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Related Questions
- What are the common evaluation metrics used for question-answering models, and how do they impact the training data and model architecture?
- How do different evaluation metrics, such as accuracy, precision, and F1-score, influence the choice of model architecture and hyperparameters in question-answering tasks?
- Can you explain the difference between extrinsic and intrinsic evaluation metrics for question-answering models and how they affect the training process and model design?
- How do evaluation metrics impact the choice of pre-trained language models and fine-tuning strategies for question-answering tasks?
- What are the limitations of commonly used evaluation metrics in question-answering models, and how do they affect the model's ability to generalize to real-world scenarios?
- How do evaluation metrics influence the development of multimodal question-answering models that incorporate multiple sources of information, such as text and images?
- Can you explain the role of evaluation metrics in identifying and addressing biases in question-answering models and improving their fairness and transparency?
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