The Development of Large Language Models from Past to Present

Abstract

LLMs (Large Language Models) are a groundbreaking technology for human‑computer interaction. LLMs, which are used in many natural language processing areas such as text generation, question-answer systems, translation and coding, show high success in complex language tasks thanks to their transformer architecture and self-attention mechanism. The development process that started with word embedding techniques has made significant progress with models such as BERT and GPT. LLMs trained with large datasets and powerful GPUs have also significantly improved grammar and context learning. Adapter-based fine-tuning methods increase the accessibility of models by reducing training costs. LLMs, which have revolutionized fields such as health, law, finance, education, and content production, can be integrated with different data types with multimodal models. LLMs have potential future uses in areas such as personalized education, autonomous systems and bio-artificial intelligence integration. However, at this point, challenges such as high computational costs and data quality should also be considered. In conclusion, LLMs have revolutionized the field of natural language processing due to their ability to understand and generate human-like language. Efficient algorithms and innovative solutions are needed in the development and dissemination of language models. In the future, it is expected that LLMs will have a wide range of applications and will be more used and visible in many fields. From a Management Information Systems perspective, the effective integration of LLMs into corporate processes is expected to play critical role in decision support, information management, and increasing overall managerial efficiency.

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