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Related Questions
- How do contextualized embeddings, such as those from BERT, address the challenge of data bias in language models?
- What are some common sources of data bias in language models, and how do contextualized embeddings help to mitigate these biases?
- Can you explain how contextualized embeddings learn to capture nuanced contextual information that can help reduce data bias in language models?
- How do contextualized embeddings compare to traditional word embeddings in terms of their ability to address data bias?
- What are some strategies for further mitigating data bias in language models, and how do contextualized embeddings fit into these strategies?
- Can you discuss the role of pre-training and fine-tuning in contextualized embeddings, and how these processes can impact data bias?
- How do contextualized embeddings handle out-of-vocabulary words and their potential impact on data bias in language models?
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