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Related Questions
- Can you describe the concept of 'tech accuracy' versus 'natural language understanding' in the context of scientific prompts and how they affect each other?
- What are some techniques for calibrating a large language model to improve its performance on scientific tasks while minimizing the trade-off between accuracy and understanding?
- How does the 'prompt engineering' process help to mitigate this trade-off, and what are some best practices for designing effective scientific prompts?
- Can you discuss the role of active learning in mitigating the trade-off between technical accuracy and natural language understanding in scientific prompts?
- How do you recommend balancing the need for accuracy in technical domains with the need for natural language understanding in scientific prompts?
- What are some strategies for using pre-training and fine-tuning a large language model to adapt to different scientific domains while maintaining good performance on accuracy and understanding?
- Can you explain the concept of 'evaluation metrics' and how they can be used to assess the trade-off between technical accuracy and natural language understanding in scientific prompts?
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