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Related Questions
- How does entity-based attention influence the overall performance of large language models on out-of-vocabulary words?
- Can entity-based attention create over-reliance on contextual information, potentially hurting model generalizability?
- How does entity-based attention compare to other methods, such as token-based attention or pre-trained embeddings, in handling OOV words?
- Are there specific domains or tasks where entity-based attention excels or struggles in handling OOV words?
- How does entity-based attention affect the interpretation of word meanings in large language models, particularly when encountering unknown words?
- Can entity-based attention be used in conjunction with other techniques, such as subword modeling or denoising autoencoders, to improve OOV word handling?
- What are the potential pitfalls of using entity-based attention in high-resource languages versus low-resource languages in terms of OOV word handling?
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