## 1 Introduction
Moreover, inequality in access to computing resources and trainable data has created a technological divide between developed and developing societies [19]. Given the rapidly expanding use of LLMs across all dimensions of personal and societal life, a comprehensive and interdisciplinary examination of their challenges and potential consequences has become an urgent necessity. Accordingly, this article adopts an analytical and practical approach to explore the multifaceted aspects of LLMs across domains such as healthcare, education, law, media, business, art, agriculture, politics, and science. Moreover, the current research attempts to develop a structured taxonomy and provide a comprehensive review of the challenges, limitations, solutions, and future directions in the era of LLMs. It should be noted that this study adopts a hybrid scoping and mapping review approach. Additionally, the analysis examines key academic databases, including Web of Science, Scopus, and Google Scholar. The forthcoming sections aim to explain the structure, applications, challenges, and future directions of LLMs by dissecting and analyzing the various dimensions of this technology. In Section 2, a general and comprehensive definition of LLMs is presented. To provide the reader with an initial understanding of the nature of this technology, foundational concepts related to language models are introduced. This is followed by a detailed examination of the architectural structure of LLMs. Moreover, the types of data required to train these models are thoroughly discussed to help readers gain a deeper understanding of how LLMs learn and acquire their capabilities. Overall, this section offers a cohesive and comprehensive overview of the theoretical and technical foundations of LLMs. Section 3 focuses on the challenges that LLMs face in various aspects of everyday life. It examines the wide-ranging applications of these models and the associated issues across diverse domains such as medicine, finance, business, industry, agriculture, energy, education, research, programming, content creation, art, law, and even tourism. At the end of this section, for each identified challenge, a practical and actionable solution is proposed, providing a strategic outlook for addressing potential problems. In Section 4, the article delves into the more technical and specialized challenges related to the development and deployment of LLMs. This includes an analysis of issues such as fairness and bias mitigation, countering malicious attacks and potential misuse, integrating heterogeneous data, advancements in multimodal large language model research, supporting low-resource languages, ensuring continuous and stable learning in LLMs, and promoting ethical and responsible use of these technologies. Each of these topics is examined through relevant examples and practical analysis, and concrete solutions are offered to mitigate risks and enhance effectiveness in each area. Figure 1: Timeline of key developments in the evolution of large language models from 1943 to 2023. Section 5 takes a forward-looking approach, exploring the future trajectory of LLMs. This section investigates key questions, such as the following: In which domains will LLMs see the most growth? What technological advancements are necessary for their evolution? And what emerging risks may arise? It emphasizes the importance of interdisciplinary collaboration, the development of legal and regulatory frameworks, and the integration of human-centered and ethical perspectives in the continued evolution of these models. Finally, Section 6 provides a comprehensive conclusion that synthesizes the main points discussed throughout the article. This concluding section aims to present a clear and accessible picture of the current landscape, challenges, and opportunities surrounding LLMs one that not only enhances scientific understanding but also paves the way for future research and informed decision making.