title: Rambler: Supporting Writing With Speech via LLM-Assisted Gist
    Manipulation
summary: 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.
paper_authors._id: 65adf42114e6582c300d13f1
paper_authors.name: Susan Lin
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paper_authors.fullname: Matthew Lee
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paper_authors.fullname: Sauhard Jain
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paper_authors.name: Michael Xuelin Huang
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paper_authors.name: Piyawat Lertvittayakumjorn
paper_authors._id: 65adf42114e6582c300d13f8
paper_authors.name: Shanqing Cai
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paper_authors.fullname: Shanqing Cai
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paper_authors.user: sqcai
paper_authors._id: 65adf42114e6582c300d13f9
paper_authors.name: Shumin Zhai
paper_authors._id: 65adf42114e6582c300d13fa
paper_authors.name: Björn Hartmann
paper_authors._id: 65adf42114e6582c300d13fb
paper_authors.name: Can Liu
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paper_authors.fullname: Can Liu
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paper_authors.user: canlhci
paper_title: Rambler: Supporting Writing With Speech via LLM-Assisted Gist
    Manipulation
paper_summary: 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.
paper_ai_keywords: LLM-powered graphical user interface gist extraction macro revision keywords summaries anchors LLM-assisted macro revisions respeak split merge transform interactive dictation verbal composition tasks speech-to-text editor ChatGPT iterative revisions user control