Since the introduction of transformer architecture in 2017 and the subsequent launch of ChatGPT in November 2022, Large Language Models (LLMs) have revolutionized how businesses address complex problems. Despite their potential, these models face significant challenges such as high resource requirements, hallucinations, data security, and more. This article will explore recent advancements in LLMs, including federated learning, split learning, reinforcement learning through human feedback, grouped attention, multi token prediction, use of a mixture of expert architecture etc.

We will discuss how these enhancements improve the efficiency and applicability of LLMs in various business contexts, supported by case studies, expert quotes, and the latest research findings. Will also briefly cover why reduction in hallucinations are critical from perspective of EU AI Act and Biden's executive order.