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Related Questions
- What are the challenges researchers face when evaluating the performance of LLMs on out-of-vocabulary (OOV) words?
- Can you explain the concept of OOV words in the context of LLMs and how they impact model performance?
- What are some common evaluation metrics used to assess the performance of LLMs on OOV words?
- How do researchers handle OOV words during the testing phase of LLMs, and what are the implications?
- What are some techniques used to improve the performance of LLMs on OOV words?
- Can you provide examples of real-world applications where LLMs struggle with OOV words and how it affects the results?
- What is the relationship between OOV words and the perplexity metric in LLMs, and how do they affect each other?
- How do LLMs learn to handle OOV words during the training phase, and what are the key factors that influence this process?
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