This paper presents an in-depth study of ChatGPT, a state-of-the-art language model that is
revolutionizing generative text. We provide a comprehensive analysis of its architecture …

Natural Language Generation (NLG) has improved exponentially in recent years thanks to
the development of sequence-to-sequence deep learning technologies such as Transformer …

Large language models (LLMs) have gained widespread adoption in various natural
language processing tasks, including question answering and dialogue systems. However …

Prompt-based methods have been successfully applied in sentence-level few-shot learning
tasks, mostly owing to the sophisticated design of templates and label words. However …

Language modeling studies the probability distributions over strings of texts. It is one of the
most fundamental tasks in natural language processing (NLP). It has been widely used in …

General-purpose language models have demonstrated impressive capabilities, performing
on par with state-of-the-art approaches on a range of downstream natural language …

Zero-shot cross-domain dialogue state tracking (DST) enables us to handle task-oriented
dialogue in unseen domains without the expense of collecting in-domain data. In this paper …

Learning to converse using only a few examples is a great challenge in conversational AI.
The current best conversational models, which are either good chit-chatters (eg, BlenderBot) …

Few-shot learning has drawn researchers' attention to overcome the problem of data
scarcity. Recently, large pre-trained language models have shown great performance in few …

Abstract The Abstraction and Reasoning Corpus (ARC) is a set of procedural tasks that tests
an agent's ability to flexibly solve novel problems. While most ARC tasks are easy for …