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Related Questions
- What is transfer learning in the context of large language models (LLMs), and how does it enable models to adapt to new tasks and domains?
- How does transfer learning help LLMs generalize to viewpoint and lighting variations in visual tasks, and what are some common techniques used to achieve this?
- Can you provide an example of how transfer learning is applied to a specific LLM architecture, such as a convolutional neural network (CNN) or a transformer-based model?
- How does the concept of 'domain adaptation' relate to transfer learning in LLMs, and what are some key challenges in adapting models to new domains?
- In what ways do viewpoint and lighting variations impact the performance of LLMs in visual tasks, and how can transfer learning help mitigate these effects?
- What are some common evaluation metrics used to assess the performance of LLMs on tasks involving viewpoint and lighting variations?
- Can you discuss the role of 'self-supervised learning' in transfer learning for LLMs, and how it enables models to learn from unlabeled data?
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