Presenting Papers
New!
STAR: Boosting Low-Resource Information Extraction by Structure-to-Text Data Generation with Large Language ModelsAAAI,
2024
We propose STAR, a structure-to-text data generation method for complicated structure prediction tasks that first generates complicated event structures (Y) and then generates input passages (X), all with Large Language Models. We further reduce errors and improve data quality through self-reflection error identification and self-refinement with iterative revision. We show that the data generated by STAR significantly improves the performance of low-resource event extraction and relation extraction tasks, even surpassing the effectiveness of human-curated data.
New!
MIDDAG: Where Does Our News Go? Investigating Information Diffusion via Community-Level Information PathwaysMingyu Derek Ma,
Alexander K. Taylor,
Nuan Wen,
Yanchen Lin,
Po-Nien Kung,
Wenna Qin,
Shicheng Wen,
Azure Zhou,
Diyi Yang,
Xuezhe Ma,
Nanyun Peng,
Wei Wang AAAI Demonstrations,
2024
We present MIDDAG, an intuitive, interactive system that visualizes the information propagation paths on social media triggered by COVID-19-related news articles accompanied by comprehensive insights including user/community susceptibility level, as well as events and popular opinions raised by the crowd while propagating the information.