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

arXiv: https://arxiv.org/abs/2209.01390 · HTML: https://mlsanity.com/paper/2209.01390/

## 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 (most cited first, 17 of 17)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [Program Synthesis with Large Language Models](https://mlsanity.com/paper/2108.07732/) (2021)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://mlsanity.com/paper/2110.08207/) (2021)
- [What Makes Good In-Context Examples for GPT-3?](https://mlsanity.com/paper/2101.06804/) (2021)
- [Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity](https://mlsanity.com/paper/2104.08786/) (2021)
- [Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference](https://mlsanity.com/paper/2001.07676/) (2020)
- [AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts](https://mlsanity.com/paper/2110.01691/) (2021)
- [AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts](https://mlsanity.com/paper/2010.15980/) (2020)
- [Sharp Nearby, Fuzzy Far Away: How Neural Language Models Use Context](https://mlsanity.com/paper/1805.04623/) (2018)
- [What Context Features Can Transformer Language Models Use?](https://mlsanity.com/paper/2106.08367/) (2021)
- [SynthBio: A Case Study in Human-AI Collaborative Curation of Text Datasets](https://mlsanity.com/paper/2111.06467/) (2021)
- [Thinking Aloud: Dynamic Context Generation Improves Zero-Shot Reasoning Performance of GPT-2](https://mlsanity.com/paper/2103.13033/) (2021)

## Built on by (most cited first, 30 of 44)

- [Enabling Conversational Interaction with Mobile UI using Large Language Models](https://mlsanity.com/paper/2209.08655/) (2022)
- [Mental-LLM: Leveraging Large Language Models for Mental Health Prediction via Online Text Data](https://mlsanity.com/paper/2307.14385/) (2023)
- [When Search Engine Services meet Large Language Models: Visions and Challenges](https://mlsanity.com/paper/2407.00128/) (2024)
- [Rehearsal: Simulating Conflict to Teach Conflict Resolution](https://mlsanity.com/paper/2309.12309/) (2023)
- [CoQuest: Exploring Research Question Co-Creation with an LLM-based Agent](https://mlsanity.com/paper/2310.06155/) (2023)
- [Bringing Generative AI to Adaptive Learning in Education](https://mlsanity.com/paper/2402.14601/) (2024)
- [From Google Gemini to OpenAI Q* (Q-Star): A Survey of Reshaping the Generative Artificial Intelligence (AI) Research Landscape](https://mlsanity.com/paper/2312.10868/) (2023)
- ["It Felt Like Having a Second Mind": Investigating Human-AI Co-creativity in Prewriting with Large Language Models](https://mlsanity.com/paper/2307.10811/) (2023)
- [LLM Whisperer: An Inconspicuous Attack to Bias LLM Responses](https://mlsanity.com/paper/2406.04755/) (2024)
- [Zero-shot and Few-shot Learning with Instruction-following LLMs for Claim Matching in Automated Fact-checking](https://mlsanity.com/paper/2501.10860/) (2025)
- [Exploring Prompt Engineering Practices in the Enterprise](https://mlsanity.com/paper/2403.08950/) (2024)
- [An Evaluation of ChatGPT-4's Qualitative Spatial Reasoning Capabilities in RCC-8](https://mlsanity.com/paper/2309.15577/) (2023)
- [Laboratory-Scale AI: Open-Weight Models are Competitive with ChatGPT Even in Low-Resource Settings](https://mlsanity.com/paper/2405.16820/) (2024)
- [A Human-Computer Collaborative Tool for Training a Single Large Language Model Agent into a Network through Few Examples](https://mlsanity.com/paper/2404.15974/) (2024)
- [Can Large Language Models Reason about the Region Connection Calculus?](https://mlsanity.com/paper/2411.19589/) (2024)
- [Collaborative Document Editing with Multiple Users and AI Agents](https://mlsanity.com/paper/2509.11826/) (2025)
- [Large Language Models Meet User Interfaces: The Case of Provisioning Feedback](https://mlsanity.com/paper/2404.11072/) (2024)
- [Large Language Model Agents for Improving Engagement with Behavior Change Interventions: Application to Digital Mindfulness](https://mlsanity.com/paper/2407.13067/) (2024)
- [Trash to Treasure: Using text-to-image models to inform the design of physical artefacts](https://mlsanity.com/paper/2302.00561/) (2023)
- [Exploring EFL students' prompt engineering in human-AI story writing: an Activity Theory perspective](https://mlsanity.com/paper/2306.01798/) (2023)
- [Cases of EFL Secondary Students' Prompt Engineering Pathways to Complete a Writing Task with ChatGPT](https://mlsanity.com/paper/2307.05493/) (2023)
- [Use Me Wisely: AI-Driven Assessment for LLM Prompting Skills Development](https://mlsanity.com/paper/2503.02532/) (2025)
- [Large Language Models for Oral History Understanding with Text Classification and Sentiment Analysis](https://mlsanity.com/paper/2508.06729/) (2025)
- ["If You're Very Clever, No One Knows You've Used It": The Social Dynamics of Developing Generative AI Literacy in the Workplace](https://mlsanity.com/paper/2602.01386/) (2026)
- [Large Language Models Are Also Good Prototypical Commonsense Reasoners](https://mlsanity.com/paper/2309.13165/) (2023)
- [Offline Evaluation of Set-Based Text-to-Image Generation](https://mlsanity.com/paper/2410.17331/) (2024)
- [Generative AI in Medicine](https://mlsanity.com/paper/2412.10337/) (2024)
- [An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts](https://mlsanity.com/paper/2501.12521/) (2025)
- [Prompt Orchestration Markup Language](https://mlsanity.com/paper/2508.13948/) (2025)
- [Synthetic Cognitive Walkthrough: Aligning Large Language Model Performance with Human Cognitive Walkthrough](https://mlsanity.com/paper/2512.03568/) (2025)

## 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.