Sparks: Inspiration for Science Writing using Language Models

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.