## 2 Background and Conceptual Foundations
### LLM Technologies and Their Impact  
Establishing similarity and homogeneity emerges as the exclusive criterion and principal objective of machine learning algorithms, positioning them at the forefront of their priorities. A proficiently trained machine learning model empowers the anticipation of the classification of novel entities, such as distinct properties, along with predicting the necessary attribute values for objects intended for accurate classification. For instance, envisioning each unique entity defined by specific attribute values and belonging to a designated group enables the determination of attribute values for uncertain entities based on the characteristic value range of their respective group when individual attribute values are unclear. In the realm of contemporary scientific research, the pivotal role in machine learning played by advanced language models, such as large language models, is undeniable. They serve not only as tools but as a revolutionary technology that enables a profound understanding and analysis of intricate language structures. In this study, we postulate that the utilization of LLMs as a research tool extends beyond mere textual analysis or language structure scrutiny. These powerful models allow for a deep dive into diverse fields of knowledge, providing a perspective that transcends traditional research approaches. Throughout the exploration of this new avenue, the research will focus on the unique capabilities offered by LLMs, accompanied by a reflection on the challenges that these advanced models pose to the contemporary research environment. LLM technology has garnered widespread acceptance globally, spanning various entities such as academic institutions, technology firms, healthcare organizations, financial services companies, government agencies, and more. Moreover, an increasing number of enterprises are considering its integration to boost operational efficiency and provide enhanced value to their clients. This is achieved by automating routine tasks and elevating them to a higher level. The boundaries of AI are rapidly expanding, driven by intense competition and substantial investments. The primary objective is to advance generative AI, with the overarching goal of developing LLMs capable of emulating intricate human thought and learning processes. This transformative pursuit is propelling the frontiers of AI forward at a rapid pace [37]. The author [PERSON] [38], in a thought-provoking Forbes article on the new generation of LLMs, discusses three ways that future generative LLMs will progress [37]. Firstly, a novel avenue of AI research aims to enable large language models to bootstrap their own intelligence effectively and learn autonomously. The author refers to a research effort by a group of academics and Google scholars who have outlined their model for self-improvement in their paper \"Large Language Models Can Self-Improve\" [39]. Undoubtedly, self-learning represents a paradigm shift of monumental proportions, potentially mimicking the most sophisticated aspects of human cognition and bringing AI closer to achieving artificial general intelligence (AGI). Secondly, the challenge of incorrect or misleading answers and the need to avoid \"hallucinations\" is addressed. Trustworthy responses from LLMs require the ability to substantiate answers by providing references that validate their accuracy. This practice empowers users to exercise discretion in determining what to accept. Microsoft's Bing and Google's Bard have embraced this approach, a trend expected to be adopted by other entities. Additionally, the colossal scale of LLMs, characterized by billions or even trillions of parameters, necessitates a strategic response. Instead of employing the entire expansive model, which contains trillions of parameters, for each individual prompt, the development of \"sparse\" models is essential.