Rambler: Supporting Writing With Speech via LLM-Assisted Gist Manipulation (2401.10838v2)

Abstract: 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.

• The paper demonstrates that a novel LLM-assisted GUI enhances speech-to-text conversion through gist extraction and macro revision.
• The methodology uses LLM-derived keyword summarization and iterative text refinement to reduce cognitive load and improve coherence.
• Evaluation with 12 participants shows Rambler outperforming traditional speech-to-text systems in supporting flexible, high-level writing revisions.

Analyzing "Rambler: Speech-based Long-form Writing via an LLM-Augmented GUI"

The paper "Rambler: Speech-based Long-form Writing via an LLM-Augmented GUI" addresses a crucial challenge in modern human-computer interaction: facilitating the efficient conversion of spoken language into structured written content. As the ubiquity of mobile devices continues to rise, leveraging natural language inputs, such as speech, presents a valuable opportunity to simplify interaction and input processes. However, the inherent verbosity and potential incoherence of spoken language pose significant obstacles.

Core Contributions and Methodology

Rambler is introduced as an LLM-powered graphical user interface designed to assist users in effective speech-to-text writing, capitalizing on recent advancements in LLMs. The novel interface diverges from traditional speech-to-text systems by focusing on "gist-based" manipulations—conceptual chunks that facilitate users' management of their spoken content. Rambler comprises two pivotal components: gist extraction and macro revision.

Gist Extraction

This process involves generating keywords and summaries from the raw transcriptions—these elements serve as navigational anchors, improving users' ability to review and comprehend their dictated text. By abstracting longer text into concise summaries and focusing on key ideas, Rambler reduces cognitive load, aiding users in identifying and reorganizing central concepts more effectively.

Macro Revision

Through LLM-powered tools, users can perform high-level text manipulations such as merging, splitting, and transforming text chunks without needing precise edit points. This enables users to reconceptualize their spoken input on a macro level, supporting iterative refinement that mirrors more traditional writing processes. Respeaking segmentations directly into distinct "Rambles" allows users to develop structurally coherent text output iteratively.

Evaluation and Findings

In a comparative paper involving 12 participants, Rambler was evaluated against a baseline combination of a speech-to-text editor and ChatGPT. Analysis of the user paper indicates that Rambler outperforms this baseline, particularly in supporting iterative revisions and providing enhanced user control over content management. Interestingly, participants demonstrated diverse revision strategies facilitated by Rambler's flexible, gist-centric affordances.

Implications and Future Directions

The theoretical implications of this work suggest a promising avenue for integrating LLMs more meaningfully into human-computer interaction interfaces. By prioritizing semantic content over verbatim transcription, Rambler exemplifies how AI can facilitate more natural, fluent interactions with technology. Practically, it points toward an innovative design approach for mobile writing applications, potentially reducing the barriers to starting and iterating on long-form text compositions.

Future explorations could expand upon this functionality to encompass other input modalities or develop more granular user customization capabilities. Additionally, as LLM efficiency and accuracy continue to improve, the latency issues observed within the real-time application of LLMs for dynamic text manipulation may diminish, paving the way for seamless integration in various writing contexts.

In conclusion, the development and evaluation of Rambler represent a thoughtful application of state-of-the-art AI to address a tangible usability challenge. Its emphasis on integrating conceptual-level interactions within conventional GUI frameworks marks a significant contribution toward the seamless integration of speech into our digital writing habits.