CorpusStudio: Surfacing Emergent Patterns in a Corpus of Prior Work while Writing
arXiv:2503.12436 · doi:10.1145/3706598.3713974
Abstract
Many communities, including the scientific community, develop implicit writing norms. Understanding them is crucial for effective communication with that community. Writers gradually develop an implicit understanding of norms by reading papers and receiving feedback on their writing. However, it is difficult to both externalize this knowledge and apply it to one's own writing. We propose two new writing support concepts that reify document and sentence-level patterns in a given text corpus: (1) an ordered distribution over section titles and (2) given the user's draft and cursor location, many retrieved contextually relevant sentences. Recurring words in the latter are algorithmically highlighted to help users see any emergent norms. Study results (N=16) show that participants revised the structure and content using these concepts, gaining confidence in aligning with or breaking norms after reviewing many examples. These results demonstrate the value of reifying distributions over other authors' writing choices during the writing process.
19 pages, 12 figures, 1 table, ACM CHI 2025
References in corpus (10)
- CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities
- The Metacognitive Demands and Opportunities of Generative AI
- A Design Space for Intelligent and Interactive Writing Assistants
- Beyond Text Generation: Supporting Writers with Continuous Automatic Text Summaries
- Choice Over Control: How Users Write with Large Language Models using Diegetic and Non-Diegetic Prompting
- RAG-Fusion: a New Take on Retrieval-Augmented Generation
- Writer-Defined AI Personas for On-Demand Feedback Generation
- CiteSee: Augmenting Citations in Scientific Papers with Persistent and Personalized Historical Context
- Interacting with next-phrase suggestions: How suggestion systems aid and influence the cognitive processes of writing
- PaperWeaver: Enriching Topical Paper Alerts by Contextualizing Recommended Papers with User-collected Papers