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Related Questions
- Can a large training dataset with diverse linguistic examples improve an LLM's ability to recognize homographs?
- How does the ratio of in-domain to out-of-domain training data impact an LLM's performance on homograph recognition?
- Do LLMs require a minimum number of training examples to achieve optimal performance on homograph recognition tasks?
- Can the use of pre-training on large-scale corpora with diverse genres and styles improve an LLM's ability to recognize homographs?
- How does the quality of the training data, in terms of accuracy and relevance, affect an LLM's ability to recognize homographs?
- Can the inclusion of semantically rich training data, such as annotated text or knowledge graphs, enhance an LLM's ability to recognize homographs?
- Does the size of the training dataset impact the LLM's ability to generalize to unseen homograph instances?
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