Sparks: Inspiration for Science Writing using Language Models

Katy Gero, Vivian Liu, Lydia Chilton


Abstract
Large-scale language models are rapidly improving, performing well on a 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 run a user study with 13 STEM graduate students 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 overall quality of sparks seen by a given participant did not correlate with their satisfaction with the tool.
Anthology ID:
2022.in2writing-1.12
Volume:
Proceedings of the First Workshop on Intelligent and Interactive Writing Assistants (In2Writing 2022)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Editors:
Ting-Hao 'Kenneth' Huang, Vipul Raheja, Dongyeop Kang, John Joon Young Chung, Daniel Gissin, Mina Lee, Katy Ilonka Gero
Venue:
In2Writing
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
83–84
Language:
URL:
https://aclanthology.org/2022.in2writing-1.12
DOI:
10.18653/v1/2022.in2writing-1.12
Bibkey:
Cite (ACL):
Katy Gero, Vivian Liu, and Lydia Chilton. 2022. Sparks: Inspiration for Science Writing using Language Models. In Proceedings of the First Workshop on Intelligent and Interactive Writing Assistants (In2Writing 2022), pages 83–84, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Sparks: Inspiration for Science Writing using Language Models (Gero et al., In2Writing 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.in2writing-1.12.pdf
Video:
 https://aclanthology.org/2022.in2writing-1.12.mp4