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Emulating Author Style: A Feasibility Study of Prompt-enabled Text Stylization with Off-the-Shelf LLMs
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User-centric personalization of text opens many avenues of applications from stylized email composition to machine translation.Existing approaches in this domain often encounter limitations in data and resource requirements.Drawing inspiration from the success of prompt-enabled stylization in related fields, this work conducts the first feasibility study into 12 pre-trained SOTA LLMs for author style emulation.Although promising, the results suggest that current off-the-shelf LLMs fall short of achieving effective author style emulation.
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“Despite the potential of prompt engineering, there are few studies focusing on how it can be used to help LLMs imitate language styles.”
From Using Prompts to Guide Large Language Models in Imitating a Real Person's Language Style (2024) · cites this paper as [5]
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Cited by
Sorted by citations
Using Prompts to Guide Large Language Models in Imitating a Real Person's Language Style
Chen, Z., & Moscholios, S. (2024) · arXiv · doi:10.48550/arxiv.2410.03848
“[5] conducted the first feasibility study on using promptings to guide pre-trained large language models for controllable stylized text generation.”
BackgroundZheng, Y., Guo, L., Cai, Z., Li, J., Wang, Y., & Zhu, G. (2026) · Complex & Intelligent Systems · doi:10.1007/s40747-026-02259-7
Attribution Quality in AI-Generated Content: Benchmarking Style Embeddings and LLM Judges
Abbas, M. (2025) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw69685.2025.00205
Breaking the Imitation Game: Can LLMs Fool Humans and Machines Alike?
Ullah, U., Laudanna, S., Sorbo, A. D., & Visaggio, C. A. (2026) · International Journal of Intelligent Systems · doi:10.1155/int/8117975
Metadata
- Authors
- Bhandarkar, A., Wilson, R., Swarup, A., & Woodard, D.
- Affiliations
- University of Florida (US)
- Venue
- Proceedings of the 1st Workshop on Personalization of Generative AI Systems (PERSONALIZE 2024)
- Published
- 2024
- Pages
- 76-82
- OpenAlex
- W7126413578
- Cited by
- 3
- References
- 0
- Citations by year
- 2026: 2 · 2025: 1
- FWCI
- 0.89
- Type
- conference paper
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Authorship Attribution and Profiling
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- prompt engineering, large language models, machine translation, feasibility study
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- The paper
- Bhandarkar, A., Wilson, R., Swarup, A., & Woodard, D. (2024). Emulating Author Style: A Feasibility Study of Prompt-enabled Text Stylization with Off-the-Shelf LLMs. Proceedings of the 1st Workshop on Personalization of Generative AI Systems (PERSONALIZE 2024). https://doi.org/10.18653/v1/2024.personalize-1.6
