ChatGPT is a state-of-the-art language model that has revolutionized natural language processing by generating human-like text with context and coherence, enabling new possibilities for human-AI interaction [1]. Its impressive performance in various language tasks and benchmarks has established it as one of the leading language models in the world [2]. ChatGPT’s advanced language modeling capabilities have the potential to transform the way we interact with computers and machines by enabling more natural and intuitive communication [3]. Pre-training on massive amounts of text data has equipped ChatGPT with the ability to understand the nuances of language and generate highly accurate responses, even in complex and ambiguous contexts [4]. Additionally, ChatGPT’s ability to learn from both structured and unstructured data makes it a highly flexible and versatile conversational AI tool [5]. Its advanced neural architecture allows it to handle multiple inputs and generate highly personalized responses, leading to a more engaging and satisfying user experience [6].

Moreover, ChatGPT’s ability to learn and adapt to user preferences and conversational styles over time makes it a highly effective tool for building long-term relationships with customers and clients [7]. ChatGPT’s ability to generate coherent and contextually relevant responses in multiple languages has the potential to break down language barriers and promote cross-cultural communication [8]. Its impressive performance in generating creative and novel text has opened up new possibilities for applications in fields such as creative writing, marketing, and advertising [9]. Finally, ChatGPT’s ability to generate highly realistic and convincing conversational responses can transform the way we learn, interact, and communicate with each other in the digital age [10].

ChatGPT was developed through a two-phase process involving unsupervised pre-training followed by supervised fine-tuning [4]. During the pre-training phase, the model was trained on a massive corpus of text utilizing unsupervised learning techniques, including language modeling and masked language modeling. The primary objective of this phase was to enable the model to acquire a comprehensive understanding of the structure of natural language and the complex interrelationships between words and sentences.

Following the pre-training phase, the model was subject to fine-tuning various downstream tasks such as text completion, question-answering, and dialogue generation. The fine-tuning process encompassed the model’s training on labeled datasets comprising task-specific input-output pairs. The model’s parameters were iteratively adjusted to minimize the discrepancies between the model’s predicted outputs and the proper labels for the given tasks [11].

The outcome was a versatile language model that could proficiently execute diverse natural language processing tasks and generate human-like responses to user inputs [4]. ChatGPT has undergone extensive training on a substantial corpus of data and encompasses many parameters that contribute to its exceptional performance on numerous benchmarks evaluating natural language processing.

ChatGPT is a generative AI model that utilizes deep learning methods to process and produce natural language text. Initially launched as a prototype on 30 November 2022, it became available to the public on 30 January 2023 [12]. The model is trained on vast amounts of text data, enabling it to capture human language patterns, nuances, and complexities. The training corpus includes various sources, such as books, articles, reviews, online conversations, and human-generated data, allowing the model to engage in non-trivial dialogues and provide accurate information on diverse topics [13]. By leveraging the GPT (Generative Pretrained Model [14]) as its foundation, ChatGPT not only expands upon its predecessor but also illuminates a promising trajectory for future research endeavors within this field.

The core advantages of such extensive language models are their ability to understand the context of a given input and produce the correct output [15]. This improvement is significant compared to earlier models because earlier models could not interpret the context of the piece of text. Additionally, the text generated by GPT models is of high quality and is difficult to distinguish from human text. The model can provide answers to questions that cannot be obtained from a search on the web. The responses can also be trusted because the model has been trained from extensive input data [13].

2. ChatGPT Training Process

2.1. The Architecture of the Model

2.2. Pre-Processing of Text Data

• Tokenization is a fundamental step in natural language processing that involves segmenting text into discrete units of meaning, known as tokens [27]. The purpose of tokenization is to facilitate the subsequent processing of text by the model. In the case of ChatGPT, tokenization is performed using a pre-trained tokenizer designed explicitly for natural language processing tasks. This tokenizer converts the input text into a sequence of tokens, where each token represents a specific word or subword unit. The resulting token sequence is then used as input for the model in further processing.
• Subword encoding is a widely used technique in natural language processing to handle rare or out-of-vocabulary words in the input text. It involves breaking down the input text into smaller units or subwords, which the model can then process. Subword encoding has been shown to improve the performance of language models on various natural language processing tasks. In the case of ChatGPT, subword encoding is performed using a pre-trained subword encoder, such as the Byte Pair Encoding (BPE) algorithm, specifically designed for natural language processing tasks [27,28].
• Data cleaning is a crucial step in pre-processing text data as it aims to eliminate irrelevant or noisy information from the input text, ultimately improving the quality and suitability of the input data for the model [29]. It involves a series of steps, such as removing punctuation, numbers, and special characters and correcting spelling and grammatical errors, among others. Data cleaning transforms the input text into a more coherent and standardized form, thereby enhancing the model’s ability to capture meaningful patterns in the data.

2.3. Training Algorithm

2.3.1. Pre-Training Phase

2.3.2. Fine-Tuning Phase