How Today’s Large Language Models (LLMs) Are Created and Where They Are Headed
Large language models (LLMs, from the English term Large Language Models) have been one of the most widely discussed topics in artificial intelligence in recent years. These models, such as ChatGPT, Gemini, or Claude, can generate text, answer questions, translate languages, and help with a wide range of tasks. But how are they actually created and how do they develop? And where is their further development heading? In this article, I will try to explain simply how LLMs work, what their history is, and what the latest trends are bringing.
What Are Large Language Models?
Large language models are computer programs that learn to understand and generate human language. They learn from enormous amounts of text – for example, books, articles, websites, or discussions. As a result, they can answer questions, write essays, summarize texts, or even program. These models are based on a technology called the transformer, which was first introduced in 2017. The transformer allows models to process and understand relationships in text efficiently.
How Are LLMs Created?
- Data collection: First, it is necessary to gather an enormous amount of textual data. This data is obtained from publicly available sources, such as books, websites, or encyclopedias. Data quality is also important – for example, inappropriate or false information is removed.
- Model training: The model “learns” from this data. For example, it learns which words follow one another, what grammatical structures look like, or how to answer questions. Training takes place on powerful computers and can last from weeks to months.
- Fine-tuning and testing: After basic training, the model is further fine-tuned to make it safer and more accurate. It is tested on various tasks and errors are corrected.
- Deployment and updates: The finished model is deployed in practice – for example, in chat applications or search engines. Developers continue to update and improve it based on user feedback.
A Brief History of LLMs
- 2017: The transformer architecture is created, enabling more efficient training of language models.
- 2018–2020: The first large models, such as BERT (Google) and GPT-2 (OpenAI), appear.
- 2022–2023: Models such as GPT-3, GPT-4, Claude, and Gemini arrive, with billions to trillions of parameters and the ability to generate very high-quality text.
- 2024–2025: The latest models (for example, GPT-4o, Gemini 1.5, Claude 3) focus on multimodality (processing text, images, and sound), better context understanding, and safety.
Where Is Development Heading Today?
According to the latest information from 2024 and the beginning of 2025, LLM development is moving in several directions:
- Multimodality: Models are no longer limited to text but can also work with images, sound, or video. This makes it possible, for example, to describe images or analyze videos.
- Efficiency and lower consumption: Developers are trying to make models faster, cheaper to operate, and available on smaller devices as well.
- Safety and reliability: Great emphasis is placed on ensuring that models do not spread disinformation, produce harmful content, and are transparent about how they work.
- Personalization: Models are learning to adapt better to individual users and their needs.
- Openness and collaboration: The number of open models that can be used and improved by a broader community is growing (for example, models from Meta – Llama 3).
Can LLMs Replace Something?
Replace completely: At present, there is no technology that could fully replace LLMs in all the areas where they are used today.
Complement or replace in specific tasks: Yes, in some cases, smaller models, RAG systems, or traditional NLP methods are more suitable – for example, because of speed, privacy protection, costs, or simplicity.
Over the past several years, large language models have evolved from simple tools into highly sophisticated systems that are changing the way we communicate with computers. Development is moving forward rapidly, and in the coming years we can expect further major innovations – for example, even better understanding of human speech, work with multimedia, or greater safety. LLMs are thus becoming an integral part of our digital world.
