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Related Questions
- What are the primary mechanisms by which large language models like Llama and Qwen learn to generalize to new words and concepts during fine-tuning?
- Can you explain the role of the training data and its quality in enabling generalization in Llama and Qwen models?
- How do the architecture and design of Llama and Qwen models influence their ability to generalize to new words and concepts?
- What are the key hyperparameters that control the generalization ability of Llama and Qwen models during fine-tuning?
- How do the learning objectives and optimization algorithms used in Llama and Qwen fine-tuning impact their generalization capabilities?
- Can you discuss the importance of regularization techniques in preventing overfitting and promoting generalization in Llama and Qwen models during fine-tuning?
- What are the trade-offs between the level of fine-tuning and the model's ability to generalize to new words and concepts?
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