Interpretable Stylistic Variation in Human and LLM Writing Across Genres, Models, and Decoding Strategies

2026-04-15

AI Summary

This study systematically investigates stylistic differences between human- and large language model (LLM)-generated texts across genres, models, and decoding strategies to inform responsible LLM deployment. Leveraging Biber’s multidimensional framework of register variation, the authors conduct a large-scale comparative analysis of texts produced by 11 LLMs across eight genres and four decoding strategies. The findings reveal that model type and genre exert substantially stronger influences on textual style than prompting or decoding choices. Notably, chat-oriented models exhibit pronounced clustering in stylistic space, and key linguistic features of LLM-generated text demonstrate robustness across generation conditions, with genre effects consistently outweighing those of text origin.

Technology Category

• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining
• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Large Language Models (LLMs) are now capable of generating highly fluent, human-like text. They enable many applications, but also raise concerns such as large scale spam, phishing, or academic misuse. While much work has focused on detecting LLM-generated text, only limited work has gone into understanding the stylistic differences between human-written and machine-generated text. In this work, we perform a large scale analysis of stylistic variation across human-written text and outputs from 11 LLMs spanning 8 different genres and 4 decoding strategies using Douglas Biber's set of lexicogrammatical and functional features. Our findings reveal insights that can guide intentional LLM usage. First, key linguistic differentiators of LLM-generated text seem robust to generation conditions (e.g., prompt settings to nudge them to generate human-like text, or availability of human-written text to continue the style); second, genre exerts a stronger influence on stylistic features than the source itself; third, chat variants of the models generally appear to be clustered together in stylistic space, and finally, model has a larger effect on the style than decoding strategy, with some exceptions. These results highlight the relative importance of model and genre over prompting and decoding strategies in shaping the stylistic behavior of machine-generated text.

Problem

• stylistic variation
• human writing
• LLM-generated text
• genre
• interpretability

Innovation

• stylistic variation
• large language models
• Biber dimensions
• genre analysis
• decoding strategies