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Related Questions
- What are the key differences between few-shot learning and traditional supervised learning in the context of dialogue model fine-tuning?
- Can you provide an example of a scenario where few-shot learning is more suitable than traditional supervised learning for dialogue model fine-tuning?
- How does few-shot learning adapt to the nuances of human dialogue, and what makes it more effective in certain situations?
- In what types of dialogue scenarios is few-shot learning particularly well-suited, and why?
- How does the limited amount of data required for few-shot learning impact the performance of a dialogue model compared to traditional supervised learning?
- Can you give an example of a real-world application where few-shot learning was used to fine-tune a dialogue model, and what were the results?
- What are the potential drawbacks of using few-shot learning for dialogue model fine-tuning, and how can they be mitigated?
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