Papers by Sepideh Mamooler

1 papers
PICLe: Pseudo-annotations for In-Context Learning in Low-Resource Named Entity Detection (2025.naacl-long)

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Challenge: In-context learning is sensitive to the choice of demonstrations and can be used for tasks with few examples.
Approach: They propose a framework for in-context learning with noisy, pseudo-annotated demonstrations . they annotate large quantities of demonstrations in a zero-shot first pass .
Outcome: The proposed framework outperforms ICL on biomedical NED datasets with zero human-annotation.

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