How to Prompt? Opportunities and Challenges of Zero- and Few-Shot Learning for Human-AI Interaction in Creative Applications of Generative Models (2209.01390v1)

Abstract: 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.

• The paper demonstrates that prompt engineering with zero- and few-shot techniques can democratize access to creative AI for non-expert users.
• It shows that interactive, modular prompt interfaces significantly enhance task-specific outcomes and user collaboration in generative models.
• The study identifies challenges such as iterative trial-and-error, computational delays, and ethical issues that must be tackled for optimal human-AI interaction.

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

Introduction

The paper titled "How to Prompt? Opportunities and Challenges of Zero- and Few-Shot Learning for Human-AI Interaction in Creative Applications of Generative Models" explores how deep generative models can be repurposed for creative tasks through prompting. The prominence of zero-shot and few-shot learning has markedly shifted how task-specific AI models are utilized, emphasizing user-written prompts without the need for conventional retraining processes. This paper explores the interaction paradigms, especially in creative writing, and evaluates the potential and hurdles associated with leveraging prompts for human-AI co-creation.

Interactive Applications and Prompt Engineering

Recent interactive systems, such as AI Chains, demonstrate the necessity for sequential and modular use of LLMs to solve complex tasks, moving beyond individual one-shot interactions. Projects like Story Centaur demonstrate graphical interfaces that help users construct effective prompts by providing structural guidance and pre-defined phrases. Such platforms underscore the evolving landscape where few-shot learning can be applied in user-facing applications without requiring profound technical expertise.

Prompt engineering, a systematic method for optimizing prompt efficacy, continues to evolve with strategies like modifying word order for improved outcomes or using automatic prompt generation tools like AutoPrompt. These advancements suggest that even non-expert users can significantly harness LLM capabilities with correct interfacing.

Figure 1: In this GUI example, users enter a prompt in natural language and the system automatically parses this input. Key parameters such as detected task and task parameters can thus be edited directly. Then, the system automatically creates the (refined) text prompt for the generative model.

Opportunities in Prompting for Creative Applications

Prompting allows users, particularly those without technical backgrounds, to define, refine, and operate AI-driven tools through natural language. This both democratizes access to generative systems and enhances real-time user collaboration in creative processes. The paper identifies several opportunities arising from this paradigm shift:

• End-User Programming: Prompts can be crafted and reused across contexts, empowering users to develop personalized generative tools.
• Extension of Creative Expression: By employing multimodal prompts, users can explore styles and synthesize diverse creative forms without deep technical understanding.
• Inspiration and Feedback: AI-driven content generation via prompts can help overcome creative blocks by providing new perspectives, simulated feedback, and rapid iteration paths.

  Figure 2: In this example GUI, users create a prompt not by writing from scratch but by selecting from predefined ``building blocks'' that have been proven to work well and cover typical use case-specific aspects. Free entry is still supported as well.

Challenges and Systematic Prompting Support

Despite the potential, the trial-and-error nature of current prompting systems creates challenges. Most users struggle with lack of systematic guidance, and prompts often require iterative, exploratory approaches without guaranteed outcomes. This necessitates interfaces that can abstract commonly effective prompt patterns for everyday users.

Moreover, the interaction might be hindered by computational latency and resource-intensive requirements. As users rely on prompts to drive creative outcomes, time delays can disrupt user experience and workflow continuity. Ethical concerns relating to bias in model outputs and accessibility gaps also represent pivotal areas for ongoing research and interface design adjustments.

Figure 3: This example GUI focuses on prompt exploration and combination: Users write prompts to direct a ``narrative tree'' showing multiple possible responses to each prompt. Users select some of them as context for the next prompt(s), which direct the narrative further.

Designing Supportive UIs for Creative Prompting

The authors propose several design goals for creating user interfaces that effectively support prompting in creative applications:

• Efficient Prompt Formulation: Interfaces should facilitate easy drafting and refinement of prompts through guided detection and template selection mechanisms.
• Prompt Combination and Exploration: Tools should enable users to experiment with various narrative directions or prompt interactions, allowing for iterative creative exploration.
• Application and Integration: Interfaces must seamlessly integrate prompt execution in interactive environments to leverage prompts without disrupting users’ workflows.
• Prompt Representation and Interaction: Effective visuals and symbolic representations are necessary to intuitively convey prompt functionalities and outcomes within GUIs.

  Figure 4: This GUI example supports users in applying prompts by enabling them to write and save prompts as tools in a toolbar.

Conclusion

Prompts represent a significant shift in enabling real-time, versatile user interaction with generative AI models, particularly in creative domains. By addressing the outlined challenges and capitalizing on emergent opportunities, prompting can bolster human creativity, enabling dynamic interaction with AI. Future work should focus on refining user interfaces to ensure they are conducive to effective prompting while maintaining accessibility and ethical integrity. Through collaborative and interactive design strategies, the potential of generative models as user-driven creative agents can be further realized.