Large language models (LLMs) have demonstrated remarkable capabilities across a broad
spectrum of tasks. They have attracted significant attention and been deployed in numerous …

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 …

Recent advances in large language models (LLMs) have demonstrated remarkable
successes in zero-and few-shot performance on various downstream tasks, paving the way …

Assessing factuality of text generated by large language models (LLMs) is an emerging yet
crucial research area, aimed at alerting users to potential errors and guiding the …

Automatically determining whether a text and a corresponding image are semantically
aligned is a significant challenge for vision-language models, with applications in generative …

Natural Language Processing (NLP) research is increasingly focusing on the use of Large
Language Models (LLMs), with some of the most popular ones being either fully or partially …

Abstract We evaluate recent Large Language Models (LLMs) on the challenging task of
summarizing short stories, which can be lengthy, and include nuanced subtext or scrambled …

Abstractive summarization has enjoyed renewed interest in recent years, thanks to pre-
trained language models and the availability of large-scale datasets. Despite promising …

Evaluating the factual consistency of automatically generated summaries is essential for the
progress and adoption of reliable summarization systems. Despite recent advances, existing …

With the recent appearance of LLMs in practical settings, having methods that can effectively
detect factual inconsistencies is crucial to reduce the propagation of misinformation and …