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

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arxiv 2209.01390 v1 pith:E2D47PPQ submitted 2022-09-03 cs.HC cs.CL

classification cs.HCcs.CL
keywords creativefew-shotgenerativelearningmodelspromptingwritingapplications
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 93 citations worldwide. Full citation record

  1. Can LLM Code Explanations Adapt to Diverse Problem-Solvers' Needs?

    cs.SE 2026-07 conditional novelty 6.0 of 10

    LLMs measurably vary code-explanation wording when prompted with different problem-solving styles, yielding a 13-category taxonomy and a model ranking.

  2. Prompt Orchestration Markup Language

    cs.HC 2025-08 conditional novelty 6.0 of 10

    POML is a markup language that structures LLM prompts, embeds multimodal data, and decouples formatting via stylesheets, with case studies showing strong prompt format sensitivity.

  3. MoGraphGPT: Creating Interactive Scenes Using Modular LLM and Graphical Control

    cs.HC 2025-02 conditional novelty 6.0 of 10

    An interactive no-code system combining element-level modular LLM sessions, drawing-based graphical proxies, and automatic sliders creates 2D scenes faster than Cursor Composer in a 10-participant study.

  4. Workflow-Based Evaluation of Music Generation Systems

    eess.AS 2025-06 conditional novelty 5.0 of 10

    A single-producer workflow evaluation of eight music AI tools finds they work as idea and sound generators but not as complete composers, and proposes a reusable framework.

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