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Related Questions
- How can prompt engineering be used to reduce the uncertainty of large language model predictions?
- What are some strategies for designing prompts that elicit more transparent and explainable model behavior?
- Can you explain the concept of 'prompt swapping' in the context of large language models and its potential benefits for interpretability?
- How do large language models handle out-of-domain or ambiguous prompts, and what techniques can be used to improve their performance in such cases?
- What role does 'prompt chaining' play in improving the interpretability of large language model outputs, and how can it be implemented effectively?
- What are some best practices for using active learning techniques in conjunction with prompt engineering to improve the interpretability of large language models?
- Can you discuss the relationship between prompt engineering and model regularisation, and how can they be combined to improve model transparency and trustworthiness?
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