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

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

Inspired by the superior language abilities of large language models (LLM), large vision-
language models (LVLM) have been recently explored by integrating powerful LLMs for …

Generative Large Language Models (LLMs) such as GPT-3 are capable of generating highly
fluent responses to a wide variety of user prompts. However, LLMs are known to hallucinate …

Hallucination has been widely recognized to be a significant drawback for large language
models (LLMs). There have been many works that attempt to reduce the extent of …

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

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 …

The performance of text summarization has been greatly boosted by pre-trained language
models. A main concern of existing methods is that most generated summaries are not …

Recently developed large language models have achieved remarkable success in
generating fluent and coherent text. However, these models often tend to'hallucinate'which …