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Related Questions
- What are some common techniques for detecting out-of-distribution prompts in LLMs?
- How can LLMs be fine-tuned to recognize and respond to novel or unseen input?
- What is the role of data augmentation in improving LLMs' ability to handle out-of-distribution prompts?
- Can you explain the concept of 'OOD' (out-of-distribution) detection in LLMs and its importance?
- How do LLMs handle ambiguity or uncertainty in out-of-distribution prompts?
- What are some potential risks or limitations of relying solely on LLMs to handle out-of-distribution prompts?
- Can you discuss the relationship between LLMs' generalizability and their ability to recognize out-of-distribution prompts?
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