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 …

In this study, we propose the use of Variational Autoencoders (VAEs) and Generative
Adversarial Networks (GANs) to generate synthetic data for crop recommendation (CR). CR …

We introduce GLM-130B, a bilingual (English and Chinese) pre-trained language model
with 130 billion parameters. It is an attempt to open-source a 100B-scale model at least as …

The development of Artificial Intelligence Generated Content (AIGC) has been facilitated by
advancements in model algorithms, scalable foundation model architectures, and the …

Pretraining a language model (LM) on text has been shown to help various downstream
NLP tasks. Recent works show that a knowledge graph (KG) can complement text data …

Large pre-trained models, also known as foundation models (FMs), are trained in a task-
agnostic manner on large-scale data and can be adapted to a wide range of downstream …

The contemporary LLMs are prone to producing hallucinations, stemming mainly from the
knowledge gaps within the models. To address this critical limitation, researchers employ …

Recent multimodal models such as DALL-E and CM3 have achieved remarkable progress
in text-to-image and image-to-text generation. However, these models store all learned …

Large Language Models (LLMs) have taken Knowledge Representation--and the world--by
storm. This inflection point marks a shift from explicit knowledge representation to a renewed …

Fine-tuning pretrained language models (LMs) without making any architectural changes
has become a norm for learning various language downstream tasks. However, for non …