2000 character limit reached
Ink and Individuality: Crafting a Personalised Narrative in the Age of LLMs (2404.00026v5)
Published 20 Mar 2024 in cs.HC, cs.AI, cs.CL, cs.IR, and cs.LG
Abstract: 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-computer interaction systems and enhancing writing assistants for personalization and individuality.
- Promptsource: An integrated development environment and repository for natural language prompts. arXiv preprint arXiv:2202.01279 (2022).
- Martin Bekker. 2024. Large language models and academic writing: Five tiers of engagement. South African Journal of Science 120, 1/2 (Jan. 2024). https://doi.org/10.17159/sajs.2024/17147
- On the dangers of stochastic parrots: Can language models be too big?. In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency. 610–623.
- A survey on evaluation of large language models. ACM Transactions on Intelligent Systems and Technology (2023).
- TaleBrush: Sketching Stories with Generative Pretrained Language Models. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). Association for Computing Machinery, Article 209, 19 pages. https://doi.org/10.1145/3491102.3501819
- Realtoxicityprompts: Evaluating neural toxic degeneration in language models. arXiv preprint arXiv:2009.11462 (2020).
- Katy Ilonka Gero and Lydia B Chilton. 2019a. How a Stylistic, Machine-Generated Thesaurus Impacts a Writer’s Process. In Proceedings of the 2019 on Creativity and Cognition. 597–603.
- Katy Ilonka Gero and Lydia B Chilton. 2019b. Metaphoria: An algorithmic companion for metaphor creation. In Proceedings of the 2019 CHI conference on human factors in computing systems. 1–12.
- Introassist: A tool to support writing introductory help requests. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. 1–13.
- How can we know what language models know? Transactions of the Association for Computational Linguistics 8 (2020), 423–438.
- LMCanvas: Object-Oriented Interaction to Personalize Large Language Model-Powered Writing Environments. arXiv preprint arXiv:2303.15125 (2023).
- Cheryl Krueger. 2001. From, Content, and Critical Distance: The Role of “Creative Personalization” in Language and Content Courses. Foreign Language Annals 34, 1 (2001), 18–25.
- Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. Comput. Surveys 55, 9 (2023), 1–35.
- Large language models challenge the future of higher education. Nature Machine Intelligence 5, 4 (2023), 333–334.
- Co-Writing Screenplays and Theatre Scripts with Language Models: Evaluation by Industry Professionals. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. 1–34.
- Training language models to follow instructions with human feedback, 2022. URL https://arxiv. org/abs/2203.02155 13 (2022).
- Vishakh Padmakumar and He He. 2023. Does Writing with Language Models Reduce Content Diversity? arXiv preprint arXiv:2309.05196 (2023).
- Chris Park. 2017. In other (people’s) words: plagiarism by university students—literature and lessons. Academic ethics (2017), 525–542.
- Red teaming language models with language models, 2022. URL https://arxiv. org/abs/2202.03286 ([n. d.]).
- Interacting with literary style through computational tools. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. 1–12.
- Towards mutual theory of mind in human-ai interaction: How language reflects what students perceive about a virtual teaching assistant. In Proceedings of the 2021 CHI conference on human factors in computing systems. 1–14.
- Simon Wang and Cissy Li. 2022. Personalization vs. Standardization: Digitalizing Feedback on Written Assignments in Freshman English Courses in Hong Kong. In The Use of Technology in English Medium Education. Springer, 65–89.
- Re3: Generating longer stories with recursive reprompting and revision. arXiv preprint arXiv:2210.06774 (2022).
- GhostWriter: Augmenting Collaborative Human-AI Writing Experiences Through Personalization and Agency. arXiv preprint arXiv:2402.08855 (2024).
- Wordcraft: Story Writing With Large Language Models. In 27th International Conference on Intelligent User Interfaces (Helsinki, Finland) (IUI ’22). Association for Computing Machinery, New York, NY, USA, 841–852. https://doi.org/10.1145/3490099.3511105