Artificial intelligence, especially in the form of Large Language Models (LLMs) such as ChatGPT, is based on complex algorithms and enormous amounts of data. These models are trained through a process called machine learning, specifically using deep learning, a subset of machine learning. Deep learning is based on artificial neural networks, which are modeled on the structure of the neural networks of the human brain.

The training phase of an LLM involves reading and processing large amounts of text data. These texts come from various sources such as books, articles, websites and other media. Through training, the models learn to recognize patterns and relationships in the data, understand words and sentences in context and even mimic the style and tone of different types of text. The goal is to predict the probability of which word or phrase will continue a sentence based on the previous context.

After initial training, LLMs often go through a phase of fine-tuning where they are adapted to specific tasks or domains. This can increase the accuracy and relevance of responses, especially when used in specialized areas such as academic writing.

Despite their advanced capabilities, LLMs are prone to a phenomenon known as “hallucination”. This is where the model generates answers that are falsely confident but factually inaccurate or completely wrong. This problem occurs because the models have no real understanding or awareness. They are based solely on statistical patterns in the data they have seen during training.

1. If the training material contains misinformation, bias or inadequate examples for certain topics or contexts, the model may learn erroneous patterns and reproduce them in its responses.

2. LLMs may tend to infer general rules from the specific training data that do not apply in novel or unfamiliar contexts.

3. Because LLMs learn primarily from the texts provided to them, they lack deeper world knowledge and the ability to validate their statements in a testable way.