research-article

Sparks: Inspiration for Science Writing using Language Models

Authors:

Katy Ilonka Gero,

Lydia ChiltonAuthors Info & Claims

DIS '22: Proceedings of the 2022 ACM Designing Interactive Systems Conference

Pages 1002 - 1019

https://doi.org/10.1145/3532106.3533533

Published: 13 June 2022 Publication History

Abstract

Large-scale language models are rapidly improving, performing well on a wide variety of tasks with little to no customization. In this work we investigate how language models can support science writing, a challenging writing task that is both open-ended and highly constrained. We present a system for generating “sparks”, sentences related to a scientific concept intended to inspire writers. We find that our sparks are more coherent and diverse than a competitive language model baseline, and approach a human-written gold standard. We run a user study with 13 STEM graduate students writing on topics of their own selection and find three main use cases of sparks—inspiration, translation, and perspective—each of which correlates with a unique interaction pattern. We also find that while participants were more likely to select higher quality sparks, the average quality of sparks seen by a given participant did not correlate with their satisfaction with the tool. We end with a discussion about what impacts human satisfaction with AI support tools, considering participant attitudes towards influence, their openness to technology, as well as issues of plagiarism, trustworthiness, and bias in AI.

References

[1]

Daniel Adiwardana, Minh-Thang Luong, David R. So, Jamie Hall, Noah Fiedel, Romal Thoppilan, Zi Yang, Apoorv Kulshreshtha, Gaurav Nemade, Yifeng Lu, and Quoc V. Le. 2020. Towards a Human-like Open-Domain Chatbot. arXiv:2001.09977 [cs, stat] (Feb. 2020). http://arxiv.org/abs/2001.09977 arXiv:2001.09977.

[2]

Kenneth C Arnold, Krysta Chauncey, and Krzysztof Z Gajos. [n.d.]. Sentiment Bias in Predictive Text Recommendations Results in Biased Writing. ([n. d.]), 8.

[3]

Kenneth C Arnold, April M Volzer, and Noah G Madrid. [n.d.]. Generative Models can Help Writers without Writing for Them. ([n. d.]), 8.

[4]

Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie Cai, Michael Terry, Quoc Le, and Charles Sutton. 2021. Program Synthesis with Large Language Models. arXiv:2108.07732 [cs] (Aug. 2021). http://arxiv.org/abs/2108.07732 arXiv:2108.07732.

[5]

Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. 2021. On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. ACM, Virtual Event Canada, 610–623. https://doi.org/10.1145/3442188.3445922

Digital Library

[6]

Virginia Braun and Victoria Clarke. 2012. Thematic analysis.In APA handbook of research methods in psychology, Vol 2: Research designs: Quantitative, qualitative, neuropsychological, and biological., Harris Cooper, Paul M. Camic, Debra L. Long, A. T. Panter, David Rindskopf, and Kenneth J. Sher (Eds.). American Psychological Association, Washington, 57–71. https://doi.org/10.1037/13620-004

[7]

Anthony C. Breu. 2020. From Tweetstorm to Tweetorials: Threaded Tweets as a Tool for Medical Education and Knowledge Dissemination. Seminars in Nephrology 40, 3 (May 2020), 273–278. https://doi.org/10.1016/j.semnephrol.2020.04.005

[8]

Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020. Language Models are Few-Shot Learners. arXiv:2005.14165 [cs] (July 2020). http://arxiv.org/abs/2005.14165 arXiv:2005.14165.

[9]

Michael Brüggemann, Ines Lörcher, and Stefanie Walter. 2020. Post-normal science communication: exploring the blurring boundaries of science and journalism. Journal of Science Communication 19, 03 (June 2020), A02. https://doi.org/10.22323/2.19030202

[10]

Alex Calderwood, Vivian Qiu, Katy Ilonka Gero, and Lydia B Chilton. 2018. How Novelists Use Generative Language Models: An Exploratory User Study. In 23rd International Conference on Intelligent User Interfaces. ACM.

[11]

Tuhin Chakrabarty, Xurui Zhang, Smaranda Muresan, and Nanyun Peng. 2021. MERMAID: Metaphor Generation with Symbolism and Discriminative Decoding. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, Online, 4250–4261. https://doi.org/10.18653/v1/2021.naacl-main.336

[12]

Mia Xu Chen, Benjamin N. Lee, Gagan Bansal, Yuan Cao, Shuyuan Zhang, Justin Lu, Jackie Tsay, Yinan Wang, Andrew M. Dai, Zhifeng Chen, Timothy Sohn, and Yonghui Wu. 2019. Gmail Smart Compose: Real-Time Assisted Writing. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, Anchorage AK USA, 2287–2295. https://doi.org/10.1145/3292500.3330723

Digital Library

[13]

Erin Cherry and Celine Latulipe. 2014. Quantifying the Creativity Support of Digital Tools through the Creativity Support Index. ACM Transactions on Computer-Human Interaction 21, 4 (Aug. 2014), 1–25. https://doi.org/10.1145/2617588

Digital Library

[14]

Elizabeth Clark, Anne Spencer Ross, Chenhao Tan, Yangfeng Ji, and Noah A. Smith. 2018. Creative Writing with a Machine in the Loop: Case Studies on Slogans and Stories. In 23rd International Conference on Intelligent User Interfaces. ACM, Tokyo Japan, 329–340. https://doi.org/10.1145/3172944.3172983

Digital Library

[15]

Andy Coenen, Luke Davis, Daphne Ippolito, Emily Reif, and Ann Yuan. 2021. Wordcraft: a Human-AI Collaborative Editor for Story Writing. arXiv:2107.07430 [cs] (July 2021). http://arxiv.org/abs/2107.07430 arXiv:2107.07430.

[16]

N Dehouche. 2021. Plagiarism in the age of massive Generative Pre-trained Transformers (GPT-3). Ethics in Science and Environmental Politics 21 (March 2021), 17–23. https://doi.org/10.3354/esep00195

[17]

Yao Dou, Maxwell Forbes, Rik Koncel-Kedziorski, Noah A. Smith, and Yejin Choi. 2021. Scarecrow: A Framework for Scrutinizing Machine Text. arXiv:2107.01294 [cs] (July 2021). http://arxiv.org/abs/2107.01294 arXiv:2107.01294.

[18]

Angela Fan, Mike Lewis, and Yann Dauphin. 2018. Hierarchical Neural Story Generation. arXiv:1805.04833 [cs] (May 2018). http://arxiv.org/abs/1805.04833 arXiv:1805.04833.

[19]

Linda Flower and John R. Hayes. 1981. A Cognitive Process Theory of Writing. College Composition and Communication 32, 4 (Dec. 1981), 365. https://doi.org/10.2307/356600

[20]

Katy Ilonka Gero and Lydia B. Chilton. 2019. Metaphoria: An Algorithmic Companion for Metaphor Creation. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. ACM, Glasgow Scotland Uk, 1–12. https://doi.org/10.1145/3290605.3300526

Digital Library

[21]

Katy Ilonka Gero, Chris Kedzie, Savvas Petridis, and Lydia Chilton. 2021. Lightweight Decoding Strategies for Increasing Specificity. arXiv preprint arXiv:2110.11850(2021).

[22]

Sarah A. Gilbert. 2020. ”I run the world’s largest historical outreach project and it’s on a cesspool of a website.” Moderating a Public Scholarship Site on Reddit: A Case Study of r/AskHistorians. Proceedings of the ACM on Human-Computer Interaction 4, CSCW1 (May 2020), 1–27. https://doi.org/10.1145/3392822

Digital Library

[23]

Arthur C. Graesser, Murray Singer, and Tom Trabasso. 1994. Constructing inferences during narrative text comprehension.Psychological Review 101, 3 (1994), 371–395. https://doi.org/10.1037/0033-295x.101.3.371

[24]

P. Sol Hart and Erik C. Nisbet. 2012. Boomerang Effects in Science Communication: How Motivated Reasoning and Identity Cues Amplify Opinion Polarization About Climate Mitigation Policies. Communication Research 39, 6 (Dec. 2012), 701–723. https://doi.org/10.1177/0093650211416646

[25]

Junxian He, Wojciech Kryściński, Bryan McCann, Nazneen Rajani, and Caiming Xiong. 2020. CTRLsum: Towards Generic Controllable Text Summarization. arXiv:2012.04281 [cs] (Dec. 2020). http://arxiv.org/abs/2012.04281 arXiv:2012.04281.

[26]

Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2020. The Curious Case of Neural Text Degeneration. arXiv:1904.09751 [cs] (Feb. 2020). http://arxiv.org/abs/1904.09751 arXiv:1904.09751.

[27]

Emily L. Howell, Julia Nepper, Dominique Brossard, Michael A. Xenos, and Dietram A. Scheufele. 2019. Engagement present and future: Graduate student and faculty perceptions of social media and the role of the public in science engagement. PLOS ONE 14, 5 (May 2019), e0216274. https://doi.org/10.1371/journal.pone.0216274

[28]

Daphne Ippolito, Reno Kriz, Maria Kustikova, João Sedoc, and Chris Callison-Burch. 2019. Comparison of Diverse Decoding Methods from Conditional Language Models. arXiv:1906.06362 [cs] (June 2019). http://arxiv.org/abs/1906.06362 arXiv:1906.06362.

[29]

Dan Jurafsky and James H. Martin. 2020. Speech and Language Processing.

[30]

Anjuli Kannan, Karol Kurach, Sujith Ravi, Tobias Kaufmann, Andrew Tomkins, Balint Miklos, Greg Corrado, Laszlo Lukacs, Marina Ganea, Peter Young, and Vivek Ramavajjala. 2016. Smart Reply: Automated Response Suggestion for Email. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, San Francisco California USA, 955–964. https://doi.org/10.1145/2939672.2939801

Digital Library

[31]

Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney, Caiming Xiong, and Richard Socher. 2019. CTRL: A Conditional Transformer Language Model for Controllable Generation. arXiv:1909.05858 [cs] (Sept. 2019). http://arxiv.org/abs/1909.05858 arXiv:1909.05858.

[32]

Wei-Jen Ko, Greg Durrett, and Junyi Jessy Li. [n.d.]. Domain Agnostic Real-Valued Specificity Prediction. ([n. d.]), 8.

[33]

Mina Lee, Percy Liang, and Qian Yang. 2022. CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities. CoRR abs/2201.06796(2022). arXiv:2201.06796https://arxiv.org/abs/2201.06796

[34]

Xiang Li, Aynaz Taheri, Lifu Tu, and Kevin Gimpel. 2016. Commonsense Knowledge Base Completion. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Berlin, Germany, 1445–1455. https://doi.org/10.18653/v1/P16-1137

[35]

Xiang Lisa Li and Percy Liang. 2021. Prefix-Tuning: Optimizing Continuous Prompts for Generation. arXiv:2101.00190 [cs] (Jan. 2021). http://arxiv.org/abs/2101.00190 arXiv:2101.00190.

[36]

Stephanie Lin, Jacob Hilton, and Owain Evans. [n.d.]. TruthfulQA: Measuring How Models Mimic Human Falsehoods. ([n. d.]), 13.

[37]

Xiao Liu, Yanan Zheng, Zhengxiao Du, Ming Ding, Yujie Qian, Zhilin Yang, and Jie Tang. 2021. GPT Understands, Too. arxiv:2103.10385 [cs.CL]

[38]

Clara Meister, Tim Vieira, and Ryan Cotterell. 2021. If beam search is the answer, what was the question?arXiv:2010.02650 [cs] (Jan. 2021). http://arxiv.org/abs/2010.02650 arXiv:2010.02650.

[39]

Bonnie J. F. Meyer and Melissa N. Ray. 2017. Structure strategy interventions: Increasing reading comprehension of expository text. International Electronic Journal of Elementary Education 4, 1 (Aug. 2017), 127–152. https://www.iejee.com/index.php/IEJEE/article/view/217

[40]

Changhoon Oh, Jungwoo Song, Jinhan Choi, Seonghyeon Kim, Sungwoo Lee, and Bongwon Suh. 2018. I Lead, You Help but Only with Enough Details: Understanding User Experience of Co-Creation with Artificial Intelligence. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. ACM, Montreal QC Canada, 1–13. https://doi.org/10.1145/3173574.3174223

Digital Library

[41]

Zhenhui Peng, Qingyu Guo, Ka Wing Tsang, and Xiaojuan Ma. 2020. Exploring the Effects of Technological Writing Assistance for Support Providers in Online Mental Health Community. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. ACM, Honolulu HI USA, 1–15. https://doi.org/10.1145/3313831.3376695

Digital Library

[42]

Ethan Perez, Saffron Huang, Francis Song, Trevor Cai, Roman Ring, John Aslanides, Amelia Glaese, Nat McAleese, and Geoffrey Irving. 2022. Red Teaming Language Models with Language Models. arxiv:2202.03286 [cs.CL]

[43]

Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. [n.d.]. Language Models are Unsupervised Multitask Learners. ([n. d.]), 24.

[44]

Nils Reimers and Iryna Gurevych. 2019. Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. arXiv:1908.10084 [cs] (Aug. 2019). http://arxiv.org/abs/1908.10084 arXiv:1908.10084.

[45]

Laria Reynolds and Kyle McDonell. 2021. Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm. arXiv:2102.07350 [cs] (Feb. 2021). http://arxiv.org/abs/2102.07350 arXiv:2102.07350.

[46]

Melissa Roemmele. [n.d.]. Writing Stories with Help from Recurrent Neural Networks. ([n. d.]), 2.

[47]

Ramin Sadeghi. 2019. The attitude of scholars has not changed towards plagiarism since the medieval period: Definition of plagiarism according to Shams-e-Qays, thirteenth-century Persian literary scientist. Research Ethics 15, 2 (April 2019), 1–3. https://doi.org/10.1177/1747016116654065

[48]

Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. 2020. DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. arXiv:1910.01108 [cs] (Feb. 2020). http://arxiv.org/abs/1910.01108 arXiv:1910.01108.

[49]

Oliver Schmitt and Daniel Buschek. 2021. CharacterChat: Supporting the Creation of Fictional Characters through Conversation and Progressive Manifestation with a Chatbot. In Creativity and Cognition. ACM, Virtual Event Italy, 1–10. https://doi.org/10.1145/3450741.3465253

Digital Library

[50]

Hanieh Shakeri, Carman Neustaedter, and Steve DiPaola. 2021. SAGA: Collaborative Storytelling with GPT-3. Association for Computing Machinery, New York, NY, USA, 163–166. https://doi.org/10.1145/3462204.3481771

Digital Library

[51]

Ashley Shelby and Karen Ernst. 2013. Story and science: How providers and parents can utilize storytelling to combat anti-vaccine misinformation. Human Vaccines & Immunotherapeutics 9, 8 (Aug. 2013), 1795–1801. https://doi.org/10.4161/hv.24828

[52]

Nikhil Singh, Guillermo Bernal, Daria Savchenko, and Elena L. Glassman. 2022. Where to Hide a Stolen Elephant: Leaps in Creative Writing with Multimodal Machine Intelligence. ACM Trans. Comput.-Hum. Interact. (jan 2022). https://doi.org/10.1145/3511599 Just Accepted.

Digital Library

[53]

Alice Soragni and Anirban Maitra. 2019. Of scientists and tweets. Nature Reviews Cancer 19, 9 (Sept. 2019), 479–480. https://doi.org/10.1038/s41568-019-0170-4

[54]

Allen B. Tucker, Sergei Nirenburg, and Victor Raskin. 1986. Discourse and cohension in expository text. In Proceedings of the 11th coference on Computational linguistics -. Association for Computational Linguistics. https://doi.org/10.3115/991365.991419

Digital Library

[55]

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. [n.d.]. Attention is All you Need. ([n. d.]), 11.

[56]

Ashwin K. Vijayakumar, Michael Cogswell, Ramprasath R. Selvaraju, Qing Sun, Stefan Lee, David Crandall, and Dhruv Batra. 2018. Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models. arXiv:1610.02424 [cs] (Oct. 2018). http://arxiv.org/abs/1610.02424 arXiv:1610.02424.

[57]

Dustin J Welbourne and Will J Grant. [n.d.]. Science communication on YouTube: Factors that affect channel and video popularity. Public Understanding of Science([n. d.]), 13.

[58]

Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander Rush. 2020. Transformers: State-of-the-Art Natural Language Processing. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. Association for Computational Linguistics, Online, 38–45. https://doi.org/10.18653/v1/2020.emnlp-demos.6

[59]

Tongshuang Wu, Michael Terry, and Carrie J. Cai. 2021. AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts. arXiv:2110.01691 [cs] (Oct. 2021). http://arxiv.org/abs/2110.01691 arXiv:2110.01691.

[60]

Kai-Cheng Yang, Francesco Pierri, Pik-Mai Hui, David Axelrod, Christopher Torres-Lugo, John Bryden, and Filippo Menczer. 2020. The COVID-19 Infodemic: Twitter versus Facebook. arXiv:2012.09353 [cs] (Dec. 2020). http://arxiv.org/abs/2012.09353 arXiv:2012.09353.

[61]

Jingqing Zhang, Yao Zhao, Mohammad Saleh, and Peter J. Liu. 2020. PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization. arXiv:1912.08777 [cs] (July 2020). http://arxiv.org/abs/1912.08777 arXiv:1912.08777.

[62]

Ruqing Zhang, Jiafeng Guo, Yixing Fan, Yanyan Lan, Jun Xu, and Xueqi Cheng. [n.d.]. Learning to Control the Specificity in Neural Response Generation. ([n. d.]), 10.

Cited By

Bonnet STeuteberg F(2025)Unfolding the Potential of Generative Artificial IntelligenceInternational Journal of Knowledge Management10.4018/IJKM.36822321:1(1-25)Online publication date: 1-Feb-2025
https://doi.org/10.4018/IJKM.368223
Akkan HSeyyar G(2025)Improving readability in AI-generated medical information on fragility fractures: the role of prompt wording on ChatGPT’s responsesOsteoporosis International10.1007/s00198-024-07358-0Online publication date: 8-Jan-2025
https://doi.org/10.1007/s00198-024-07358-0
Imteyaz KFlores Saviaga CSavage S(2024)GigSenseGigSenseAvances en Interacción Humano-Computadora10.47756/aihc.y9i1.1599:1(135-145)Online publication date: 30-Nov-2024
https://doi.org/10.47756/aihc.y9i1.159
Show More Cited By

Recommendations

Metaphorian: Leveraging Large Language Models to Support Extended Metaphor Creation for Science Writing
DIS '23: Proceedings of the 2023 ACM Designing Interactive Systems Conference

Science writers commonly use extended metaphors to communicate unfamiliar concepts in a more accessible way to a wider audience. However, creating metaphors for science writing is challenging even for professional writers; according to our formative ...
Metaphoria: An Algorithmic Companion for Metaphor Creation
CHI '19: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

Creative writing, from poetry to journalism, is at the crux of human ingenuity and social interaction. Existing creative writing support tools produce entire passages or fully formed sentences, but these approaches fail to adapt to the writer's own ...
Can Children Emulate a Robotic Non-Player Character's Figural Creativity?
CHI PLAY '20: Proceedings of the Annual Symposium on Computer-Human Interaction in Play

Can intelligent non-player game characters (NPCs) increase children's creativity during collaborative gameplay? Children's creativity is influenced by collaborative play with creative peers through social emulation. In this paper, we study children's ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

DIS '22: Proceedings of the 2022 ACM Designing Interactive Systems Conference

June 2022

1947 pages

ISBN:9781450393584

DOI:10.1145/3532106

Editors:
Florian `Floyd' Mueller
Monash University, Melbourne, Australia
,
Stefan Greuter
Deakin University, Melbourne, Australia
,
Rohit Ashok Khot
RMIT University, Melbourne, Australia
,
Penny Sweetser
The Australian National University, Canberra, Australia
,
Marianna Obrist
University College London, UK

Copyright © 2022 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGCHI: ACM Special Interest Group on Computer-Human Interaction

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 June 2022

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Funding Sources

Conference

DIS '22

Sponsor:

SIGCHI

DIS '22: Designing Interactive Systems Conference

June 13 - 17, 2022

Virtual Event, Australia

Acceptance Rates

Overall Acceptance Rate 1,158 of 4,684 submissions, 25%

Upcoming Conference

DIS '25

Sponsor:
sigchi

Designing Interactive Systems Conference

July 5 - 9, 2025

Funchal , Portugal

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

71
Total Citations
View Citations
2,027
Total Downloads

Downloads (Last 12 months)659
Downloads (Last 6 weeks)44

Reflects downloads up to 28 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Bonnet STeuteberg F(2025)Unfolding the Potential of Generative Artificial IntelligenceInternational Journal of Knowledge Management10.4018/IJKM.36822321:1(1-25)Online publication date: 1-Feb-2025
https://doi.org/10.4018/IJKM.368223
Akkan HSeyyar G(2025)Improving readability in AI-generated medical information on fragility fractures: the role of prompt wording on ChatGPT’s responsesOsteoporosis International10.1007/s00198-024-07358-0Online publication date: 8-Jan-2025
https://doi.org/10.1007/s00198-024-07358-0
Imteyaz KFlores Saviaga CSavage S(2024)GigSenseGigSenseAvances en Interacción Humano-Computadora10.47756/aihc.y9i1.1599:1(135-145)Online publication date: 30-Nov-2024
https://doi.org/10.47756/aihc.y9i1.159
Shao Z(2024)GAI in the 2023 WGA StrikeUnderstanding Generative AI in a Cultural Context10.4018/979-8-3693-7235-7.ch009(211-244)Online publication date: 18-Dec-2024
https://doi.org/10.4018/979-8-3693-7235-7.ch009
Shabalin VLysov NPavlova OSupil'nikov ASamsonova EAnosova EYaremin B(2024)The Scientific Journal "Vestnik of the Medical Institute "REAVIZ": History, Challenges, and PerspectivesBulletin of the Medical Institute "REAVIZ" (REHABILITATION, DOCTOR AND HEALTH)10.20340/vmi-rvz.2024.3.EDT.114:3(6-14)Online publication date: 24-Sep-2024
https://doi.org/10.20340/vmi-rvz.2024.3.EDT.1
Malaviya CAgrawal PGanchev KSrinivasan PHuot FBerant JYatskar MDas DLapata MAlberti C(2024) Dolomites : Domain-Specific Long-Form Methodical Tasks Transactions of the Association for Computational Linguistics10.1162/tacl_a_0072713(1-29)Online publication date: 7-Jan-2024
https://doi.org/10.1162/tacl_a_00727
Kreminski M(2024)The Dearth of the Author in AI-Supported WritingProceedings of the Third Workshop on Intelligent and Interactive Writing Assistants10.1145/3690712.3690725(48-50)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3690712.3690725
Rismani SBlodgett SOlteanu ALiao QMoon A(2024)How different mental models of AI-based writing assistants impact writers’ interactions with themProceedings of the Third Workshop on Intelligent and Interactive Writing Assistants10.1145/3690712.3690722(34-37)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3690712.3690722
Armstrong LLiu AMacNeil SMetaxa D(2024)The Silicon Ceiling: Auditing GPT’s Race and Gender Biases in HiringProceedings of the 4th ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization10.1145/3689904.3694699(1-18)Online publication date: 29-Oct-2024
https://dl.acm.org/doi/10.1145/3689904.3694699
Imamura SHiraki HRekimoto J(2024)Serendipity Wall: A Discussion Support System Using Real-time Speech Recognition and Large Language ModelProceedings of the Augmented Humans International Conference 202410.1145/3652920.3652931(237-247)Online publication date: 4-Apr-2024
https://dl.acm.org/doi/10.1145/3652920.3652931
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Figures

Tables

Media

View Table of Conten