Overfitting is when your language model (LM)<\/strong> is too good at remembering the training data but bad at generalizing<\/strong> to new, unseen data \ud83d\ude2c.<\/p>\n\n\n\n Think of it like this:<\/p>\n\n\n\n \ud83d\udcd8 Training Data<\/strong> = Your school textbook \u2705 Training loss: Low<\/strong> If you’re tracking accuracy or perplexity<\/strong> (a measure for language models):<\/p>\n\n\n\n That\u2019s a big red flag \ud83d\udea9<\/p>\n\n\n\n That’s a classic overfitting curve \ud83e\udde0\ud83d\udd25<\/p>\n\n\n\n \u2705 Use more data<\/strong> <\/p>\n","protected":false},"excerpt":{"rendered":"
\ud83e\udde0 Overfitted Model<\/strong> = A student who memorized every page but struggles with test questions that are worded differently.<\/p>\n\n\n\n
\n\n\n\n\ud83d\udea8 Signs Your LM is Overfitted<\/h3>\n\n\n\n
1. \ud83d\udcc9 Low Training Loss, \ud83d\ude80 High Validation\/Test Loss<\/h4>\n\n\n\n
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\u274c Validation\/Test loss: High<\/strong><\/p>\n\n\n\n
\n\n\n\n2. \ud83e\ude9e Huge Gap Between Accuracy or Perplexity<\/h4>\n\n\n\n
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\n\n\n\n3. \ud83d\udcca Your Loss Curve Looks Like This:<\/h4>\n\n\n\n
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\n\n\n\n\ud83d\udee0\ufe0f How to Fix It?<\/h3>\n\n\n\n
\u2705 Regularization techniques<\/strong> like dropout \ud83d\udd73\ufe0f
\u2705 Early stopping<\/strong> \u23f9\ufe0f
\u2705 Smaller model<\/strong> if data is limited
\u2705 Data Augmentation<\/strong> (e.g., paraphrasing for text)<\/p>\n\n\n\n
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