Traditionally, writing assistance systems have focused on short or even single-word suggestions. Recently, large language models like GPT-3 have made it possible to generate significantly longer natural-sounding suggestions, offering more advanced assistance opportunities. This study explores the trade-offs between sentence- vs. message-level suggestions for AI-mediated communication. We recruited 120 participants to act as staffers from legislators' offices who often need to respond to large volumes of constituent concerns. Participants were asked to reply to emails with different types of assistance. The results show that participants receiving message-level suggestions responded faster and were more satisfied with the experience, as they mainly edited the suggested drafts. In addition, the texts they wrote were evaluated as more helpful by others. In comparison, participants receiving sentence-level assistance retained a higher sense of agency, but took longer for the task as they needed to plan the flow of their responses and decide when to use suggestions. Our findings have implications for designing task-appropriate communication assistance systems.

翻译：传统上，写作辅助系统主要聚焦于简短甚至单词级的建议。近年来，GPT-3等大语言模型使得生成更长且自然的建议成为可能，从而提供更先进的辅助机会。本研究探讨了AI辅助沟通中句子级建议与消息级建议之间的权衡。我们招募了120名参与者，模拟立法机构办公室工作人员的角色，这些人员通常需要回复大量选民诉求。参与者被要求在不同辅助类型下回复邮件。结果显示，接收消息级建议的参与者回复更快，且对体验更满意，因为他们主要对建议草稿进行编辑。此外，他们撰写的文本被他人评价为更有帮助。相比之下，接收句子级建议的参与者保留了更强的自主感，但因需要规划回复流程并决定何时使用建议而花费更长时间。我们的发现对设计任务适配的沟通辅助系统具有启示意义。