## 2 Background
### Machine Translaters  
Natural Language Processing is a subfield of artificial intelligence that focuses on enabling computers to process and understand human language. It encompasses a wide range of tasks, including text and sentiment analysis, question answering, and machine translation. This study is situated within the latter domain. According to the European Association for Machine Translation [14], machine translation (MT) is defined as the application of computers to the task of translating texts from one natural language to another. Before the advent of Large Language Models (LLMs), MT was primarily achieved through Rule-Based Machine Translation and Statistical Machine Translation. The first ones relied on predefined linguistic rules and dictionaries to transform text between languages, necessitating extensive linguistic expertise and manual programming [15]. In contrast, statistical machine-based translators utilized large parallel corpora to identify translation patterns and estimate probabilistic correspondences between phrases or words in different languages [16]. The subsequent shift to Neural Machine Translation introduced encoder-decoder architectures based on Recurrent Neural Networks, often enhanced with attention mechanisms to improve translation quality [17]. This transition marked the emergence of more advanced techniques, culminating in the development of LLMs based on Transformer architectures [18]. LLMs, such as GPT and BERT [19], are trained on vast amounts of textual data and are capable of understanding, generating, and manipulating natural language. Their ability to capture complex linguistic patterns underpins many advanced NLP applications, including MT. Consequently, two broad categories of translation systems have emerged: dedicated machine translation systems and generative artificial intelligence (GenAI) models, which incorporate translation as one of their multiple capabilities. Initially, translation systems such as DeepL [20], Google Translate [21], Bing Translator [22], Systran [23], and Reverso [24] employed rule-based models. These systems later evolved through statistical methodologies before transitioning to deep neural networks and Transformer-based architectures, significantly enhancing translation fluency and accuracy. In parallel, GenAI models such as ChatGPT-4o mini [25], Gemini [26], and Copilot [27] have emerged as versatile tools capable of generating content, including translations. While traditional MT systems are optimized for linguistic precision, GenAI models offer greater adaptability across diverse tasks. Both approaches present distinct advantages depending on the use case. Dedicated MT systems are designed to maximize linguistic accuracy, whereas GenAI models exhibit higher flexibility, allowing them to handle a broader range of language-related tasks. However, GenAI tends to prioritize fidelity to the source text, sometimes at the expense of naturalness in certain contexts. Notably, among professional translators who incorporate GenAI into their workflows, ChatGPT is the most widely used tool, followed by Microsoft Copilot [12]. Furthermore, according to Google Trends data [28], Google Translate has historically been the most widely used MT system. However, since the launch of ChatGPT in November 2022, its adoption has grown rapidly, surpassing Google Translate as of September 2024. Another key distinction between machine translation systems and GenAI lies in the use of prompts. Prompts serve as the primary interface through which users interact with GenAI models, typically consisting of a sequence of characters that correspond to natural language text. These inputs are processed by the model, which generates responses based on its underlying architecture and training data. In contrast, traditional machine translation systems directly accept the text to be translated as input without requiring explicit task specification beyond selecting the target language.