Language Models: Foundations and Modern Architectures

Advanced

🧠 Language Models in NLP

Language models are at the heart of NLP, enabling systems to predict word sequences and understand context.

📈 From Statistics to Deep Learning

📊 n-gram Models
Early models using statistical probabilities based on frequency counts.

🤖 Modern Architectures

🔁 Recurrent Neural Networks (RNNs)
Designed for sequence data, capturing temporal dependencies.
🧠 Long Short-Term Memory (LSTM) & Gated Recurrent Units (GRU)
Enhanced RNNs that can learn longer-term dependencies.
⚡ Transformers
The current state-of-the-art, using self-attention mechanisms to capture relationships in data regardless of position.

🚀 Real-World Transformer Models

Transformers power advanced models like:

🧾 BERT — uses bidirectional training to understand context from both directions
✍️ GPT — excels in text completion, generation, and translation

These models enable tasks such as:

❓ Question answering
📜 Text summarization
😊 Sentiment analysis

🧩 Diagram: Evolution of Language Models

n-gram Models
     |
     v
    RNNs
     |
     v
  LSTM / GRU
     |
     v
 Transformers
     |
     v
[BERT]  [GPT] ---> Advanced NLP Tasks