We introduce a new problem KTRL+F, a knowledge-augmented in-document search task that necessitates real-time identification of all semantic targets within a document with the awareness of external sources through a single natural query. This task addresses following unique challenges for in-document search: 1) utilizing knowledge outside the document for extended use of additional information about targets to bridge the semantic gap between the query and the targets, and 2) balancing between real-time applicability with the performance. We analyze various baselines in KTRL+F and find there are limitations of existing models, such as hallucinations, low latency, or difficulties in leveraging external knowledge. Therefore we propose a Knowledge-Augmented Phrase Retrieval model that shows a promising balance between speed and performance by simply augmenting external knowledge embedding in phrase embedding. Additionally, we conduct a user study to verify whether solving KTRL+F can enhance search experience of users. It demonstrates that even with our simple model users can reduce the time for searching with less queries and reduced extra visits to other sources for collecting evidence. We encourage the research community to work on KTRL+F to enhance more efficient in-document information access.

翻译：我们提出了一个新问题KTRL+F，即知识增强的文档内搜索任务，该任务要求通过单次自然查询实时识别文档内所有语义目标，并同时利用外部知识源。该任务针对文档内搜索面临以下独特挑战：1）利用文档外部知识扩展目标的相关信息，以弥合查询与目标之间的语义鸿沟；2）在实时应用性与性能之间取得平衡。我们分析了KTRL+F中的多种基线方法，发现现有模型存在幻觉、延迟高或难以利用外部知识等局限性。因此，我们提出了一种知识增强的短语检索模型，通过简单地在短语嵌入中融合外部知识嵌入，在速度与性能之间实现了有前景的平衡。此外，我们进行了一项用户研究，以验证解决KTRL+F问题能否提升用户的搜索体验。结果表明，即使使用我们简单的模型，用户也能以更少的查询次数和更少的外部证据查阅次数缩短搜索时间。我们鼓励研究界致力于KTRL+F的研究，以提升更高效的文档内信息访问能力。