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 do not support transparency and verifiability of the editing changes that they suggest. To give the author more agency 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 an auditor to perform an a-posteriori verification by Auditing the document 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, more efficient editing, and improved user experience.

翻译：基于大语言模型（LLMs）的对话界面近年来已成为文档编辑中获取反馈的流行方式。然而，标准的聊天式对话界面无法支持其建议编辑变更的透明性与可验证性。为使作者在使用LLM进行编辑时获得更多自主权，我们提出了InkSync——一种可直接在编辑文档中建议可执行编辑操作的编辑界面。鉴于LLM已知会引入事实性错误，InkSync还采用三阶段方法降低此风险：在建议编辑引入新信息时向作者发出警告；通过外部搜索帮助作者验证新信息的准确性；允许审计员通过追踪所有自动生成内容的痕迹对文档进行事后审计。两项可用性研究证实，与基于LLM的标准聊天界面相比，InkSync各组件在提升编辑准确性、效率及改善用户体验方面均具有显著效果。