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Related Questions
- How do word embeddings affect the performance of large language models on out-of-vocabulary words?
- Can you explain the concept of word embeddings and their role in improving language model performance on rare words?
- What is the impact of word embeddings on the ability of a language model to generalize to novel words?
- How do different types of word embeddings (e.g. word2vec, GloVe) affect a language model's performance on rare words?
- Can you discuss the trade-offs between using pre-trained word embeddings and training word embeddings from scratch for a language model?
- What are some strategies for improving a language model's performance on rare words, such as using subword models or character-level embeddings?
- How do word embeddings impact the interpretability of a language model's predictions on novel or rare words?
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