Wordcraft: a Human-AI Collaborative Editor for Story Writing

Abstract

As neural language models grow in effectiveness, they are increasingly being applied in real-world settings. However these applications tend to be limited in the modes of interaction they support. In this extended abstract, we propose Wordcraft, an AI-assisted editor for story writing in which a writer and a dialog system collaborate to write a story. Our novel interface uses few-shot learning and the natural affordances of conversation to support a variety of interactions. Our editor provides a sandbox for writers to probe the boundaries of transformer-based language models and paves the way for future human-in-the-loop training pipelines and novel evaluation methods.

References in corpus (6)

• Language Models are Few-Shot Learners
• CTRL: A Conditional Transformer Language Model for Controllable Generation
• Prefix-Tuning: Optimizing Continuous Prompts for Generation
• Extracting Training Data from Large Language Models
• Towards a Human-like Open-Domain Chatbot
• Hierarchical Neural Story Generation

Cited by in corpus (4)

• LaMPost: Design and Evaluation of an AI-assisted Email Writing Prototype for Adults with Dyslexia
• Suggestion Lists vs. Continuous Generation: Interaction Design for Writing with Generative Models on Mobile Devices Affect Text Length, Wording and Perceived Authorship
• CALYPSO: LLMs as Dungeon Masters' Assistants
• Read, Revise, Repeat: A System Demonstration for Human-in-the-loop Iterative Text Revision