Learning with limited labelled data, such as prompting, in-context learning, fine-tuning, meta-
learning, or few-shot learning, aims to effectively train a model using only a small amount of …

Foundation models like CLIP allow zero-shot transfer on various tasks without additional
training data. Yet, the zero-shot performance is less competitive than a fully supervised one …

A well-trained deep neural network on balanced datasets usually exhibits the Neural
Collapse (NC) phenomenon, which is an informative indicator of the model achieving good …

As artificial intelligence (AI) continues to advance, it demonstrates capabilities comparable
to human intelligence, with significant potential to transform education and workforce …

Large Language Models (LLMs) could enhance access to the legal system. However,
empirical research on their effectiveness in conducting legal tasks is scant. We study …

Generalization to new domains not seen during training is one of the long-standing
challenges in deploying neural networks in real-world applications. Existing generalization …

Over the past year a large body of multimodal research has emerged around zero-shot
evaluation using GPT descriptors. These studies boost the zero-shot accuracy of pretrained …

Abstract Large Language Models (LLMs) could be a useful tool for lawyers. However,
empirical research on their effectiveness in conducting legal tasks is scant. We study …

Contrastive Language-Image Pre-training (CLIP) provides a foundation model by integrating
natural language into visual concepts, enabling zero-shot recognition on downstream tasks …

Pre-trained vision-language models have shown impressive success on various computer
vision tasks with their zero-shot generalizability. Recently prompt learning approaches have …