# Centralized Database Access: Transformer Framework and LLM/Chatbot Integration-Based Hybrid Model
## 2 Modeling Background  
Chatbots, virtual assistants that communicate with users, have become an integral part of the digital age. These programs, which are ubiquitous in many industries, facilitate interaction between humans and technology and provide fast and efficient solutions for various tasks. A computer program, algorithm, or artificial intelligence that communicates with a person or another participant in the communication is called a chatbot. The chatbot symbolizes a dialog between humans and machines through natural language [16; 17; 18]. Some chatbots work with generative models and use statistical machine translation (SMT) methods to translate input sentences into corresponding responses. Among them, Seq2 Seq (Sequence to Sequence) stands out, an SMT (sequence-to-sequence machine translation) algorithm that uses recurrent neural networks (RNNs) for encoding and decoding inputs into responses--a predominant example of today's practice [19; 20]. OpenAI's GPT technology focuses on an innovative approach to improving language understanding capabilities in machine learning models. It utilizes a dual process that exploits the potential of generative pre-training (GPT) [21]. First, the model is trained with a large amount of text data without a specific task and then tuned to specific tasks. This approach has proven to be effective, as the model outperforms the competition on many language tasks [22; 23]. The underlying generative pre-training technology takes advantage of the abundance of unlabeled text data available on the Internet and provides a scalable solution to the challenges of understanding language [24]. The core idea of this technology lies in generative pre-training, where the model is exposed to a large amount of text data without an assigned task. The goal is to enable the model to predict the next word in a sentence. Through this process, the model acquires the structure of the language, including grammar, semantics, and some context. This helps the model to understand the nuances of language and build a basic and comprehensive understanding of language [24; 25]. The architecture of the novel hybrid model is based on the transformer framework. Transformers use attention mechanisms that allow the model to focus on specific parts of the input data that are relevant to the task. This includes dynamically evaluating the meaning of different words in a sentence, capturing the context and relationships between words in a more efficient way [19; 23]. After generative pre-training of the model, where basic linguistic understanding is acquired, the model is tailored to specific tasks through a process called fine-tuning. This involves making precise adjustments to the model on smaller datasets targeted at these tasks. For example, if the objective is to develop a system for answering questions, the pre-trained model is customized using a dataset specifically designed for questions and answers. This bidirectional approach ensures the retention of the general understanding acquired during pre-training while emphasizing its effectiveness for specific tasks [24]. Education is a sector that is constantly evolving, and as technology advances, the integration of chatbots into education is becoming more commonplace. Whether they help with learning, tutoring, or administrative tasks, chatbots offer new possibilities in the educational context. There are numerous examples of the use of AI and chatbots in this field [26], two of which are highlighted in this study. The first is a chatbot model, the chatbot model described in the paper \"Chatbot: An Education Support System for Student\" by [PERSON] et al. [27], which has been adapted for integration into e-learning platforms. Its purpose is to provide students with an enriching and interactive learning experience that allows them to ask questions and receive immediate informative feedback. The architecture is built to be scalable and capable of managing many interactions with students simultaneously.