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Related Questions
- Can you explain how pre-trained word embeddings affect the model's ability to generalize to out-of-domain data?
- How do word embeddings impact the model's transfer learning capabilities?
- What are the factors that influence the robustness of pre-trained word embeddings in a language model?
- In what ways do pre-trained word embeddings contribute to a model's ability to adapt to new tasks and datasets?
- What role do pre-trained word embeddings play in reducing overfitting and improving a model's generalization abilities?
- How do different pre-training techniques and architectures affect the performance and generalizability of word embeddings?
- Can you discuss the impact of pre-trained word embeddings on a model's ability to handle domain shift and semantic drift?
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