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Related Questions
- What are some common pitfalls to avoid when fine-tuning a pre-trained LLM for a specific task to mitigate hallucinations?
- How can data augmentation and oversampling of minority classes help reduce hallucinations in LLMs?
- What is the role of task-specific objectives and constraints in minimizing hallucinations during fine-tuning?
- Can you explain the concept of 'adversarial training' and how it can be used to enhance the robustness of LLMs against hallucinations?
- In what ways can the use of multi-task learning and knowledge distillation help mitigate hallucinations in LLMs?
- How does the choice of hyperparameters, such as learning rate and batch size, impact the occurrence of hallucinations during fine-tuning?
- What are some strategies for incorporating expert feedback and human evaluation to detect and correct hallucinations in LLMs?
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