Background: Few-shot learning (FSL) is a class of machine learning methods that require
small numbers of labeled instances for training. With many medical topics having limited …

Large Language Models (LLMs) have made remarkable strides in various tasks. Whether
LLMs are competitive few-shot solvers for information extraction (IE) tasks, however, remains …

Named entity recognition in real-world applications suffers from the diversity of entity types,
the emergence of new entity types, and the lack of high-quality annotations. To address the …

In text classification tasks, useful information is encoded in the label names. Label semantic
aware systems have leveraged this information for improved text classification performance …

The objective of few-shot named entity recognition is to identify named entities with limited
labeled instances. Previous works have primarily focused on optimizing the traditional token …

Motivation Few-shot learning that can effectively perform named entity recognition in low-
resource scenarios has raised growing attention, but it has not been widely studied yet in the …

We consider event extraction in a generative manner with template-based conditional
generation. Although there is a rising trend of casting the task of event extraction as a …

Few-shot named entity recognition (NER) exploits limited annotated instances to identify
named mentions. Effectively transferring the internal or external resources thus becomes the …

For named entity recognition (NER) in zero-resource languages, utilizing knowledge
distillation methods to transfer language-independent knowledge from the rich-resource …

Few-shot named entity recognition (NER) targets generalizing to unseen labels and/or
domains with few labeled examples. Existing metric learning methods compute token-level …