Rambler: Supporting Writing With Speech via LLM-Assisted Gist
  Manipulation
Authors: name: Susan Lin, name: Jeremy Warner, name: J. D. Zamfirescu-Pereira, name: Matthew G. Lee, name: Sauhard Jain, name: Michael Xuelin Huang, name: Piyawat Lertvittayakumjorn, name: Shanqing Cai, name: Shumin Zhai, name: Björn Hartmann, name: Can Liu

Dictation enables efficient text input on mobile devices. However, writing
with speech can produce disfluent, wordy, and incoherent text and thus requires
heavy post-processing. This paper presents Rambler, an LLM-powered graphical
user interface that supports gist-level manipulation of dictated text with two
main sets of functions: gist extraction and macro revision. Gist extraction
generates keywords and summaries as anchors to support the review and
interaction with spoken text. LLM-assisted macro revisions allow users to
respeak, split, merge and transform dictated text without specifying precise
editing locations. Together they pave the way for interactive dictation and
revision that help close gaps between spontaneous spoken words and
well-structured writing. In a comparative study with 12 participants performing
verbal composition tasks, Rambler outperformed the baseline of a speech-to-text
editor + ChatGPT, as it better facilitates iterative revisions with enhanced
user control over the content while supporting surprisingly diverse user
strategies.