## 1 Introduction  
Large language models (LLMs), as one of the most advanced and transformative tools of artificial intelligence (AI), have played an unparalleled role in the development of modern applications of natural language processing (NLP) [1]. These models, using deep architectures such as Transformers and self-supervised learning algorithms, have achieved remarkable capabilities in language understanding, text generation, translation, question answering, sentiment analysis, summarization, and even linguistic reasoning [2]. In recent years, models like GPT [3], BERT [4], LLMa[5], ChatGPT [6], and PaLM [7] have not only pushed the technical boundaries of human language understanding but have also penetrated various scientific, industrial, social, and cultural domains. To better understand the current position of LLMs, a brief look at their history is essential. Large language models are the result of decades-long evolution in language and machine learning research. The roots of this journey go back to the 1940s, when the basic concepts of artificial neural networks were introduced by [PERSON] and [PERSON] [8]. In the following decades, statistical language models such as n-gram [9] and Hidden Markov Models (HMMs) [10] emerged, which operated based on word occurrence probabilities but were incapable of understanding contextual meanings and deep semantic relationships. In the 2010s, the emergence of semantic embeddings like Word2 Vec [11] and GloVe [12] enabled the representation of words in a continuous vector space, yet these models remained static and context-insensitive. Subsequently, models based on Recurrent Neural Networks (RNNs) [13] and Long Short-Term Memory (LSTM) [14], despite their ability to model sequential dependencies, struggled with issues such as gradient instability and limited memory. The major transformation came in 2017 with the introduction of Transformer architectures, with a design that, through the self-attention mechanism, overcame the limitations of previous models [2]. This architecture formed the foundation for the development of successful models like BERT, GPT-2, T5, and GPT-3. With the release of GPT-3 in 2020, featuring 175 billion parameters, and later GPT-4 with multimodal input capabilities, LLMs evolved from research tools into powerful engines for language generation. In parallel, models like RoBERTa, BLOOM, LLaMA, PaLM, and LaMDA emerged, aiming to optimize performance, accuracy, resource consumption, and task-specific efficiency [15]. The evolution of language models has progressed from simple statistical systems to today's complex and multilayered neural architectures a journey that, while opening new horizons for human-machine interaction, has also introduced significant challenges. These challenges are not solely technical; they extend to ethical, social, legal, cultural, and economic dimensions as well. In fact, as these models become more advanced and widely adopted, the need for responsibility in their design, interpretation, and use becomes increasingly critical [1]. For instance, in the healthcare domain, serious obstacles include preserving patient privacy, interpretability of outputs, and clinical trustworthiness [16]. In the legal field, hallucination referring to the generation of plausible but factually incorrect or fabricated information in legal references and algorithmic biases can result in unjust decisions being made [17]. In education, concerns such as the weakening of critical thinking skills, dependency on machine-generated content, and reduced educational equity are prominent [18]. A summarized overview of this historical trajectory is presented in Figure 1, highlighting the key milestones in the evolution of large language models. Likewise, in fields such as media, art, programming, agriculture, and energy, LLMs face challenges such as misinformation generation, lack of cultural context understanding, opacity, high energy consumption, and heavy infrastructure costs.