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 …

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

Retrieval-Augmented Generation (RAG) merges retrieval methods with deep learning
advancements to address the static limitations of large language models (LLMs) by enabling …

As organizations increasingly rely on diverse data sources like invoices and surveys,
efficient Information Extraction (IE) is crucial. Natural Language Processing (NLP) enhances …

This study investigates the integration of Retrieval Augmented Generation (RAG) into the
Mistral 8x7B Large Language Model (LLM), which already uses Mixture of Experts (MoE), to …

Background This study assessed the consistency and accuracy of responses provided by
two artificial intelligence (AI) applications, ChatGPT and Google Bard (Gemini), to questions …

Improving the performance of large language models (LLMs) in complex question-
answering (QA) scenarios has always been a research focal point. Recent studies have …

Recent studies have explored the use of Large Language Models (LLMs) with Retrieval
Augmented Generation (RAG) for Knowledge Graph Question Answering (KGQA). They …

Abstract The advent of Large Language Models opened new perspectives concerning their
usage within the digital health domain. However, their intrinsic probabilistic and …

In the fast-paced realm of digital transformation, businesses are increasingly pressured to
innovate and boost efficiency to remain competitive and foster growth. Large Language …