Large language models (LLMs) are a special class of pretrained language models (PLMs)
obtained by scaling model size, pretraining corpus and computation. LLMs, because of their …

This article surveys and organizes research works in a new paradigm in natural language
processing, which we dub “prompt-based learning.” Unlike traditional supervised learning …

The recent success of zero-and few-shot prompting with models like GPT-3 has led to a
paradigm shift in NLP research. In this paper, we study its impact on text summarization …

Abstract⚠ This paper contains prompts and model outputs that are offensive in nature. When
trained on large, unfiltered crawls from the Internet, language models pick up and reproduce …

Long documents such as academic articles and business reports have been the standard
format to detail out important issues and complicated subjects that require extra attention. An …

Large-scale language models are rapidly improving, performing well on a wide variety of
tasks with little to no customization. In this work we investigate how language models can …

Selecting the “right” amount of information to include in a summary is a difficult task. A good
summary should be detailed and entity-centric without being overly dense and hard to …

Contrastive learning models have achieved great success in unsupervised visual
representation learning, which maximize the similarities between feature representations of …

We introduce a simple but flexible mechanism to learn an intermediate plan to ground the
generation of abstractive summaries. Specifically, we prepend (or prompt) target summaries …

Text summarization is the process of condensing a long text into a shorter version by
maintaining the key information and its meaning. Automatic text summarization can save …