Abstract Hierarchical Text Classification poses the difficult challenge of classifying
documents into multiple labels organized in a hierarchy. The vast majority of works aimed to …

We study open-world multi-label text classification under extremely weak supervision (XWS),
where the user only provides a brief description for classification objectives without any …

Purpose: In this paper, we present an automated method for article classification, leveraging
the power of Large Language Models (LLM). The primary focus is on the field of …

Few-shot text classification has seen significant advancements, particularly with entailment-
based methods, which typically use either class labels or intensional definitions of class …

Text classification is crucial for applications such as sentiment analysis and toxic text
filtering, but it still faces challenges due to the complexity and ambiguity of natural language …

Background: In this paper, we present an automated method for article classification,
leveraging the power of large language models (LLMs). Objective: The aim of this study is to …

Automatic biomedical annotation is essential for advancing medical research, diagnosis,
and treatment. However, it presents significant challenges, especially when entities are not …

In the rapidly evolving landscape of spoken question-answering (SQA), the integration of
large language models (LLMs) has emerged as a transformative development …

Paraphrase identification (PI) and natural language inference (NLI) are two important tasks
in natural language processing. Despite their distinct objectives, an underlying connection …

This study investigates the application of a state-of-the-art zero-shot and few-shot natural
language processing (NLP) technique for text classification tasks in Catalan, a moderately …