What are the limitations of contextual embeddings?

What are the limitations of contextual embeddings?

Contextual embeddings, while powerful, have limitations such as dependency on large datasets, computational cost, and potential biases.

Can you explain how biases affect contextual embeddings?

Biases in training data can lead to biased embeddings, which may perpetuate stereotypes or inaccuracies in language understanding.

Are there specific examples of these biases?

Yes, for instance, embeddings might associate certain professions with specific genders, reflecting societal biases present in the data.

How can we mitigate these biases in contextual embeddings?

Mitigation strategies include using diverse training datasets, applying debiasing techniques, and continuously evaluating model outputs.