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Related Questions
- What are some strategies for designing prompt primes to enhance the robustness of LLMs in the face of out-of-distribution inputs?
- How can prompt engineering techniques, such as masking and filtering, be leveraged to improve the ability of LLMs to generalize to unseen data?
- What role do adversarial examples play in assessing the robustness of LLMs, and how can prompt priming help mitigate their impact?
- Can you describe a scenario where prompt priming is used to increase the robustness of a conversational AI system against malicious or unexpected user input?
- What types of prompt primes are effective in reducing the likelihood of mode collapse or overfitting in LLMs?
- How do the choice of prompt primitives, such as keywords, entities, or relationships, affect the robustness of LLMs to out-of-distribution inputs?
- What are the limitations of prompt priming as a method for improving LLM robustness, and what alternative approaches may be more effective?
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