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Related Questions
- What statistical methods can be used to assess model performance onOOD intents?
- How can benchmark datasets for OOD OOD intent-based prompts be designed?
- What are some key metrics to evaluate the robustness of large language models to out-of-distribution intent-based prompts?
- Can you explain the difference between out-of-distribution and in-distribution samples in the context of intents?
- What are some state-of-the-art techniques to detect or mitigate OOD intent-based prompts?
- In what ways can the understanding of semantic meaning and intentions be applied to improve models' OOD robustness?
- What are some machine learning methodologies that can deal with noisy or ambiguous in/out-distribution intent prompt examples?
- How can self-supervised learning be explored to help models generalize for OOD intent-based task performance?
- What are critical aspects in prompt engineering while designing novel tasks to cover diverse ranges of intents during testing scenarios?
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