Creative Writing with an AI-Powered Writing Assistant: Perspectives from Professional Writers

• URL: http://arxiv.org/abs/2211.05030v1
• Date: Wed, 9 Nov 2022 17:00:56 GMT
• Title: Creative Writing with an AI-Powered Writing Assistant: Perspectives from Professional Writers
• Authors: Daphne Ippolito, Ann Yuan, Andy Coenen, Sehmon Burnam
• Abstract summary: 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.
• Score: 9.120878749348986
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent developments in natural language generation (NLG) using neural language models have brought us closer than ever to the goal of building AI-powered creative writing tools. However, most prior work on human-AI collaboration in the creative writing domain has evaluated new systems with amateur writers, typically in contrived user studies of limited scope. In this work, we commissioned 13 professional, published writers from a diverse set of creative writing backgrounds to craft stories using Wordcraft, a text editor with built-in AI-powered writing assistance tools. Using interviews and participant journals, we discuss the potential of NLG to have significant impact in the creative writing domain--especially with respect to brainstorming, generation of story details, world-building, and research assistance. Experienced writers, more so than amateurs, typically have well-developed systems and methodologies for writing, as well as distinctive voices and target audiences. Our work highlights the challenges in building for these writers; NLG technologies struggle to preserve style and authorial voice, and they lack deep understanding of story contents. In order for AI-powered writing assistants to realize their full potential, it is essential that they take into account the diverse goals and expertise of human writers.

Related papers

• AI as Humanity's Salieri: Quantifying Linguistic Creativity of Language Models via Systematic Attribution of Machine Text against Web Text [53.15652021126663]
  We present CREATIVITY INDEX as the first step to quantify the linguistic creativity of a text. To compute CREATIVITY INDEX efficiently, we introduce DJ SEARCH, a novel dynamic programming algorithm. Experiments reveal that the CREATIVITY INDEX of professional human authors is on average 66.2% higher than that of LLMs.
  arXiv  Detail & Related papers  (2024-10-05T18:55:01Z)
• 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)
• SARD: A Human-AI Collaborative Story Generation [0.0]
  We propose SARD, a drag-and-drop visual interface for generating a multi-chapter story using large language models. Our evaluation of the usability of SARD and its creativity support shows that while node-based visualization of the narrative may help writers build a mental model, it exerts unnecessary mental overhead to the writer. We also found that AI generates stories that are less lexically diverse, irrespective of the complexity of the story.
  arXiv  Detail & Related papers  (2024-03-03T17:48:42Z)
• GhostWriter: Augmenting Collaborative Human-AI Writing Experiences Through Personalization and Agency [1.7707677585873678]
  Large language models (LLMs) are becoming more prevalent and have found a ubiquitous use in providing different forms of writing assistance. We introduce GhostWriter, an AI-enhanced writing design probe where users can exercise enhanced agency and personalization. We study 18 participants who use GhostWriter on two different writing tasks, observing that it helps users craft personalized text generations and empowers them by providing multiple ways to control the system's writing style.
  arXiv  Detail & Related papers  (2024-02-13T23:48:59Z)
• Writer-Defined AI Personas for On-Demand Feedback Generation [32.19315306717165]
  We propose a concept that generates on-demand feedback, based on writer-defined AI personas of any target audience. This work contributes to the vision of supporting writers with AI by expanding the socio-technical perspective in AI tool design.
  arXiv  Detail & Related papers  (2023-09-19T08:49:35Z)
• The Future of AI-Assisted Writing [0.0]
  We conduct a comparative user-study between such tools from an information retrieval lens: pull and push. Our findings show that users welcome seamless assistance of AI in their writing. Users also enjoyed the collaboration with AI-assisted writing tools and did not feel a lack of ownership.
  arXiv  Detail & Related papers  (2023-06-29T02:46:45Z)
• Visualize Before You Write: Imagination-Guided Open-Ended Text Generation [68.96699389728964]
  We propose iNLG that uses machine-generated images to guide language models in open-ended text generation. Experiments and analyses demonstrate the effectiveness of iNLG on open-ended text generation tasks.
  arXiv  Detail & Related papers  (2022-10-07T18:01:09Z)
• 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)
• CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities [92.79451009324268]
  We present CoAuthor, a dataset designed for revealing GPT-3's capabilities in assisting creative and argumentative writing. We demonstrate that CoAuthor can address questions about GPT-3's language, ideation, and collaboration capabilities. We discuss how this work may facilitate a more principled discussion around LMs' promises and pitfalls in relation to interaction design.
  arXiv  Detail & Related papers  (2022-01-18T07:51:57Z)
• Telling Creative Stories Using Generative Visual Aids [52.623545341588304]
  We asked writers to write creative stories from a starting prompt, and provided them with visuals created by generative AI models from the same prompt. Compared to a control group, writers who used the visuals as story writing aid wrote significantly more creative, original, complete and visualizable stories. Findings indicate that cross modality inputs by AI can benefit divergent aspects of creativity in human-AI co-creation, but hinders convergent thinking.
  arXiv  Detail & Related papers  (2021-10-27T23:13:47Z)
• Wordcraft: a Human-AI Collaborative Editor for Story Writing [10.028560442375914]
  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.
  arXiv  Detail & Related papers  (2021-07-15T16:18:27Z)

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

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.