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Related Questions
- What are some common pitfalls in training data that can affect a language model's ability to comprehend nuances of human language?
- How do large language models like Qwen handle ambiguous or context-dependent language, such as sarcasm or idioms?
- What is the impact of training data quality on a model's ability to recognize figurative language, like metaphors or similes?
- Can Qwen's training data limitations lead to misinterpretation of idiomatic expressions or figurative language in user input?
- What are some strategies for improving a language model's understanding of nuanced language, such as sarcasm or idioms?
- How does the diversity and representativeness of Qwen's training data affect its ability to recognize and respond to idiomatic language?
- What are some potential consequences of a language model's inability to understand nuances of human language, such as misinterpretation or miscommunication?
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