AI Director and AI Independant Researcher here.

Because an LLM is fundamentally trained to predict the most probable next token, it does not actually “know” whether a statement is true or false. Its objective during training is not factual correctness, it is statistical likelihood given the text it has seen.

When the model generates an answer, it is essentially estimating:

Probability of (next token given previous tokens)

This means it will produce text that looks plausible within the patterns of language it learned, even if the information is incorrect. There are a few reasons this leads to hallucinations:

• The model optimizes for what words tend to follow other words, not whether the statement is factually correct. If a pattern appears believable in language, the model may generate it even if it is wrong.

• Training data is finite and frozen at a certain time. If the model has limited examples of a topic, it may interpolate from related patterns and generate something that sounds reasonable but isn’t accurate.

• The model is designed to always continue the sequence unless explicitly instructed to stop. If it does not actually know the answer, it may still generate one because predicting something is part of its objective.

• Transformers are extremely good at combining patterns. Sometimes they merge multiple partially related concepts into a response that is grammatically correct but factually incorrect.

A language model doesn’t retrieve facts the way a database does, it generates text that statistically fits the context, which is why it can sometimes produce convincing but incorrect information.

There is no concept of "correct", just what word should follow the last word.

AI Director and AI Independant Researcher here.

Because an LLM is fundamentally trained to predict the most probable next token, it does not actually “know” whether a statement is true or false. Its objective during training is not factual correctness, it is statistical likelihood given the text it has seen.

When the model generates an answer, it is essentially estimating:

Probability of (next token given previous tokens)

This means it will produce text that looks plausible within the patterns of language it learned, even if the information is incorrect. There are a few reasons this leads to hallucinations:

The model optimizes for what words tend to follow other words, not whether the statement is factually correct. If a pattern appears believable in language, the model may generate it even if it is wrong.

Training data is finite and frozen at a certain time. If the model has limited examples of a topic, it may interpolate from related patterns and generate something that sounds reasonable but isn’t accurate.

The model is designed to always continue the sequence unless explicitly instructed to stop. If it does not actually know the answer, it may still generate one because predicting something is part of its objective.

Transformers are extremely good at combining patterns. Sometimes they merge multiple partially related concepts into a response that is grammatically correct but factually incorrect.

A language model doesn’t retrieve facts the way a database does, it generates text that statistically fits the context, which is why it can sometimes produce convincing but incorrect information.

There is no concept of "correct", just what word should follow the last word.