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Related Questions
- What is the impact of varying training data sizes on the contextual understanding of LLMs for entity recognition and relation extraction?
- How does the type of training data (e.g., text, speech, or multimodal) influence the contextual understanding of LLMs for entity recognition and relation extraction?
- Can you explain how the distribution of training data (e.g., domain-specific vs. general knowledge) affects the contextual understanding of LLMs for entity recognition and relation extraction?
- How does the quality of training data (e.g., noisy, biased, or high-quality) impact the contextual understanding of LLMs for entity recognition and relation extraction?
- What is the relationship between the amount of training data and the ability of LLMs to generalize to unseen contexts for entity recognition and relation extraction?
- Can you discuss how the type of entity recognition and relation extraction tasks (e.g., named entity recognition, relation classification, or coreference resolution) influence the contextual understanding of LLMs?
- How does the use of transfer learning and fine-tuning on specific tasks affect the contextual understanding of LLMs for entity recognition and relation extraction?
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