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 …

Pretraining Neural Language Models (NLMs) over a large corpus involves chunking the text
into training examples, which are contiguous text segments of sizes processable by the …

The development of language models have moved from encoder-decoder to decoder-only
designs. In addition, we observe that the two most popular multimodal tasks, the generative …

In-context learning (ICL) improves language models' performance on a variety of NLP tasks
by simply demonstrating a handful of examples at inference time. It is not well understood …

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 …

Large pre-trained vision-language models like CLIP have shown great potential in learning
representations that are transferable across a wide range of downstream tasks. Different …

The task of vision-and-language navigation (VLN) requires an agent to follow text
instructions to find its way through simulated household environments. A prominent …

Large language models (LLMs), such as GPT-3 and ChatGPT, have demonstrated
remarkable results in various natural language processing (NLP) tasks with in-context …