Aligning Language Models for Counterfactual Invariance
In brief
- Standard alignment methods amplify counterfactual biases between demographic groups.
- A new policy gradient technique reduces these gaps without degrading overall response quality.
- Models remain adaptable when sensitive information is relevant to the task.
This study reveals that standard language model alignment methods amplify counterfactual biases across demographic groups. The authors propose a policy gradient technique with a group-level reward function to mitigate these gaps while maintaining response quality.
What it changes for you
- Public SectorPublic administrations could reduce biases in AI tools used for public decisions by integrating this method.
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