Many governments worldwide have proposed transitioning from a fossil-based economy to a
bioeconomy to address climate change, resource depletion, and other environmental …

Active learning (AL) is a human-and-model-in-the-loop paradigm that iteratively selects
informative unlabeled data for human annotation, aiming to improve over random sampling …

Annotating abusive language is expensive, logistically complex and creates a risk of
psychological harm. However, most machine learning research has prioritized maximizing …

As large language models (LLMs) continue to evolve, understanding and quantifying the
uncertainty in their predictions is critical for enhancing application credibility. However, the …

This work presents a new, original document classification dataset, BioSift, to expedite the
initial selection and labeling of studies for drug repurposing. The dataset consists of 10,000 …

We present ALToolbox–an open-source framework for active learning (AL) annotation in
natural language processing. Currently, the framework supports text classification, sequence …

Though Large Language Models (LLMs) have demonstrated the powerful capabilities of few-
shot learning through prompting methods, supervised training is still necessary for complex …

Abstract Information from social media can provide essential information for emergency
response during natural disasters in near real-time. However, it is a difficult task to identify …

Despite achieving state-of-the-art results in nearly all Natural Language Processing
applications, fine-tuning Transformer-based language models still requires a significant …

Citation Worthiness Detection (CWD) consists in determining which sentences, within an
article or collection, should be backed up with a citation to validate the information it …