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Related Questions
- How can data preprocessing steps like tokenization and stopword removal introduce bias in a language model?
- Can you provide examples of how word embeddings can perpetuate existing biases if the training data is biased?
- How does the choice of tokenizer and its parameters affect the representation of underrepresented groups in the data?
- In what ways can data augmentation techniques like paraphrasing or back-translation introduce bias in the preprocessed data?
- Can you explain how the removal of punctuation and special characters can impact the model's performance on certain demographics?
- How does the use of out-of-vocabulary (OOV) handling in preprocessed data affect the model's ability to represent rare or minority groups?
- What are some common pitfalls in data preprocessing that can lead to biased language models, and how can they be avoided?
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