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Related Questions
- Can self-supervised learning techniques be applied to improve the coherence of metaphorical language in LLMs by generating contrasting pairs of metaphors to identify subtle differences in meaning?
- How can the use of noise injection in self-supervised learning techniques help LLMs develop more nuanced understanding of metaphorical language, leading to improved coherence and consistency?
- In what ways can the application of self-supervised learning to metaphorical language in LLMs be used to identify and learn from ambiguities and uncertainties in metaphorical expressions?
- Can the use of self-supervised learning enable LLMs to discover and learn common metaphorical patterns and motifs across diverse domains and topics?
- How can the integration of metaphorical reasoning with self-supervised learning techniques in LLMs lead to the development of more coherent and consistent models of metaphorical language?
- What potential benefits and drawbacks arise when applying self-supervised learning techniques to metaphorical language in LLMs, and how might these impacts be mitigated?
- In what areas of metaphorical language – such as comparisons, tropes, and idiomatic expressions – are self-supervised learning techniques most applicable and effective?
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