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Related Questions
- What are the key strategies used in prompt engineering to enhance few-shot learning in large language models?
- Can you explain how the design of a prompt can impact the performance of a large language model in a few-shot learning scenario?
- How do large language models handle out-of-vocabulary words or concepts in few-shot learning settings, and what techniques can be employed to address this challenge?
- What role does prompt engineering play in reducing the reliance on large amounts of training data in few-shot learning?
- Can you discuss the relationship between prompt engineering and the concept of 'hallucination' in few-shot learning, and how to mitigate it?
- How can prompt engineering be used to improve the robustness and generalizability of large language models in few-shot learning settings?
- What are some best practices for designing effective prompts for few-shot learning in large language models, and how can they be evaluated?
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