Bias Mitigation Techniques in LLM-Based Applications

Bias in Large Language Models (LLMs) refers to unfair or prejudiced outputs. These can reflect societal biases present in training data. Biases may discriminate against certain groups or individuals.

Common Types of Bias in LLMs

1. Gender bias: Stereotyping roles based on gender.
2. Racial bias: Unfair treatment or representation of racial groups.
3. Cultural bias: Favoring certain cultural perspectives over others.
4. Age bias: Discriminating based on age groups.
5. Socioeconomic bias: Favoring certain economic classes.

Bias Mitigation Techniques

1. Diverse and Representative Training Data

• Use data from various sources and demographics.
• Ensure balanced representation of different groups.
• Include data from underrepresented communities.

2. Data Augmentation

• Artificially create or modify training examples.
• Balance datasets by generating synthetic samples for underrepresented groups.
• Use techniques like back-translation or paraphrasing.