根据用户背景生成个性化提问，让文档阅读更高效。
Persona-SQ: A Personalized Suggested Question Generation Framework For Real-world Documents
- 结合职业和阅读目标生成定制问题
- 生成的问题质量更高、种类更多
- 适合需要本地化私密问答的场景
建议问题（SQ）为用户提供与文档互动的有效入口。实际阅读中，用户背景和阅读目标各异，但现有方法通常忽略这些信息，导致问题同质或低效。本文提出一个融合读者画像（职业与阅读目标）的生成框架，通过两种方式验证其有效性：1）作为改进的SQ生成管道，相比基线生成更高质量且多样化的提问；2）作为数据生成器，用于微调极小模型，使其在性能上媲美更大模型。该方法可直接替换现有系统以即时提升表现，也可支持开发本地运行的小型模型，实现快速、私密的问答体验。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。