Towards User-Centric Text-to-Text Generation: A Survey
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Personalized AI applications, including personalized AI writing assistance, are becoming more common and readily accessible.
“Meanwhile, personalized AI applications-including personalized AI writing assistance-are becoming more common and readily accessible [9,14,27,65].”
From 'It was 80% me, 20% AI': Seeking Authenticity in Co-Writing with Large Language Models (2025) · cites this paper as [65]
Cites
Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning
Hitaj, B., Ateniese, G., & Perez-Cruz, F. (2017) · Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3133956.3134012 · Cited by 1,377 · see citing works
Diurnal and seasonal mood vary with work, sleep, and daylength across diverse cultures
Golder, S. A., & Macy, M. W. (2011) · Science · doi:10.1126/science.1202775 · Cited by 1,155 · see citing works
Meteor: an automatic metric for MT evaluation with high levels of correlation with human judgments
Lavie, A., & Agarwal, A. (2007) · Proceedings of the Second Workshop on Statistical Machine Translation - StatMT '07 · doi:10.3115/1626355.1626389 · Cited by 1,047 · see citing works
From Eliza to XiaoIce: Challenges and opportunities with social chatbots
Shum, H.-Y., He, X.-D., & Li, D. (2018) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.1700826 · Cited by 749 · see citing works
Chaves, A. P., & Gerosa, M. A. (2021) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2020.1841438 · Cited by 572 · see citing works
Style transfer through back-translation
Prabhumoye, S., Tsvetkov, Y., Salakhutdinov, R., & Black, A. W. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1080 · Cited by 348 · see citing works
Controlling Politeness in Neural Machine Translation via Side Constraints
Sennrich, R., Haddow, B., & Birch, A. (2016) · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/n16-1005 · Cited by 273 · see citing works
At Your Service: Designing Voice Assistant Personalities to Improve Automotive User Interfaces
Braun, M., Mainz, A., Chadowitz, R., Pfleging, B., & Alt, F. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300270 · Cited by 182 · see citing works
Towards Knowledge-Based Personalized Product Description Generation in E-commerce
Chen, Q., Lin, J., Zhang, Y., Yang, H., Zhou, J., & Tang, J. (2019) · Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3292500.3330725 · Cited by 92 · see citing works
Argument Strength is in the Eye of the Beholder: Audience Effects in Persuasion
Lukin, S., Anand, P., Walker, M., & Whittaker, S. (2017) · Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers · doi:10.18653/v1/e17-1070 · Cited by 89 · see citing works
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Draxler, F., Werner, A., Lehmann, F., Hoppe, M., Schmidt, A., Buschek, D., & Welsch, R. (2024) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3637875 · Cited by 186 · see citing works
'It was 80% me, 20% AI': Seeking Authenticity in Co-Writing with Large Language Models
Hwang, A. H.-C., Liao, Q. V., Blodgett, S. L., Olteanu, A., & Trischler, A. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3711020 · Cited by 42 · see citing works
- Authors
- Yang, D., & Flek, L.
- Affiliations
- Georgia Institute of Technology (US)
- Philipps University of Marburg (DE)
- Corresponding author
- Flek, Lucie
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2021
- Pages
- 3-22
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W3196937884
- Cited by
- 10
- References
- 10
- Citations by year
- 2025: 2 · 2024: 3 · 2023: 1 · 2022: 3 · 2021: 1
- FWCI
- 0.92
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- text-to-text generation, personalization, human-computer interaction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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