Recent work has shown that language models'(LMs) prompt-based learning capabilities
make them well suited for automating data labeling in domains where manual annotation is …

Learning from crowds describes that the annotations of training data are obtained with
crowd-sourcing services. Multiple annotators each complete their own small part of the …

Large language models (LLMs) have demonstrated remarkable capabilities out of box for a
wide range of applications, yet accuracy still remains a major growth area, especially in …

Programmatic Weak Supervision (PWS) and generative models serve as crucial tools that
enable researchers to maximize the utility of existing datasets without resorting to laborious …

Entity matching (EM) refers to the problem of identifying pairs of data records in one or more
relational tables that refer to the same entity in the real world. Supervised machine learning …

Human annotator simulation (HAS) serves as a cost-effective substitute for human
evaluation tasks such as data annotation and system assessment. It is important to …

Human annotator simulation (HAS) serves as a cost-effective substitute for human
evaluation such as data annotation and system assessment. Human perception and …

The objective of legal text entailment is to ascertain whether the assertions in a legal query
logically follow from the information provided in one or multiple legal articles. ChatGPT, a …

Weak supervision enables efficient development of training sets by reducing the need for
ground truth labels. However, the techniques that make weak supervision attractive---such …

The paper introduces LEMR, a framework that reduces annotation costs for model selection
tasks. Our approach leverages ensemble methods to generate pseudo-labels, employs …