In-context learning––the ability to configure a model's behavior with different prompts––has
revolutionized the field of natural language processing, alleviating the need for task-specific …

In-context learning, as a new paradigm in NLP, allows the model to rapidly adapt to various
tasks with only a handful of prompts and examples. But in computer vision, the difficulties for …

In-context learning is a recent paradigm in natural language understanding, where a large
pre-trained language model (LM) observes a test instance and a few training examples as …

In this paper, we study the challenging instance-wise vision-language tasks, where the free-
form language is required to align with the objects instead of the whole image. To address …

Large language models have shown tremendous performance in a variety of tasks. In-
context learning--the ability to improve at a task after being provided with a number of …

Language is crucial for human intelligence, but what exactly is its role? We take language to
be a part of a system for understanding and communicating about situations. In humans …

Large pretrained language models (LMs) have shown impressive In-Context Learning (ICL)
ability, where the model learns to do an unseen task simply by conditioning on a prompt …

A spoken language generation system has been developed that learns to describe objects
in computer-generated visual scenes. The system is trained by a 'show-and-tell'procedure in …

Learning effective representations simultaneously from multiple tasks in a unified network
framework is a fundamental paradigm for multi-task dense visual scene understanding. This …

Large language models have an exceptional capability to incorporate new information in a
contextual manner. However, the full potential of such an approach is often restrained due to …