Title: What Is Bias In Llms
Course: LLM_Prompt_Engineering_For_Developers > Section: Module 152: What Is Bias In Llms

What is Bias in LLMs?

Large Language Models (LLMs) are trained on vast datasets that include text from various sources. These models learn patterns in the data, but they can also inadvertently learn biases present in those datasets. Bias in this context refers to systematic favoritism or prejudice in the model’s outputs towards certain groups, ideas, or perspectives. Understanding bias is crucial for developers because it helps in creating more equitable and responsible AI applications.

Types of Bias

1. Data Bias: Arises from unbalanced data representation. If a dataset contains more examples from one group than another, the LLM might over-represent that group in its responses.
2. Algorithmic Bias: Occurs due to the model architecture and the way the model is trained, which may favor certain types of information or interpretations.
3. Societal Bias: Reflects societal stereotypes and prejudices present in the training data.

How Prompts Influence Outcomes