Debiasing LLMs with Gender-Aware Prompting
A novel approach that reduces bias without sacrificing performance
DR.GAP introduces a new method for reducing gender bias in large language models through demonstration and reasoning-based prompting techniques.
- Addresses bias without requiring access to model weights
- Maintains model utility while reducing discriminatory outputs
- Demonstrates better generalizability than existing approaches
- Provides a practical solution for ethical AI deployment
Security Impact: By reducing harmful biases, this approach helps prevent discriminatory outcomes and promotes fairness in AI systems, addressing a critical ethical security concern in modern NLP applications.