Large language models (LLMs) demonstrate powerful capabilities, but they still face
challenges in practical applications, such as hallucinations, slow knowledge updates, and …

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) have achieved remarkable success due to their exceptional
generative capabilities. Despite their success, they also have inherent limitations such as a …

Recent advancements in large language models (LLMs) have achieved promising
performances across various applications. Nonetheless, the ongoing challenge of …

Knowledge Graphs (KGs) play a pivotal role in advancing various AI applications, with the
semantic web community's exploration into multi-modal dimensions unlocking new avenues …

Language Models (LMs) memorize a vast amount of factual knowledge, exhibiting strong
performance across diverse tasks and domains. However, it has been observed that the …

Large Language Models (LLMs) have shown extraordinary capabilities in understanding
and generating text that closely mirrors human communication. However, a primary …

The sequential recommendation problem has attracted considerable research attention in
the past few years, leading to the rise of numerous recommendation models. In this work, we …

Aligned Large Language Models (LLMs) showcase remarkable versatility, capable of
handling diverse real-world tasks. Meanwhile, aligned LLMs are also expected to exhibit …

The effectiveness of Large Language Models (LLMs) in generating accurate responses
relies heavily on the quality of input provided, particularly when employing Retrieval …