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

This survey addresses the crucial issue of factuality in Large Language Models (LLMs). As
LLMs find applications across diverse domains, the reliability and accuracy of their outputs …

The goal of information-seeking dialogue is to respond to seeker queries with natural
language utterances that are grounded on knowledge sources. However, dialogue systems …

The propensity of abstractive summarization models to make factual errors has been studied
extensively, including design of metrics to detect factual errors and annotation of errors in …

Dialogue summarization aims to condense the original dialogue into a shorter version
covering salient information, which is a crucial way to reduce dialogue data overload …

As large language models continue to advance in Artificial Intelligence (AI), text generation
systems have been shown to suffer from a problematic phenomenon often termed as" …

Faithfully correcting factual errors is critical for maintaining the integrity of textual knowledge
bases and preventing hallucinations in sequence-to-sequence models. Drawing on humans' …

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

Previous research in multi-document news summarization has typically concentrated on
collating information that all sources agree upon. However, to our knowledge, the …

Large language models (LLMs) frequently hallucinate on abstractive summarization tasks
such as document-based question-answering, meeting summarization, and clinical report …