Ink and Individuality: Crafting a Personalised Narrative in the Age of LLMs
Open accessAbstract
Individuality and personalization comprise the distinctive characteristics that make each writer unique and influence their words in order to effectively engage readers while conveying authenticity. However, our growing reliance on LLM-based writing assistants risks compromising our creativity and individuality over time. We often overlook the negative impacts of this trend on our creativity and uniqueness, despite the possible consequences. This study investigates these concerns by performing a brief survey to explore different perspectives and concepts, as well as trying to understand people’s viewpoints, in conjunction with past studies in the area. Addressing these issues is essential for improving human-centered aspects of AI models and enhancing writing assistants for personalization and individuality.
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AI co-writing systems challenge long held ideals about agency and ownership in the creative process, thereby hindering widespread adoption.
“AI co-writing systems challenge long held ideals about agency and ownership in the creative process, thereby hindering widespread adoption.”
From Where Do I 'Add the Egg'?: Exploring Agency and Ownership in AI Creative Co-Writing Systems (2026) · cites this paper as [63]
The paper raises concerns about lost individuality in AI-assisted writing, but it makes the contrary point about adoption. Section 5 reports that surveyed users prefer to use LLMs for writing and that most are not particularly bothered by their lack of personalization; the Conclusion likewise describes heavy reliance on LLMs. The paper does not establish that concerns about agency or ownership hinder widespread adoption. (LLM, conf=0.83)
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On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445922 · Cited by 6,988 · see citing works
Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2023) · ACM Computing Surveys · doi:10.1145/3560815 · Cited by 4,361 · see citing works
Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al. (2022) · arXiv · doi:10.48550/arxiv.2203.02155 · Cited by 4,208 · see citing works
A Survey on Evaluation of Large Language Models
Chang, Y., Wang, X., Wang, J., Wu, Y., Yang, L., Zhu, K., Chen, H., Yi, X., Wang, C., Wang, Y., et al. (2024) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3641289 · Cited by 2,842 · see citing works
Wordcraft: Story Writing With Large Language Models
Yuan, A., Coenen, A., Reif, E., & Ippolito, D. (2022) · 27th International Conference on Intelligent User Interfaces · doi:10.1145/3490099.3511105 · Cited by 374 · see citing works
Red Teaming Language Models with Language Models
Perez, E., Huang, S., Song, F., Cai, T., Ring, R., Aslanides, J., Glaese, A., McAleese, N., & Irving, G. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.225 · Cited by 363 · see citing works
Mirowski, P., Mathewson, K. W., Pittman, J., & Evans, R. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581225 · Cited by 240 · cited 1× · see citing works
“LLMs show potential for helping users with a range of creative writing activities, including screenplays [15] and short stories [23, 25].”
BackgroundTaleBrush: Sketching Stories with Generative Pretrained Language Models
Chung, J. J. Y., Kim, W., Yoo, K. M., Lee, H., Adar, E., & Chang, M. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3501819 · Cited by 206 · see citing works
Large language models challenge the future of higher education
Milano, S., McGrane, J. A., & Leonelli, S. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00644-2 · Cited by 169 · see citing works
Metaphoria: An Algorithmic Companion for Metaphor Creation
Gero, K. I., & Chilton, L. B. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300526 · Cited by 146 · see citing works
Wang, Q., Saha, K., Gregori, E., Joyner, D., & Goel, A. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445645 · Cited by 120 · see citing works
In Other (People's) Words: plagiarism by university students—literature and lessons
Park, C. (2017) · Routledge eBooks · doi:10.4324/9781315263465-42 · Cited by 79 · see citing works
IntroAssist: A Tool to Support Writing Introductory Help Requests
Hui, J. S., Gergle, D., & Gerber, E. M. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173596 · Cited by 76 · see citing works
Jamplate: Exploring LLM-Enhanced Templates for Idea Reflection
Xu, X. (., Yin, J. Y., Gu, C., Mar, J., Zhang, S., E, J. L., & Dow, S. P. (2024) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3640543.3645196 · Cited by 49 · see citing works
How a Stylistic, Machine-Generated Thesaurus Impacts a Writer's Process
Gero, K. I., & Chilton, L. B. (2019) · Proceedings of the 2019 on Creativity and Cognition · doi:10.1145/3325480.3326573 · Cited by 32 · see citing works
Large language models and academic writing: Five tiers of engagement
Bekker, M. (2024) · South African Journal of Science · doi:10.17159/sajs.2024/17147 · Cited by 15 · see citing works
Interacting with Literary Style through Computational Tools
Sterman, S., Huang, E. J., Liu, V., & Paulos, E. (2020) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376730 · Cited by 14 · see citing works
Krueger, C. L. (2001) · Foreign Language Annals · doi:10.1111/j.1944-9720.2001.tb02798.x · Cited by 13 · see citing works
The Use of Technology in English Medium Education
(2022) · English language education · doi:10.1007/978-3-030-99622-2 · Cited by 10 · see citing works
Flagged first, then by citations
Research works printed but not linked: 8
- Stephen H Bach, Victor Sanh, Zheng-Xin Yong, Albert Webson, Colin Raffel, Ni- hal V Nayak, Abheesht Sharma, Taewoon Kim, M Saiful Bari, Thibault Fevry, et al. 2022. Promptsource: An integrated development environment and repository for natural language prompts. arXiv preprint arXiv:2202.01279 (2022). · arXiv
- Samuel Gehman, Suchin Gururangan, Maarten Sap, Yejin Choi, and Noah A Smith. 2020. Realtoxicityprompts: Evaluating neural toxic degeneration in language models. arXiv preprint arXiv:2009.11462 (2020). · arXiv
- Zhengbao Jiang, Frank F Xu, Jun Araki, and Graham Neubig. 2020. How can we know what language models know? Transactions of the Association for Computa- tional Linguistics 8 (2020), 423-438.
- Tae Soo Kim, Arghya Sarkar, Yoonjoo Lee, Minsuk Chang, and Juho Kim. 2023. LMCanvas: Object-Oriented Interaction to Personalize Large Language Model- Powered Writing Environments. arXiv preprint arXiv:2303.15125 (2023). · arXiv
- Vishakh Padmakumar and He He. 2023. Does Writing with Language Models Reduce Content Diversity? arXiv preprint arXiv:2309.05196 (2023). · arXiv
- Orit Shaer, Angelora Cooper, Osnat Mokryn, Andrew L Kun, and Hagit Ben Shoshan. 2024. AI-Augmented Brainwriting: Investigating the use of LLMs in group ideation. arXiv preprint arXiv:2402.14978 (2024). · arXiv
- Kevin Yang, Yuandong Tian, Nanyun Peng, and Dan Klein. 2022. Re3: Gener- ating longer stories with recursive reprompting and revision. arXiv preprint arXiv:2210.06774 (2022). · arXiv
- Catherine Yeh, Gonzalo Ramos, Rachel Ng, Andy Huntington, and Richard Banks. 2024. GhostWriter: Augmenting Collaborative Human-AI Writing Experiences Through Personalization and Agency. arXiv preprint arXiv:2402.08855 (2024). · arXiv
Cited by
Sorted by citations
PromptPilot: Exploring User Experience of Prompting with AI-Enhanced Initiative in LLMs
Kim, S., Eun, J., Park, Y. E., Lee, K., Lee, G., & Lee, J. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2489030 · Cited by 9 · see citing works
Harnessing reasoning-based AI for digital entrepreneurship in creative authorship
Grundy, D. (2025) · Artificial Intelligence in Education · doi:10.1108/aiie-03-2025-0038 · Cited by 1 · see citing works
- Authors
- Wasi, A. T., Islam, R., & Islam, M. R.
- Affiliations
- Shahjalal University of Science and Technology (BD)
- BRAC University (BD)
- Independent University, Bangladesh (BD)
- Venue
- Proceedings of the Third Workshop on Intelligent and Interactive Writing Assistants
- Publisher
- ACM
- Published
- 2024-05-11
- Pages
- 43-47
- OpenAlex
- W4403413445
- Cited by
- 2
- References
- 18
- Citations by year
- 2025: 2
- FWCI
- 0.91
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Workshop on Intelligent and Interactive Writing Assistants (In2Writing) (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by-nc-nd)
- arXiv (Cornell University)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Authorship Attribution and Profiling
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Keywords
- individuality, personalization, large language models, writing assistants, creativity, AI-generated text, human-AI collaboration, personalized narratives
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