  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. KTRL+F addresses following unique challenges for in-document search:
1)utilizing knowledge outside the document for extended use of additional
information about targets, and 2) balancing between real-time applicability
with the performance. We analyze various baselines in KTRL+F and find
limitations of existing models, such as hallucinations, high 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 in phrase
embedding. We also conduct a user study to verify whether solving KTRL+F can
enhance search experience for 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.
