Rambler：透過LLM輔助的要點操作支援書寫

摘要

口述功能使移動設備上的文本輸入更加高效。然而，使用語音書寫可能會產生不流暢、冗長和不連貫的文本，因此需要進行大量後處理。本文介紹了Rambler，一個由LLM驅動的圖形用戶界面，支持對口述文本進行主旨級別的操作，具有兩個主要功能集：主旨提取和宏觀修訂。主旨提取生成關鍵詞和摘要作為錨點，以支持審查和與口述文本的交互。LLM輔助的宏觀修訂使用戶能夠重新口述、拆分、合併和轉換口述文本，而無需指定精確的編輯位置。它們共同為互動式口述和修訂鋪平道路，有助於彌合口語的即興詞語和結構良好的書寫之間的差距。在一項比較研究中，有12名參與者執行口頭作文任務，Rambler表現優於基準的語音轉文本編輯器+ChatGPT，因為它更好地促進了具有增強用戶對內容控制的迭代修訂，同時支持多樣化的用戶策略。

English

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.

Rambler：透過LLM輔助的要點操作支援書寫

Rambler: Supporting Writing With Speech via LLM-Assisted Gist Manipulation

摘要

Support