Related papers: Wordcraft: a Human-AI Collaborative Editor for Story Writing

Wordcraft: a Human-AI Collaborative Editor for Story Writing

URL: http://arxiv.org/abs/2107.07430v1
Date: Thu, 15 Jul 2021 16:18:27 GMT
Title: Wordcraft: a Human-AI Collaborative Editor for Story Writing
Authors: Andy Coenen, Luke Davis, Daphne Ippolito, Emily Reif, Ann Yuan
Abstract summary: 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.
Score: 10.028560442375914
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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.

Related papers

"It was 80% me, 20% AI": Seeking Authenticity in Co-Writing with Large Language Models [97.22914355737676]
We examine whether and how writers want to preserve their authentic voice when co-writing with AI tools. Our findings illuminate conceptions of authenticity in human-AI co-creation. Readers' responses showed less concern about human-AI co-writing.
arXiv Detail & Related papers (2024-11-20T04:42:32Z)
Crafting Narrative Closures: Zero-Shot Learning with SSM Mamba for Short Story Ending Generation [0.0]
Authors encounter moments of creative block, where the path forward in their narrative becomes obscured. This paper is designed to address such moments by providing an innovative solution: A tool that completes stories based on given prompts. By inputting a short story prompt, users can receive a conclusion to their story, articulated in one sentence or more, thereby enhancing the storytelling process with AI-driven creativity.
arXiv Detail & Related papers (2024-10-04T18:56:32Z)
Agents' Room: Narrative Generation through Multi-step Collaboration [54.98886593802834]
We propose a generation framework inspired by narrative theory that decomposes narrative writing into subtasks tackled by specialized agents. We show that Agents' Room generates stories preferred by expert evaluators over those produced by baseline systems.
arXiv Detail & Related papers (2024-10-03T15:44:42Z)
Interactive Text Generation [75.23894005664533]
We introduce a new Interactive Text Generation task that allows training generation models interactively without the costs of involving real users. We train our interactive models using Imitation Learning, and our experiments against competitive non-interactive generation models show that models trained interactively are superior to their non-interactive counterparts.
arXiv Detail & Related papers (2023-03-02T01:57:17Z)
Creative Writing with an AI-Powered Writing Assistant: Perspectives from Professional Writers [9.120878749348986]
Natural language generation (NLG) using neural language models has brought us closer than ever to the goal of building AI-powered creative writing tools. Recent developments in natural language generation using neural language models have brought us closer than ever to the goal of building AI-powered creative writing tools.
arXiv Detail & Related papers (2022-11-09T17:00:56Z)
PEER: A Collaborative Language Model [70.11876901409906]
We introduce PEER, a collaborative language model that imitates the entire writing process itself. PEER can write drafts, add suggestions, propose edits and provide explanations for its actions. We show that PEER achieves strong performance across various domains and editing tasks.
arXiv Detail & Related papers (2022-08-24T16:56:47Z)
Suggestion Lists vs. Continuous Generation: Interaction Design for Writing with Generative Models on Mobile Devices Affect Text Length, Wording and Perceived Authorship [27.853155569154705]
We present two user interfaces for writing with AI on mobile devices, which manipulate levels of initiative and control. With AI suggestions, people wrote less actively, yet felt they were the author. In both designs, AI increased text length and was perceived to influence wording.
arXiv Detail & Related papers (2022-08-01T13:57:11Z)
Computational Storytelling and Emotions: A Survey [56.95572957863576]
This survey paper is intended to summarize and contribute to the development of research being conducted on the relationship between stories and emotions. We believe creativity research is not to replace humans with computers, but to find a way of collaboration between humans and computers to enhance the creativity.
arXiv Detail & Related papers (2022-05-23T00:21:59Z)
Multiversal views on language models [0.0]
We present a framework in which generative language models are conceptualized as multiverse generators. This framework also applies to human imagination and is core to how we read and write fiction. We call for exploration into this commonality through new forms of interfaces which allow humans to couple their imagination to AI to write.
arXiv Detail & Related papers (2021-02-12T08:28:28Z)
I love your chain mail! Making knights smile in a fantasy game world: Open-domain goal-oriented dialogue agents [69.68400056148336]
We train a goal-oriented model with reinforcement learning against an imitation-learned chit-chat'' model. We show that both models outperform an inverse model baseline and can converse naturally with their dialogue partner in order to achieve goals.
arXiv Detail & Related papers (2020-02-07T16:22:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.