Large Language Models (LLMs) are machine learning models that can understand and generate human language text. They work by analyzing huge language datasets.
What is a large language model (LLM)?
A Large Language Model (LLM) is an artificial intelligence (AI) program that, among other things, can recognize and generate text. LLM is trained on massive datasets — hence the name “Big.” LLM is based on machine learning: more specifically, a type of neural network called a transformer model.
In short, an LLM is a computer program that has been given enough examples to be able to recognize and interpret human language or other types of complex data. Many LLMs are trained using data collected from the internet (thousands or millions of gigabytes of text). However, the quality of the examples will affect how well an LLM learns natural language, so LLM programmers may use more carefully curated datasets.
The LLM uses a type of machine learning called deep learning to understand how characters, words, and sentences work together. Deep learning involves probabilistic analysis of unstructured data, ultimately enabling deep learning models to detect differences between content snippets without human intervention.
What are LLMs used for?
LLMs can be trained on a range of tasks. One of the most well-known applications is their use as generative AI: they can generate text in response to requests or questions. For example, the publicly available LLM ChatGPT can generate essays, poems, and other forms of text based on user input.Any large, complex dataset can be used to train an LLM, including programming languages. Some LLMs can help programmers write code.
- Sentiment analysis
- DNA research
- Customer service
- Chatbots
- Online search
How do LLMs work?
Machine learning and deep learning
Fundamentally, LLM is based on machine learning. Machine learning is a subset of artificial intelligence and refers to the practice of feeding a program large amounts of data to train it to recognize features in the data without human intervention.
LLM uses a type of machine learning called deep learning. Deep learning models essentially train themselves to recognize differences without human intervention, although some human fine-tuning is usually required.
Deep learning uses probabilities to "learn." For example, in the sentence "The Quick Brown Fox Jump over the Lazy Dog," the letters "e" and "O" are the most common, appearing four times each. From this, the deep learning model can (correctly) conclude that these characters are the most likely to appear in English text.
In reality, a deep learning model cannot draw conclusions from a single sentence. But after analyzing trillions of sentences, it can learn enough to predict how to logically complete an incomplete sentence, or even generate its own sentences.
Neural networks
To achieve this type of deep learning, LLM is based on neural networks. Just as the human brain is made up of neurons that connect to each other and send signals, artificial neural networks (often shortened to "neural networks") are made up of interconnected network nodes. They consist of multiple "layers": an input layer, an output layer, and one or more layers in between. The layers pass information to each other only if their own output exceeds a certain threshold.
Transformer models
The specific type of neural network used for LLM is called a Transformer model. Transformer models can learn context,which is especially important for human language, which is highly context-dependent. Transformer models use a mathematical technique called self-attention to detect subtle relationships between elements in a sequence. This allows them to understand context better than other types of machine learning. For example, they can understand how the end of a sentence relates to the beginning and how sentences in a paragraph relate to each other.
