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Related Questions
- Can analogical reasoning be used to identify and exclude irrelevant features that contribute to overfitting in language models?
- How can analogical reasoning be applied to transfer knowledge from the training data to the test data, even when the distributions are significantly different?
- In what ways can analogical reasoning help language models generalize better to unseen data by leveraging commonalities between seemingly disparate concepts?
- Can analogical reasoning be used to detect and mitigate the effects of overfitting in language models by identifying and avoiding over-specialization?
- How can analogical reasoning be employed to improve the robustness of language models to distributional shifts in the test data?
- What are some potential challenges and limitations of using analogical reasoning to mitigate overfitting in language models, and how can they be addressed?
- Can analogical reasoning be used to develop more explainable and transparent language models that can provide insights into their decision-making processes and help identify potential overfitting issues?
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