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Related Questions
- What are the key factors to consider when designing scientific prompts to assess both technical accuracy and natural language understanding?
- How can evaluators ensure that scientific prompts are clear and concise while still requiring sufficient technical complexity?
- What strategies can be employed to measure the performance of large language models on scientific tasks that require both technical knowledge and linguistic nuance?
- How do evaluators balance the trade-off between rewarding correct technical answers and penalizing overly complex or unclear responses in natural language understanding tasks?
- What role do you think prompt engineering plays in bridging the gap between technical accuracy and natural language understanding in scientific prompts?
- Can you provide examples of scientific prompts that effectively assess both technical accuracy and natural language understanding, and what made them effective?
- What are some common pitfalls that evaluators should avoid when designing scientific prompts that require both technical accuracy and natural language understanding?
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