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Related Questions
- What are the key factors that influence the ability of a large language model like Qwen to generalize to new language tasks and domains?
- How do the trade-offs between different training objectives, such as maximizing accuracy, fluency, and adaptability, impact Qwen's performance on out-of-distribution tasks?
- What are some strategies for balancing the competing demands of training a language model for a specific task while also enabling it to generalize to novel situations?
- Can you explain the concept of inductive bias in the context of language models, and how it affects Qwen's ability to generalize to new language tasks and domains?
- How does the size and complexity of the training dataset impact Qwen's ability to generalize to new language tasks and domains?
- What are some techniques for fine-tuning a pre-trained language model like Qwen for a specific task while preserving its ability to generalize to other tasks?
- Can you discuss the role of evaluation metrics in assessing a language model's ability to generalize to new language tasks and domains, and how they might be used to inform the development of more effective training objectives?
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