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

Recently, large language models (LLMs) have attracted considerable attention due to their
remarkable capabilities. However, LLMs' generation of biased or hallucinatory content …

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

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 …

Language models demonstrate both quantitative improvement and new qualitative
capabilities with increasing scale. Despite their potentially transformative impact, these new …

This research paper focuses on the challenges posed by hallucinations in large language
models (LLMs), particularly in the context of the medical domain. Hallucination, wherein …

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

The prevalence and strong capability of large language models (LLMs) present significant
safety and ethical risks if exploited by malicious users. To prevent the potentially deceptive …

We study generating abstractive summaries that are faithful and factually consistent with the
given articles. A novel contrastive learning formulation is presented, which leverages both …

Before deploying a language model (LM) within a given domain, it is important to measure
its tendency to generate factually incorrect information in that domain. Existing factual …