Lesson 1 of 0
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Evolution of Language Models
- Rule-based: Depended on manually crafted rules for language analysis. Limited capabilities.
- Statistical: Applied probabilistic techniques to model word sequences. Introduced n-gram models.
- Neural Network-based: Architectures like RNNs and LSTMs improved context modeling.
- Transfer Learning: Leveraging knowledge from pre-trained models accelerated development.
- Self-Supervised: Models trained on unlabeled data via objectives like masked language modeling.
- Large-Scale Models: Scaled up models like GPT-3 showed impressive generative abilities.