What techniques are available to debias language models without affecting their performance

Question

Can you tell me What techniques are available to debias language models without affecting their performance?

Answer 1

To debias language models without affecting their performance, common techniques are as follows:

• Bias Mitigation via Fine-Tuning: Fine-tune the model on a debiased dataset or use adversarial training to reduce bias

Here is the code snippet you can refer to:

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• Counterfactual Data Augmentation: Train the model with counterfactual examples (e.g., swapping gendered terms).

• Bias Detection and Mitigation Layers: Implement layers that flag and adjust biased predictions post-hoc.

• Regularization: Use techniques like adversarial regularization to minimize bias during training.

Hence, these methods aim to preserve model accuracy while reducing biased outputs.