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Related Questions
- What are some strategies for large language models to recognize subtle context clues and figurative language in user inputs?
- How can large language models be trained to understand implicit intentions and inferences in user prompts?
- What are some techniques for mitigating ambiguity and uncertainty in natural language processing tasks, especially with intent-based prompts?
- Can large language models be designed to account for idiomatic expressions and colloquialisms, which can be ambiguous and context-dependent?
- How do large language models handle sarcasm, irony, and other forms of figurative language that can be difficult to detect?
- What role can multi-task learning and transfer learning play in helping large language models generalize to nuanced language and intent-based prompts?
- How can large language models be fine-tuned to better handle domain-specific language and specialized terminology, which can be critical for effective communication in specific contexts?
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