LLM Language Bias is used to describe both intentional and unintentional tendencies of an LLM to generate inferences (results, answers) that reflects certain prejudices  and unfair representation of the subject matter. These biases includes race, ethnicity, sex, cultural, religion and other biases found in humans. There are many sources for these LLM biases including:-

Source Bias: 

Training Data: Source Bias occurs when a model is trained using a specific dataset that arrives at biased inferences of the LLM trainer. For instance, an LLM developer could intentionally, train their model using a Prison dataset that shows only a particular race or age group.

The next time the LLM is prompted for a prison, the inference will be drawn from the Biased dataset that was used in training it.

Societal Bias:- Occurs when a LLM developer injects existing societal Bias in their LLM dataset, either in writing, spoken and language structure and pattern.

Types of Bias ranges from Stereotyping to Representational and Associative Biases. The descriptions of these Biases are as the names states, needing very little explanations..