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Related Questions
- How does prompt engineering affect the LLM's ability to learn task-specific representations?
- What strategies can be used to create prompts that maximize domain and task generalizability?
- In what ways can prompt design impact the LLM's capacity for zero-shot, few-shot, and fine-tuning performance?
- How does the diversity and scope of training prompts impact the LLM's generalization capabilities?
- Can you provide examples of prompt engineering techniques for inducing inductive biases for improved generalization?
- What are some open research questions in prompt engineering that can help improve domain and task generalizability of LLMs?
- How can advances in prompt engineering contribute to more efficient and effective downstream applications of LLMs?
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