What are the biases in NLP pretrained models?

What are the biases present in NLP pretrained models?

NLP pretrained models can exhibit biases related to gender, race, and cultural stereotypes due to the data they are trained on.

How do these biases affect the model's performance?

These biases can lead to unfair or inaccurate predictions, reinforcing stereotypes and potentially causing harm in applications.

What steps can be taken to mitigate these biases?

Mitigation strategies include using diverse training datasets, implementing bias detection tools, and fine-tuning models with fairness in mind.

Are there any examples of biased outputs from these models?

Yes, there have been instances where models generated sexist or racially biased content, highlighting the need for careful oversight.