Transfer learning–ie, further fine-tuning a pre-trained model on a downstream task–can
confer significant advantages, including improved downstream performance, faster …

Transfer learning–ie, further fine-tuning a pre-trained model on a downstream task–can
confer significant advantages, including improved downstream performance, faster …

This work aims to explore how human assessments and AI predictions can be combined to
identify misinformation on social media. To do so, we design a personalized AI which …

Large language models (LLMs) are susceptible to a variety of risks, from non-faithful output
to biased and toxic generations. Due to several limiting factors surrounding LLMs (training …

Audio-visual learning seeks to enhance the computer's multi-modal perception leveraging
the correlation between the auditory and visual modalities. Despite their many useful …

While fine-tuning pretrained models has become common practice, these models often
underperform outside their specific domains. Recently developed model merging …

In the deployment of real-world text classification models, label scarcity is a common
problem and as the number of classes increases, this problem becomes even more …

Automatic biomedical relation extraction (bioRE) is an essential task in biomedical research
in order to generate high-quality labelled data that can be used for the development of …

While biomedical relation extraction (bioRE) datasets have been instrumental in the
development of methods to support biocuration of single variants from texts, no datasets are …

Abstract Topical Text Classification (TTC) is an ancient, yet timely research area in natural
language processing, with many practical applications. The recent dramatic advancements …