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Related Questions
- Can contextual prompts be made more robust using techniques such as subword modeling or wordpiece tokenization to handle out-of-vocabulary words and phrases?
- How can the use of knowledge graphs or ontologies be integrated into contextual prompts to improve handling of out-of-vocabulary words and phrases?
- What role can pre-training on large datasets with diverse vocabulary play in making contextual prompts more robust to out-of-vocabulary words and phrases?
- Can the use of transfer learning or multi-task learning be applied to contextual prompts to improve their ability to handle out-of-vocabulary words and phrases?
- How can the development of contextual prompts be informed by techniques from natural language processing such as named entity recognition or part-of-speech tagging to improve handling of out-of-vocabulary words and phrases?
- Can the use of generative models or sequence-to-sequence models be used to generate more robust contextual prompts that can handle out-of-vocabulary words and phrases?
- What are some strategies for fine-tuning contextual prompts on specific domains or tasks to improve their ability to handle out-of-vocabulary words and phrases?
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