@inproceedings{10.1145/3532106.3533533,
author = {Gero, Katy Ilonka and Liu, Vivian and Chilton, Lydia},
title = {Sparks: Inspiration for Science Writing Using Language Models},
year = {2022},
isbn = {9781450393584},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3532106.3533533},
doi = {10.1145/3532106.3533533},
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.},
booktitle = {Designing Interactive Systems Conference},
pages = {1002–1019},
numpages = {18},
keywords = {creativity support tools, natural language processing, science writing, writing support, co-creativity},
location = {Virtual Event, Australia},
series = {DIS '22}
}