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Related Questions
- Can you explain how prompt engineering techniques help large language models to better understand contextual subtleties?
- How do techniques such as entity-based prompts, syntax-based prompts, and conversational flow improve contextual understanding in large language models?
- What are the key differences between open-ended and constrained prompts, and how do they impact large language models' contextual understanding?
- How does the use of auxiliary data, such as additional input or context, influence large language models' ability to understand context?
- Can you discuss the trade-offs between using natural language and formalized input representations in large language models' contextual understanding?
- How do techniques such as self-supervised learning, active learning, and data augmentation contribute to the improvement of large language models' contextual understanding?
- What are some potential methods for evaluating the contextual understanding of large language models, and how can these metrics be used to optimize performance?
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