Suggested questions (SQs) provide an effective initial interface for users to engage with their documents in AI-powered reading applications. In practical reading sessions, users have diverse backgrounds and reading goals, yet current SQ features typically ignore such user information, resulting in homogeneous or ineffective questions. We introduce a pipeline that generates personalized SQs by incorporating reader profiles (professions and reading goals) and demonstrate its utility in two ways: 1) as an improved SQ generation pipeline that produces higher quality and more diverse questions compared to current baselines, and 2) as a data generator to fine-tune extremely small models that perform competitively with much larger models on SQ generation. Our approach can not only serve as a drop-in replacement in current SQ systems to immediately improve their performance but also help develop on-device SQ models that can run locally to deliver fast and private SQ experience.

翻译：推荐问题为AI驱动的阅读应用中用户与其文档互动提供了一个有效的初始界面。在实际阅读场景中，用户具有不同的背景和阅读目标，然而当前的推荐问题功能通常忽略此类用户信息，导致生成的问题同质化或效果不佳。我们提出了一种通过整合读者画像（职业和阅读目标）来生成个性化推荐问题的流程，并通过两种方式论证其实用性：1）作为一种改进的推荐问题生成流程，与现有基线相比，能产生质量更高、更多样化的问题；2）作为数据生成器，用于微调极小型模型，使其在推荐问题生成任务上能与大得多的模型竞争。我们的方法不仅可以作为当前推荐问题系统的即插即用替代方案以立即提升其性能，还能助力开发可在本地运行的设备端推荐问题模型，从而提供快速且私密的推荐问题体验。