This comprehensive review delves into the pivotal role of prompt engineering in unleashing
the capabilities of Large Language Models (LLMs). The development of Artificial Intelligence …

This survey addresses the crucial issue of factuality in Large Language Models (LLMs). As
LLMs find applications across diverse domains, the reliability and accuracy of their outputs …

Large language models (LLMs) can store a vast amount of world knowledge, often
extractable via question-answering (eg," What is Abraham Lincoln's birthday?"). However …

When large language models are aligned via supervised fine-tuning, they may encounter
new factual information that was not acquired through pre-training. It is often conjectured that …

Despite efforts to expand the knowledge of large language models (LLMs), knowledge gaps-
-missing or outdated information in LLMs--might always persist given the evolving nature of …

This survey provides an in-depth analysis of knowledge conflicts for large language models
(LLMs), highlighting the complex challenges they encounter when blending contextual and …

The emergence of tools based on large language models (LLMs), such as OpenAI's
ChatGPT and Google's Gemini, has garnered immense public attention owing to their …

Explainable AI (XAI) refers to techniques that provide human-understandable insights into
the workings of AI models. Recently, the focus of XAI is being extended towards Large …

Recently, retrieval augmentation and tool augmentation have demonstrated a remarkable
capability to expand the internal memory boundaries of language models (LMs) by providing …

Large language models are limited by challenges in factuality and hallucinations to be
directly employed off-the-shelf for judging the veracity of news articles, where factual …