2 citations · 2 across the 3 of their papers we have counts for
4 papers
ODKE+: Ontology-Guided Open-Domain Knowledge Extraction with LLMs
Samira Khorshidi, Azadeh Nikfarjam, Suprita Shankar +9
Knowledge graphs (KGs) are foundational to many AI applications, but maintaining their freshness and completeness remains costly. We present ODKE+, a production-grade system that a…
APE: Active Learning-based Tooling for Finding Informative Few-shot Examples for LLM-based Entity Matching
Kun Qian, Yisi Sang, Farima Fatahi Bayat +11
Prompt engineering is an iterative procedure often requiring extensive manual effort to formulate suitable instructions for effectively directing large language models (LLMs) in sp…
Open Domain Knowledge Extraction for Knowledge Graphs
Kun Qian, Anton Belyi, Fei Wu +15
The quality of a knowledge graph directly impacts the quality of downstream applications (e.g. the number of answerable questions using the graph). One ongoing challenge when build…
FLEEK: Factual Error Detection and Correction with Evidence Retrieved from External Knowledge
Farima Fatahi Bayat, Kun Qian, Benjamin Han +6
Detecting factual errors in textual information, whether generated by large language models (LLM) or curated by humans, is crucial for making informed decisions. LLMs' inability to…