KAIST AI Researchers Introduce KTRL+F: A Knowledge-Augmented in-Document Search Task that Necessitates Real-Time Identification of Semantic Targets within a Document

KTRL+F task is a knowledge-augmented in-document search problem that requires real-time identification of semantic targets within a document, incorporating external knowledge through a single natural query. Existing models face challenges such as hallucinations, low latency, and difficulty leveraging superficial knowledge. To address this, researchers from KAIST AI and Samsung Research propose a Knowledge-Augmented Phrase Retrieval […]

The post KAIST AI Researchers Introduce KTRL+F: A Knowledge-Augmented in-Document Search Task that Necessitates Real-Time Identification of Semantic Targets within a Document appeared first …

ai researchers ai shorts applications artificial intelligence challenges document editors pick face hallucinations identification knowledge language model large language model latency low machine learning natural query real-time researchers search semantic staff targets tech news technology through