Suggestion Lists vs. Continuous Generation: Interaction Design for Writing with Generative Models on Mobile Devices Affect Text Length, Wording and Perceived Authorship

• URL: http://arxiv.org/abs/2208.00870v1
• Date: Mon, 1 Aug 2022 13:57:11 GMT
• Title: Suggestion Lists vs. Continuous Generation: Interaction Design for Writing with Generative Models on Mobile Devices Affect Text Length, Wording and Perceived Authorship
• Authors: Florian Lehmann, Niklas Markert, Hai Dang, Daniel Buschek
• Abstract summary: 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.
• Score: 27.853155569154705
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Neural language models have the potential to support human writing. However, questions remain on their integration and influence on writing and output. To address this, we designed and compared two user interfaces for writing with AI on mobile devices, which manipulate levels of initiative and control: 1) Writing with continuously generated text, the AI adds text word-by-word and user steers. 2) Writing with suggestions, the AI suggests phrases and user selects from a list. In a supervised online study (N=18), participants used these prototypes and a baseline without AI. We collected touch interactions, ratings on inspiration and authorship, and interview data. With AI suggestions, people wrote less actively, yet felt they were the author. Continuously generated text reduced this perceived authorship, yet increased editing behavior. In both designs, AI increased text length and was perceived to influence wording. Our findings add new empirical evidence on the impact of UI design decisions on user experience and output with co-creative systems.

Related papers

• Towards Full Authorship with AI: Supporting Revision with AI-Generated Views [3.109675063162349]
  Large language models (LLMs) are shaping a new user interface (UI) paradigm in writing tools by enabling users to generate text through prompts. This paradigm shifts some creative control from the user to the system, thereby diminishing the user's authorship and autonomy in the writing process. We introduce Textfocals, a prototype designed to investigate a human-centered approach that emphasizes the user's role in writing.
  arXiv  Detail & Related papers  (2024-03-02T01:11:35Z)
• UltraFeedback: Boosting Language Models with Scaled AI Feedback [99.4633351133207]
  We present textscUltraFeedback, a large-scale, high-quality, and diversified AI feedback dataset. Our work validates the effectiveness of scaled AI feedback data in constructing strong open-source chat language models.
  arXiv  Detail & Related papers  (2023-10-02T17:40:01Z)
• 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)
• ConvXAI: Delivering Heterogeneous AI Explanations via Conversations to Support Human-AI Scientific Writing [45.187790784934734]
  This paper focuses on Conversational XAI for AI-assisted scientific writing tasks. We identify four design rationales: "multifaceted", "controllability", "mix-initiative", "context-aware drill-down" We incorporate them into an interactive prototype, ConvXAI, which facilitates heterogeneous AI explanations for scientific writing through dialogue.
  arXiv  Detail & Related papers  (2023-05-16T19:48:49Z)
• AI, write an essay for me: A large-scale comparison of human-written versus ChatGPT-generated essays [66.36541161082856]
  ChatGPT and similar generative AI models have attracted hundreds of millions of users. This study compares human-written versus ChatGPT-generated argumentative student essays.
  arXiv  Detail & Related papers  (2023-04-24T12:58:28Z)
• Exploring AI-Generated Text in Student Writing: How Does AI Help? [0.0]
  It remains unclear to what extent AI-generated text in these students' writing might lead to higher-quality writing. We explored 23 Hong Kong secondary school students' attempts to write stories comprising their own words and AI-generated text.
  arXiv  Detail & Related papers  (2023-03-10T14:36:47Z)
• The AI Ghostwriter Effect: When Users Do Not Perceive Ownership of AI-Generated Text But Self-Declare as Authors [42.72188284211033]
  We investigate authorship and ownership in human-AI collaboration for personalized language generation. We show an AI Ghostwriter Effect: Users do not consider themselves the owners and authors of AI-generated text. We discuss how our findings relate to psychological ownership and human-AI interaction to lay the foundations for adapting authorship frameworks.
  arXiv  Detail & Related papers  (2023-03-06T16:53:12Z)
• 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)
• Effidit: Your AI Writing Assistant [60.588370965898534]
  Effidit is a digital writing assistant that facilitates users to write higher-quality text more efficiently by using artificial intelligence (AI) technologies. In Effidit, we significantly expand the capacities of a writing assistant by providing functions in five categories: text completion, error checking, text polishing, keywords to sentences (K2S), and cloud input methods (cloud IME)
  arXiv  Detail & Related papers  (2022-08-03T02:24:45Z)
• Youling: an AI-Assisted Lyrics Creation System [72.00418962906083]
  This paper demonstrates textitYouling, an AI-assisted lyrics creation system, designed to collaborate with music creators. In the lyrics generation process, textitYouling supports traditional one pass full-text generation mode as well as an interactive generation mode. The system also provides a revision module which enables users to revise undesired sentences or words of lyrics repeatedly.
  arXiv  Detail & Related papers  (2022-01-18T03:57:04Z)

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.