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Related Questions
- What are the primary challenges in applying self-supervised learning to domain-specific terminology recognition in large language models (LLMs)?
- How do the limitations of self-supervised learning impact the accuracy of domain-specific terminology recognition in LLMs?
- Can you discuss the difficulties of adapting self-supervised learning approaches to handle domain-specific terminology with varying levels of ambiguity and specificity?
- What are some strategies for addressing the challenges of self-supervised learning in domain-specific terminology recognition, such as data quality and scarcity?
- How do the limitations of self-supervised learning affect the generalizability of LLMs to new domains and unseen terminology?
- What are some potential solutions to overcome the limitations of self-supervised learning in domain-specific terminology recognition, such as multi-task learning or knowledge graph-based approaches?
- Can you discuss the trade-offs between self-supervised learning and supervised learning in domain-specific terminology recognition, and how they impact model performance?
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