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cs.CL2024
PICLe: Pseudo-Annotations for In-Context Learning in Low-Resource Named Entity Detection
Sepideh Mamooler, Syrielle Montariol, Alexander Mathis +1
In-context learning (ICL) enables Large Language Models (LLMs) to perform tasks using few demonstrations, facilitating task adaptation when labeled examples are hard to obtain. How…
cs.CL2022
An Efficient Active Learning Pipeline for Legal Text Classification
Sepideh Mamooler, Rémi Lebret, Stéphane Massonnet +1
Active Learning (AL) is a powerful tool for learning with less labeled data, in particular, for specialized domains, like legal documents, where unlabeled data is abundant, but the…