Welcome to the FAQ page for Infermatic.ai! Here, you can find answers to your questions about large language models and the AI industry. Whether you’re curious about how to use our tools or want to learn more about AI, this page is a great place to start.
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Related Questions
- What is the difference between overfitting and underfitting in the context of machine learning, and how can they impact the performance of large language models?
- How do the risk of overfitting and underfitting change with the size of the language model, and what measures can be taken to prevent them?
- In what ways can regularization techniques, such as dropout or L1/L2 regularization, help to mitigate the effects of overfitting in large language models?
- What is an example of an underfitted language model, and how can additional data, feature engineering, or changes to the model architecture be used to improve its performance?
- Can the use of transfer learning from pre-trained models help reduce the risk of overfitting and underfitting in large language models?
- How can active learning, which involves iteratively selecting and labeling samples from a pool of available data, help to combat overfitting and improve the performance of large language models?
- In what ways can ensembling multiple language models with different architectures or hyperparameters improve the overall performance and generalizability of the language model?
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