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Related Questions
- Can a language model generate inaccurate or nonsensical outputs when encountering words or entities it has not been trained on?
- What are the potential implications for downstream applications such as conversational AI, sentiment analysis, or machine translation when a language model struggles to handle out-of-vocabulary entities?
- Can ignoring out-of-vocabulary entities lead to a narrowing of the model's learning capacity and a decreased ability to generalize to new data?
- How does the lack of handling for out-of-vocabulary entities impact the model's understanding of context and nuances of language?
- What are the potential consequences of a language model failing to recognize and handle proper nouns, such as names, locations, or organizations, when they appear in unseen contexts?
- Can the inability to handle out-of-vocabulary entities in a language model compromise the reliability and trustworthiness of the system as a whole?
- In what ways might the design of a language model handle out-of-vocabulary entities, such as using strategies like subword tokenization or learning a more explicit representation of unseen words?
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