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Related Questions
- What are some common biases in large language models like Llama that can impact their performance and accuracy?
- How do Llama's architecture and training data influence its ability to capture nuances in human language and context?
- Can Llama's limitations in handling ambiguity and uncertainty in natural language be addressed through advancements in machine learning algorithms or data augmentation techniques?
- In what ways do Llama's architecture and training objectives affect its ability to generate coherent and contextually relevant text?
- How do Llama's limitations in understanding figurative language, idioms, and sarcasm impact its overall performance in natural language processing tasks?
- Can Llama's architecture be modified to better handle multi-turn dialogue and conversational understanding, and if so, how?
- What are some potential ways to address Llama's limitations in handling out-of-vocabulary words and domain-specific terminology in natural language processing tasks?
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