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
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.in2writing-1.12.pdf