framing effects

DeFrame: New Technique Debiases LLMs Against Subtle Framing Effects

Researchers at KAIST have identified framing disparity as an underexplored source of hidden bias in large language models (LLMs). Their proposed DeFrame method encourages consistent responses across semantically equivalent prompts, reducing overall bias and improving robustness against framing effects. The work has implications for enterprise AI deployments where fairness across demographics is critical.