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Related Questions
- What are the key differences in performance metrics when evaluating LLMs on out-of-vocabulary words versus in-vocabulary words in out-of-context input?
- How do LLMs handle unknown words in out-of-context input, and what metrics are used to evaluate their performance in such scenarios?
- What are some common challenges in evaluating LLMs on out-of-vocabulary words in out-of-context input, and how can they be addressed?
- Can you explain the impact of out-of-vocabulary words on the performance metrics of LLMs in out-of-context input, such as perplexity and accuracy?
- How do different LLM architectures, such as transformer-based models, handle out-of-vocabulary words in out-of-context input, and what are their strengths and weaknesses?
- What role does pre-training data play in an LLM's ability to handle out-of-vocabulary words in out-of-context input, and how can it be optimized?
- Are there any specific metrics or evaluation protocols that are commonly used to assess the performance of LLMs on out-of-vocabulary words in out-of-context input?
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