Publications (5)
Demo: Statistically Significant Results On Biases and Errors of LLMs Do Not Guarantee Generalizable Results
Jonathan Liu, Haoling Qiu, Jonathan Lasko +3
Recent research has shown that hallucinations, omissions, and biases are prevalent in everyday use-cases of LLMs. However, chatbots used in medical contexts must provide consistent…
Rapid Customization for Event Extraction
Yee Seng Chan, Joshua Fasching, Haoling Qiu +1
We present a system for rapidly customizing event extraction capability to find new event types and their arguments. The system allows a user to find, expand and filter event trigg…
ZS4IE: A toolkit for Zero-Shot Information Extraction with simple Verbalizations
Oscar Sainz, Haoling Qiu, Oier Lopez de Lacalle +2
The current workflow for Information Extraction (IE) analysts involves the definition of the entities/relations of interest and a training corpus with annotated examples. In this d…
QueryBuilder: Human-in-the-Loop Query Development for Information Retrieval
Hemanth Kandula, Damianos Karakos, Haoling Qiu +4
Frequently, users of an Information Retrieval (IR) system start with an overarching information need (a.k.a., an analytic task) and proceed to define finer-grained queries covering…
ExcavatorCovid: Extracting Events and Relations from Text Corpora for Temporal and Causal Analysis for COVID-19
Bonan Min, Benjamin Rozonoyer, Haoling Qiu +2
Timely responses from policy makers to mitigate the impact of the COVID-19 pandemic rely on a comprehensive grasp of events, their causes, and their impacts. These events are repor…