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Related Questions
- What is entity-based attention and how does it help large language models with out-of-vocabulary words?
- How does entity-based attention compare to other methods, such as word embeddings or subword models, in handling out-of-vocabulary words?
- Can you explain the concept of entity-based attention and its role in improving the performance of large language models on out-of-vocabulary word tasks?
- What are the key benefits of using entity-based attention in large language models for out-of-vocabulary word tasks?
- How does entity-based attention handle polysemy and homographs in out-of-vocabulary words, and what are the implications for large language models?
- What are the computational requirements and memory usage of entity-based attention compared to other methods for handling out-of-vocabulary words?
- Can you provide examples of applications where entity-based attention has been used to improve the performance of large language models on out-of-vocabulary word tasks?
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