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Related Questions
- How can LLMs be trained on a diverse range of texts, including literature and poetry, to improve their understanding of figurative language?
- What role can cognitive architectures, such as conceptual graphs, play in enabling LLMs to better comprehend idiomatic expressions?
- Can the incorporation of world knowledge and common sense, via knowledge graphs or external knowledge sources, enhance LLMs' ability to understand figurative language?
- How can LLMs be designed to recognize and handle ambiguity, nuance, and context-dependent meaning in idiomatic expressions?
- What are some potential applications of LLMs in natural language processing tasks, such as sentiment analysis, text summarization, and machine translation, that require a deep understanding of figurative language?
- Can the use of multi-task learning and transfer learning help LLMs generalize to new domains and improve their ability to comprehend idiomatic expressions?
- What are some potential limitations and challenges of training LLMs on large datasets, and how can they be addressed to improve the models' understanding of figurative language?
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