GhostWriter: Augmenting Collaborative Human-AI Writing Experiences Through Personalization and Agency
(2402.08855)Abstract
LLMs are becoming more prevalent and have found a ubiquitous use in providing different forms of writing assistance. However, LLM-powered writing systems can frustrate users due to their limited personalization and control, which can be exacerbated when users lack experience with prompt engineering. We see design as one way to address these challenges and introduce GhostWriter, an AI-enhanced writing design probe where users can exercise enhanced agency and personalization. GhostWriter leverages LLMs to learn the user's intended writing style implicitly as they write, while allowing explicit teaching moments through manual style edits and annotations. 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. From this study, we present insights regarding people's relationship with AI-assisted writing and offer design recommendations for future work.
Overview
-
GhostWriter introduces an innovative approach to AI-assisted writing, focusing on elevating user agency and personalization through the use of LLMs that adapt to users’ preferences.
-
Developed by Harvard University and Microsoft, the study with 18 participants reveals the potential of systems like GhostWriter to offer personalized and controlled writing assistance.
-
Key features of GhostWriter include inline LLM-assisted features, explicit style teaching mechanisms, and context and style summaries, aiming at a more individualized writing experience.
-
The study highlights the demand for dynamic style adaptation, explicit personalization, and multi-faceted user agency, suggesting future AI writing tools should provide more specificity in style and greater user control.
Enhancing Human-AI Collaborative Writing with GhostWriter: A Study on Personalization and User Agency
Introduction to GhostWriter
GhostWriter introduces an innovative approach to AI-assisted writing, focusing on elevating user agency and personalization. Developed through a collaboration between Harvard University and Microsoft, GhostWriter leverages LLMs to adapt to and learn from users’ writing preferences both implicitly and explicitly. This is achieved by analyzing users’ text inputs, direct style edits, and annotated likes and dislikes. The study involving 18 participants across various writing tasks sheds light on the potential of such systems to provide a more personalized and controlled writing assistance experience.
The Design and Functionality of GhostWriter
GhostWriter is designed based on a set of core principles aimed at preserving user agency, offering familiar interaction metaphors, integrating seamlessly into users’ workflows, and providing transparency regarding the system's understanding of the user's style. Key features include:
- Inline LLM-assisted Features: Abilities such as text rewriting, continuation, and context-sensitive prompting that all adapt to the defined user style and context, offering personalized text generation.
- Explicit Style Teaching Mechanisms: Users can directly inform the system of their preferred writing styles through likes, dislikes, and manual style description edits.
- Context and Style Summaries: The system provides summaries of the captured style and context, allowing users to understand and refine the system’s interpretations.
This design effectively turns the writing environment into a canvas where users can articulate their desired writing style and context, thereby enabling the LLM to generate content that aligns closely with their intentions.
Examination of User Interaction and Feedback
The study presents several key findings:
- Dynamic Style Adaptation Introduction: Users effectively utilized GhostWriter’s features to tailor the writing style, reflecting a strong desire for more personalized content from AI writing systems.
- Importance of Explicit Personalization: The likes/dislikes feature was notably appreciated for its simplicity and effectiveness in refining the learned writing style, underscoring the value of direct user input in AI-mediated processes.
- Demand for Multi-faceted User Agency: Feedback highlighted a need for finer-grained control over text generation and style application, suggesting that further enhancements in user-driven customization options could improve experience and outcomes.
Implications and Future Directions
GhostWriter's approach to personalization and user agency opens new avenues for the design of AI-assisted writing tools. It suggests that users are looking for more than just efficiency from these tools; they seek a partnership where their style and voice are recognized and respected by the AI. The study also points to the importance of offering multiple mechanisms for style personalization, catering to diverse user preferences and writing stages.
Looking ahead, the development of AI writing systems could benefit from incorporating capabilities for handling multiple styles and contexts, adjusting AI outputs with greater specificity, and integrating format customization as part of style personalization. Moreover, understanding and mapping the evolving nature of the human-AI collaborative relationship in creative processes will be crucial in designing systems that are not just tools, but partners in the writing process.
GhostWriter's exploration into augmenting human-AI collaborative writing experiences through personalization and agency presents a promising paradigm. It demonstrates the potential of AI to serve as a capable and adaptable assistant in the creative writing process, responding to the user's stylistic preferences and offering personalized support that enhances rather than overrides the human element.
Create an account to read this summary for free: