2 citations · 3 across the 5 of their papers we have counts for
5 papers
ConvKGYarn: Spinning Configurable and Scalable Conversational Knowledge Graph QA datasets with Large Language Models
Ronak Pradeep, Daniel Lee, Ali Mousavi +7
The rapid advancement of Large Language Models (LLMs) and conversational assistants necessitates dynamic, scalable, and configurable conversational datasets for training and evalua…
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…
Increasing Coverage and Precision of Textual Information in Multilingual Knowledge Graphs
Simone Conia, Min Li, Daniel Lee +3
Recent work in Natural Language Processing and Computer Vision has been using textual information -- e.g., entity names and descriptions -- available in knowledge graphs to ground…
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…
Construction of Paired Knowledge Graph-Text Datasets Informed by Cyclic Evaluation
Ali Mousavi, Xin Zhan, He Bai +9
Datasets that pair Knowledge Graphs (KG) and text together (KG-T) can be used to train forward and reverse neural models that generate text from KG and vice versa. However models t…