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Related Questions
- Can prompt priming be used to elicit more transparent and interpretable counterfactuals from LLMs?
- How can prompt priming be designed to reveal the underlying reasoning and decision-making processes of LLMs?
- In what ways can prompt priming help identify potential biases and errors in LLMs' counterfactual reasoning?
- Can prompt priming be used to improve the faithfulness of LLMs' counterfactuals, i.e., make them more aligned with human intuition?
- How can prompt priming be used to increase the sensitivity of LLMs' counterfactuals, i.e., make them more responsive to subtle changes in input?
- Can prompt priming be used to improve the stability of LLMs' counterfactuals, i.e., reduce their sensitivity to noise and irrelevant factors?
- How can prompt priming be used to provide more explicit and transparent explanations for LLMs' counterfactual reasoning?
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