Large language models (LLMs) have demonstrated extraordinary capabilities and
contributed to multiple fields, such as generating and summarizing text, language …

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

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

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

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 …

Instruction-following models are attractive alternatives to fine-tuned approaches for question
answering (QA). By simply prepending relevant documents and an instruction to their input …

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 …

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

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 …

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 …