task-oriented Prompt Enhancement via Script Generationcited by 0
  21. 2024Key Algorithms for Keyphrase Generation: Instruction-Based LLMs for Russian Scientific Keyphrasescited by 0
  22. 2025Actionable AI: Enabling Non Experts to Understand and Configure AI Systemscited by 0
  23. 2025Automated Meta Prompt Engineering for Alignment with the Theory of Mindcited by 0
  24. 2025"I made this (sort of)": Negotiating authorship, confronting fraudulence, and exploring new musical spaces with prompt-based AI music generationcited by 0
  25. 2025Match Chat: Real Time Generative AI and Generative Computing for Tenniscited by 0
  26. 2025PromptPilot: Improving Human-AI Collaboration Through LLM-Enhanced Prompt Engineeringcited by 0
  27. 2025PustakAI: Curriculum-Aligned and Interactive Textbooks Using Large Language Modelscited by 0
  28. 2025AutoICE: Automatically Synthesizing Verifiable C Code via LLM-driven Evolutioncited by 0
  29. 2026Rewriting Video: Text-Driven Reauthoring of Video Footagecited by 0
  30. 2026I wanted it to feel more personal: Customization of social AI as AI individualism in practicecited by 0
  31. 2026Beyond the Payload: How User Invocation Shapes Coding Agent Vulnerability to Repository Poisoningcited by 0
  32. 2026Rethinking Opinion Measurement Interfaces for Human-in-the-Loop Optimizationcited by 0