Beyond the Chat: Executable and Verifiable Text-Editing with LLMs
Philippe Laban, Jesse Vig, Marti A. Hearst, Caiming Xiong, Chien-Sheng Wu
Abstract
Conversational interfaces powered by Large Language Models (LLMs) have recently become a popular way to obtain feedback during document editing. However, standard chat-based conversational interfaces cannot explicitly surface the editing changes that they suggest. To give the author more control when editing with an LLM, we present InkSync, an editing interface that suggests executable edits directly within the document being edited. Because LLMs are known to introduce factual errors, Inksync also supports a 3-stage approach to mitigate this risk: Warn authors when a suggested edit introduces new information, help authors Verify the new information’s accuracy through external search, and allow a third party to Audit with a-posteriori verification via a trace of all auto-generated content. Two usability studies confirm the effectiveness of InkSync’s components when compared to standard LLM-based chat interfaces, leading to more accurate and more efficient editing, and improved user experience.