In a case where a Natural Language Processing model starts producing offensive or biased outputs, what steps would you consider taking to rectify the issue without compromising the performance of the model?

  • Fine-tuning the model.
  • Implementing post-processing filters.
  • Re-training with more diverse data.
  • Reducing model complexity.
When a model exhibits offensive or biased outputs, re-training with more diverse and representative data is a crucial step to reduce bias and improve performance without compromising model complexity.
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