Large Language Models (LLMs) went from non-existent to ubiquitous in the machine
learning discourse within a few years. Due to the fast pace of the field, it is difficult to identify …

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 …

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 …

Ensuring alignment, which refers to making models behave in accordance with human
intentions [1, 2], has become a critical task before deploying large language models (LLMs) …

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 …

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 …

A recent focus of large language model (LLM) development, as exemplified by generative
search engines, is to incorporate external references to generate and support its claims …

The Generative Pre-trained Transformer (GPT) models, renowned for generating human-like
text, occasionally produce “hallucinations”-outputs that diverge from human expectations …

Large Language Models (LLMs) have recently emerged as an effective tool to assist
individuals in writing various types of content, including professional documents such as …