Large Language Models (LLMs) have recently transformed both the academic and industrial
landscapes due to their remarkable capacity to understand, analyze, and generate texts …

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 have achieved impressive performance on various natural
language processing tasks. However, so far they have been evaluated primarily on …

As one of the most advanced techniques in AI, Retrieval-Augmented Generation (RAG) can
offer reliable and up-to-date external knowledge, providing huge convenience for numerous …

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

Retrieval-Augmented Generation (RAG) is a promising approach for mitigating the
hallucination of large language models (LLMs). However, existing research lacks rigorous …

We explore machine unlearning (MU) in the domain of large language models (LLMs),
referred to as LLM unlearning. This initiative aims to eliminate undesirable data influence …

The rise of powerful large language models (LLMs) brings about tremendous opportunities
for innovation but also looming risks for individuals and society at large. We have reached a …

We envision" AI scientists" as systems capable of skeptical learning and reasoning that
empower biomedical research through collaborative agents that integrate AI models and …

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 …