Can You Make It Sound Like You? Post-Editing LLM-Generated Text for Personal Style

2026-04-27

AI Summary

This study addresses the risk of personal writing style erosion when users rely on large language models (LLMs) in style-sensitive writing contexts. Through a preregistered online experiment, it systematically evaluates the effectiveness of post-editing in recovering individual stylistic identity by integrating users’ subjective perceptions with an embedding-based objective measure of stylistic similarity. Comparing texts produced through unaided human writing, LLM generation, and post-edited LLM output, the findings reveal that while post-editing increases stylistic alignment with users’ native writing, the resulting text remains closer to the LLM’s inherent style and exhibits significantly lower stylistic diversity than human-authored text. These results highlight the current limitations of post-editing strategies in preserving authentic personal style and provide empirical grounding for the development of more personalized AI writing assistants.

Technology Category

• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining
• Machine Learning: Large Multimodal Models (LMMs)
• Humans and AI: Learning Human Values and Preferences

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Despite the growing use of large language models (LLMs) for writing tasks, users may hesitate to rely on LLMs when personal style is important. Post-editing LLM-generated drafts or translations is a common collaborative writing strategy, but it remains unclear whether users can effectively reshape LLM-generated text to reflect their personal style. We conduct a pre-registered online study ($n=81$) in which participants post-edit LLM-generated drafts for writing tasks where personal style matters to them. Using embedding-based style similarity metrics, we find that post-editing increases stylistic similarity to participants' unassisted writing and reduces similarity to fully LLM-generated output. However, post-edited text still remains stylistically closer in style to LLM text than to participants' unassisted control text, and it exhibits reduced stylistic diversity compared to unassisted human text. We find a gap between perceived stylistic authenticity and model-measured stylistic similarity, with post-edited text often perceived as representative of participants' personal style despite remaining detectable LLM stylistic traces.

Problem

• personal style
• large language models
• post-editing
• stylistic similarity
• human-AI collaboration

Innovation

• personal writing style
• post-editing
• large language models
• style similarity
• stylistic authenticity