Approach Intelligent Writing Assistants Usability with Seven Stages of Action

• URL: http://arxiv.org/abs/2304.02822v1
• Date: Thu, 6 Apr 2023 02:11:55 GMT
• Title: Approach Intelligent Writing Assistants Usability with Seven Stages of Action
• Authors: Avinash Bhat, Disha Shrivastava, Jin L.C. Guo
• Abstract summary: We adopt Norman's seven stages of action as a framework to approach the interaction design of intelligent writing assistants. We illustrate the framework's applicability to writing tasks by providing an example of software tutorial authoring.
• Score: 9.378355457555319
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite the potential of Large Language Models (LLMs) as writing assistants, they are plagued by issues like coherence and fluency of the model output, trustworthiness, ownership of the generated content, and predictability of model performance, thereby limiting their usability. In this position paper, we propose to adopt Norman's seven stages of action as a framework to approach the interaction design of intelligent writing assistants. We illustrate the framework's applicability to writing tasks by providing an example of software tutorial authoring. The paper also discusses the framework as a tool to synthesize research on the interaction design of LLM-based tools and presents examples of tools that support the stages of action. Finally, we briefly outline the potential of a framework for human-LLM interaction research.

Related papers

• WorkArena++: Towards Compositional Planning and Reasoning-based Common Knowledge Work Tasks [85.95607119635102]
  Large language models (LLMs) can mimic human-like intelligence. WorkArena++ is designed to evaluate the planning, problem-solving, logical/arithmetic reasoning, retrieval, and contextual understanding abilities of web agents.
  arXiv  Detail & Related papers  (2024-07-07T07:15:49Z)
• LangSuitE: Planning, Controlling and Interacting with Large Language Models in Embodied Text Environments [70.91258869156353]
  We introduce LangSuitE, a versatile and simulation-free testbed featuring 6 representative embodied tasks in textual embodied worlds. Compared with previous LLM-based testbeds, LangSuitE offers adaptability to diverse environments without multiple simulation engines. We devise a novel chain-of-thought (CoT) schema, EmMem, which summarizes embodied states w.r.t. history information.
  arXiv  Detail & Related papers  (2024-06-24T03:36:29Z)
• Towards a copilot in BIM authoring tool using a large language model-based agent for intelligent human-machine interaction [0.40964539027092917]
  Designers often seek to interact with the software in a more intelligent and lightweight manner. We propose an autonomous agent framework that can function as a copilot in the BIM authoring tool. In a case study based on the BIM authoring software Vectorworks, we implemented a software prototype to integrate the proposed framework seamlessly.
  arXiv  Detail & Related papers  (2024-06-02T17:47:57Z)
• CELA: Cost-Efficient Language Model Alignment for CTR Prediction [71.85120354973073]
  Click-Through Rate (CTR) prediction holds a paramount position in recommender systems. Recent efforts have sought to mitigate these challenges by integrating Pre-trained Language Models (PLMs) We propose textbfCost-textbfEfficient textbfLanguage Model textbfAlignment (textbfCELA) for CTR prediction.
  arXiv  Detail & Related papers  (2024-05-17T07:43:25Z)
• Fine-Tuning Large Vision-Language Models as Decision-Making Agents via Reinforcement Learning [79.38140606606126]
  We propose an algorithmic framework that fine-tunes vision-language models (VLMs) with reinforcement learning (RL) Our framework provides a task description and then prompts the VLM to generate chain-of-thought (CoT) reasoning. We demonstrate that our proposed framework enhances the decision-making capabilities of VLM agents across various tasks.
  arXiv  Detail & Related papers  (2024-05-16T17:50:19Z)
• Evaluating Large Language Model Creativity from a Literary Perspective [13.672268920902187]
  This paper assesses the potential for large language models to serve as assistive tools in the creative writing process. We develop interactive and multi-voice prompting strategies that interleave background descriptions, instructions that guide composition, samples of text in the target style, and critical discussion of the given samples.
  arXiv  Detail & Related papers  (2023-11-30T16:46:25Z)
• Creativity Support in the Age of Large Language Models: An Empirical Study Involving Emerging Writers [33.3564201174124]
  We investigate the utility of modern large language models in assisting professional writers via an empirical user study. We find that while writers seek LLM's help across all three types of cognitive activities, they find LLMs more helpful in translation and reviewing.
  arXiv  Detail & Related papers  (2023-09-22T01:49:36Z)
• TPTU: Large Language Model-based AI Agents for Task Planning and Tool Usage [28.554981886052953]
  Large Language Models (LLMs) have emerged as powerful tools for various real-world applications. Despite their prowess, intrinsic generative abilities of LLMs may prove insufficient for handling complex tasks. This paper proposes a structured framework tailored for LLM-based AI Agents.
  arXiv  Detail & Related papers  (2023-08-07T09:22:03Z)
• VISAR: A Human-AI Argumentative Writing Assistant with Visual Programming and Rapid Draft Prototyping [13.023911633052482]
  VISAR is an AI-enabled writing assistant system designed to help writers brainstorm and revise hierarchical goals within their writing context. It organizes argument structures through synchronized text editing and visual programming, and enhances persuasiveness with argumentation spark recommendations. A controlled lab study confirmed the usability and effectiveness of VISAR in facilitating the argumentative writing planning process.
  arXiv  Detail & Related papers  (2023-04-16T15:29:03Z)
• CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society [58.04479313658851]
  This paper explores the potential of building scalable techniques to facilitate autonomous cooperation among communicative agents. We propose a novel communicative agent framework named role-playing. Our contributions include introducing a novel communicative agent framework, offering a scalable approach for studying the cooperative behaviors and capabilities of multi-agent systems.
  arXiv  Detail & Related papers  (2023-03-31T01:09:00Z)
• 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)

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.