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Related Questions
- Can you explain how overfitting and underfitting occur in machine learning, and how back-translation and paraphrasing can potentially contribute to these issues?
- How can the complexity of a training dataset impact the model's ability to generalize and why might back-translation lead to overfitting if not done properly?
- What are the key factors that determine when back-translation is effective and when it may cause the model to underfit or overfit, such as dataset size or architectural choices?
- Can you discuss the risks of relying too heavily on translation-based augmentations and possible mitigations to avoid this, including strategies for balance and evaluation?
- In what situations may paraphrasing be beneficial but also riskier, with a greater chance of producing underfitting models unless careful considerations are made towards model training and hyperparameters?
- If back-translation leads to overfitting due to its strong bias towards certain subsets of data, are there methods or techniques to quantify this effect and better tune hyperparameters to achieve proper generalization?
- Are there common problems in specific domains where a high-quality translation can contribute to poor model generalizability and, conversely, a well-implemented paraphrasing strategy that inadvertently exacerbates underfitting?
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