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Writer-Defined AI Personas for On-Demand Feedback Generation
This is challenging, as writers may struggle to empathize with readers, get feedback in time, or gain access to the target group.
The AI Ghostwriter Effect: When Users Do Not Perceive Ownership of AI-Generated Text But Self-Declare as Authors
Participants were more likely to attribute ownership to supposedly human ghostwriters than AI ghostwriters, resulting in a higher ownership-authorship discrepancy for human ghostwriters.
Choice Over Control: How Users Write with Large Language Models using Diegetic and Non-Diegetic Prompting
We propose a conceptual perspective on prompts for Large Language Models (LLMs) that distinguishes between (1) diegetic prompts (part of the narrative, e. g. "Once upon a time, I saw a fox..."), and (2) non-diegetic prompts (external, e. g. "Write about the adventures of the fox.").
How to Prompt? Opportunities and Challenges of Zero- and Few-Shot Learning for Human-AI Interaction in Creative Applications of Generative Models
Deep generative models have the potential to fundamentally change the way we create high-fidelity digital content but are often hard to control.
Beyond Text Generation: Supporting Writers with Continuous Automatic Text Summaries
As a key finding, the summaries gave users an external perspective on their writing and helped them to revise the content and scope of their drafted paragraphs.
Suggestion Lists vs. Continuous Generation: Interaction Design for Writing with Generative Models on Mobile Devices Affect Text Length, Wording and Perceived Authorship
2) Writing with suggestions, the AI suggests phrases and user selects from a list.
Nine Potential Pitfalls when Designing Human-AI Co-Creative Systems
This position paper examines potential pitfalls on the way towards achieving human-AI co-creation with generative models in a way that is beneficial to the users' interests.
