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

As large language models continue to develop in the field of AI, text generation systems are
susceptible to a worrisome phenomenon known as hallucination. In this study, we …

While large language models (LLMs) have demonstrated remarkable capabilities across a
range of downstream tasks, a significant concern revolves around their propensity to exhibit …

The emergence of large language models (LLMs) has marked a significant breakthrough in
natural language processing (NLP), fueling a paradigm shift in information acquisition …

Large language models (LLMs), such as ChatGPT, are prone to generate hallucinations, ie,
content that conflicts with the source or cannot be verified by the factual knowledge. To …

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 …

Large Language Models (LLMs) have drawn a lot of attention due to their strong
performance on a wide range of natural language tasks, since the release of ChatGPT in …

Pretrained language models (LMs) are susceptible to generate text with nonfactual
information. In this work, we measure and improve the factual accuracy of large-scale LMs …

Grounded text generation systems often generate text that contains factual inconsistencies,
hindering their real-world applicability. Automatic factual consistency evaluation may help …

Conversational information seeking (CIS) is concerned with a sequence of interactions
between one or more users and an information system. Interactions in CIS are primarily …