The Language Model Revolution: LLM and SLM Analysis

As technology develops day by day, significant developments have been made in the field of artificial intelligence (AI). In particular, machine learning (ML) and deep learning (DL), as the main technologies that form the basis of artificial intelligence, have offered revolutionary innovations and laid the foundation for future technologies. Traditional artificial intelligence models are based on algorithms that show high performance in certain tasks such as classification, scoring, prediction, and pattern recognition. These algorithms are developed to best perform a specific task, making it difficult for artificial intelligence to be sufficiently effective in areas that require flexibility. Generative artificial intelligence, which has become widespread in recent years, has the ability to produce certain types of content in addition to the competencies of traditional artificial intelligence models. This has revolutionized the field of productivity in artificial intelligence. Generative artificial intelligence language models have gone beyond the limitations and started a new era in artificial intelligence applications. Where traditional artificial intelligence models are limited, language models have come into play, especially with their natural language processing (NLP) capabilities. Rather than just analyzing data, language models can learn the rules of the language and provide human-like responses, produce text, and offer a wider range of applications. In this way, artificial intelligence systems have become more flexible, extensible, and dynamic. With the rise of language models in this field, concepts such as large language models (LLM) and small language models (SLM) have emerged. Large language models have come to the fore as systems that can provide deep knowledge and language production on a wide variety of topics by being trained on huge data sets. Large language models such as ChatGPT are one of the most common and impressive examples in this field. However, small language models, which are smaller and specialized language models, have begun to be used as an alternative to large language models in certain areas because they require less data and processing power. Small language models stand out with their lighter but targeted performance, offering effective solutions, especially in situations where there are resource limitations. At this point, using both large and small versions of language models in the right scenarios provides great advantages in terms of sustainability and efficiency. This study aims to reveal the transformative effect of technology on artificial intelligence and the critical role of language models in this process by evaluating language models and the issues to be considered in the selection of these models.