  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.

Computation and Language