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Related Questions
- What are the common challenges faced by large language models when encountering out-of-domain prompts?
- How do large language models determine the relevance of a prompt, and what techniques can be used to improve this process?
- What is the role of pre-training and fine-tuning in helping large language models handle ambiguous or out-of-domain prompts?
- Can you explain the concept of prompt engineering and how it can be used to improve the performance of large language models on out-of-domain prompts?
- What are some common techniques used to handle out-of-domain or ambiguous prompts in large language models, such as filtering, ranking, or generating multiple responses?
- How do large language models use context and semantics to disambiguate ambiguous prompts, and what techniques can be used to improve this process?
- What are the potential risks and limitations of relying on large language models to handle out-of-domain or ambiguous prompts, and how can these be mitigated?
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