Large language model

Computers can learn to use words.

They read a huge amount of text. This helps them learn how people talk. They can write stories or answer questions.

These tools use patterns from human writing. They can also translate words into new ways. Sometimes they can even look at pictures. Some tools are very expensive to build. They are getting better every year.

A large language model, or LLM, is a type of computer program.

It is trained on a vast amount of data. This data is mostly text written by humans. By reading this text, the model learns patterns in how we speak and write.

This helps the LLM do many tasks. It can summarize stories or translate languages. It can even reason through problems.

Most modern LLMs use a transformer architecture. This is a special way of organizing the model's parts. It helps the model handle long pieces of text very well. Some models are also multimodal. This means they can process more than just words. They can also understand images or sounds.

Training these models takes a lot of work. It requires very big computers and a lot of power. For example, training the PaLM model cost 8 million dollars.

Because they learn from human writing, they can sometimes make mistakes. They might repeat biases found in the original text. Scientists use benchmarks to test how well these models work.

A large language model, or LLM, is a powerful computer program.

It is designed to understand and use human language. These models are trained on huge amounts of text data. This data helps the model learn how words fit together. LLMs can do many different jobs with text. They can summarize long stories or translate one language to another. They can even use reasoning to solve problems.

To work, an LLM must turn words into numbers. This first step is called tokenization.

Computers cannot read letters, so they use small pieces called tokens. A method called byte-pair encoding helps group common characters together. Once the text is tokens, the model uses math to find patterns. Most modern LLMs use a special structure called a transformer architecture. This allows the model to look at many parts of a sentence at once. This makes it much faster and better at handling long pieces of text.

People have been working on language models for a long time. In the early 1990s, IBM researchers worked on ways to translate words. By 2001, models were trained on 300 million words. In 2017, Google researchers changed everything with a paper called "Attention Is All You Need." This paper introduced the transformer architecture we use today. In 2018, a model named BERT became very popular. Later, OpenAI released GPT-1 in 2018 and GPT-2 in 2019.

There are many different famous models to know. GPT-3 arrived in 2020 and was very large. In 2022, the chatbot ChatGPT became famous all over the world. GPT-4 came in 2023 and could handle many different types of information.

In 2024, OpenAI released o1, which thinks through steps before answering. There are also open-weight models like LLaMA and Mistral. In January 2025, DeepSeek released R1, which has 671 billion parameters. Training these huge models is very expensive. For example, the PaLM model cost 8 million dollars to train.

LLMs are a lot like how you learn to read. When you read books, you learn how sentences are built. LLMs do the same thing by looking at massive datasets. However, they can sometimes learn bad habits from the text. If the human text has mistakes or biases, the LLM might repeat them. Scientists use benchmarks to test if a model is accurate and safe.

Some new models are also multimodal. This means they can work with images and audio too. This connects text to the rest of the digital world.

A large language model, or LLM, is a sophisticated computational model designed for natural language processing. These models are built to handle tasks like language generation, summarization, translation, and reasoning.

Most powerful LLMs are generative pre-trained transformers, commonly known as GPTs. They function by using massive amounts of artificial intelligence parameters. These parameters allow the model to identify and replicate patterns found in human-written text. Because they learn from human data, they can reflect the complexities of natural language. However, they may also inherit inaccuracies or biases present in that original training data.

To process language, a model must first perform a step called tokenization.

Since machine learning algorithms process numbers rather than letters, text must be converted into a numerical format. First, the system decides on a vocabulary. It then assigns a unique integer index to each entry in that vocabulary. Finally, an embedding is associated with each index. One common method is byte-pair encoding, or BPE. This process starts with individual characters and merges the most frequent adjacent pairs into larger groups. This continues until a specific vocabulary size is reached. This method helps compress datasets for more efficient training.

Modern LLMs rely on a specific structure called the transformer architecture.

This architecture was introduced by Google researchers in the 2017 paper "Attention Is All You Need." Before transformers, researchers used recurrent neural networks or LSTM-based encoder-decoder architectures. Transformers are superior because they allow for more efficient parallelization. They can also handle longer contexts and scale better with massive data volumes. This architecture enables emergent behaviors, such as compositional reasoning and the ability to find specific data within huge sets. Some newer models even explore different structures like Mamba, which is a state space model.

The history of these models shows a rapid increase in complexity. In the early 1990s, IBM pioneered word alignment for machine translation. By 2001, smoothed n-gram models were trained on 300 million words. The 2010s saw a shift toward neural networks and word embeddings, such as Word2Vec in 2013. After the transformer breakthrough, BERT was introduced in 2018 as an encoder-only model. OpenAI later released the GPT series, starting with GPT-1 in 2018. GPT-2 gained attention in 2019, and GPT-3 arrived in 2020. The release of ChatGPT in 2022 brought these models into the public eye.

Training these models requires immense computational resources and financial investment. The cost of training has grown alongside the size of the models. In 2019, training the 1.5-billion-parameter GPT-2 cost approximately $50,000. By 2022, training the 540-billion-parameter PaLM model cost about $8 million. The Megatron-Turing NLG 530B model cost around $11 million in 2021. There is no official threshold for what makes a model "large," but the parameter counts continue to climb. For example, the DeepSeek R1 model released in 2025 features 671 billion parameters.

Researchers use benchmark evaluations to test a model's abilities. These tests measure reasoning, factual accuracy, safety, and alignment. However, optimizing a model just to pass a benchmark can be problematic. It might result in a model that follows benchmark outputs without gaining genuine, robust capabilities. To improve behavior, models undergo fine-tuning. This often involves instruction fine-tuning to help the model follow user commands. Another method is reinforcement learning from human feedback, or RLHF. This process shapes how the model responds to different prompts.

The field is also expanding through multimodality and open-weight models. Multimodal large language models, or MLLMs, can process more than just text. They can also understand or generate images, audio, and 3D meshes. Additionally, open-weight models like LLaMA, Mistral, and DeepSeek R1 are becoming more popular. These models allow for greater transparency and community participation. Researchers have found that community-driven contributions can improve model efficiency. This openness helps accelerate innovation and makes AI research more reproducible across the scientific community.