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Related Questions
- Can large language models be fine-tuned to recognize homophone nuances in idiomatic expressions through targeted training data?
- How effective is the integration of linguistically diverse datasets in enhancing an LLM's comprehension of homophones in context?
- What are the most common pitfalls in training data that hinder an LLM's ability to accurately grasp homophones in idiomatic expressions?
- Can transfer learning from multilingual models improve an LLM's understanding of homophones in idiomatic expressions across languages?
- What are the key challenges in evaluating the success of LLMs in identifying homophones in idiomatic expressions, and how can these be addressed?
- Can the inclusion of more nuanced linguistic representations, such as semantic embeddings, improve an LLM's comprehension of homophones in context?
- What role can crowdsourcing play in providing diverse and relevant training data to improve an LLM's understanding of homophones in idiomatic expressions?
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