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Related Questions
- What are the challenges in scaling knowledge graph-based representations to accommodate domain-specific jargon?
- How can knowledge graphs be updated to capture nuanced meanings and context-dependent relationships?
- What are the trade-offs between using pre-trained language models and fine-tuning them on domain-specific knowledge graphs?
- Can you explain the limitations of using entity embeddings to represent domain-specific concepts and entities?
- How can we ensure that knowledge graphs accurately capture the subtleties of human language and context?
- What are the implications of using knowledge graphs for adapting LLMs to new domains, and how can we evaluate their effectiveness?
- Can you discuss the challenges of integrating knowledge graphs with other AI components, such as natural language processing and machine learning algorithms?
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