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3 months ago Understanding Large Language Models (LLMs) requires familiarity with several fundamental concepts in machine learning and natural language processing. Here are the key concepts:
1. Neural Networks
- Basics: Neural networks are the foundation of LLMs. They consist of layers of nodes (neurons) that process input data to produce output.
- Types: Common types include feedforward neural networks, recurrent neural networks (RNNs), and transformer networks.
2. Transformers
- Architecture: Transformers are a type of neural network architecture that has revolutionized NLP. They use mechanisms called attention to process sequences of data more efficiently than RNNs.
- Key Models: Examples include BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer).
3. Attention Mechanism
- Self-Attention: This allows the model to weigh the importance of different words in a sentence when encoding a particular word.
- Multi-Head Attention: This extends self-attention by running multiple attention mechanisms in parallel.
4. Tokenization
- Tokens: Breaking down text into smaller units (tokens) that the model can process.
- Methods: Includes word-level, subword-level, and character-level tokenization.
5. Training and Fine-Tuning
- Pre-training: Training a model on a large corpus of text data to learn language patterns.
- Fine-Tuning: Adjusting the pre-trained model on a smaller, task-specific dataset to improve performance on a specific task.
6. Parameters
- Weights and Biases: The adjustable elements of the model that get updated during training.
- Scale: LLMs typically have millions or billions of parameters.
7. Learning Rate
- Definition: A hyperparameter that controls how much the model's parameters are adjusted during training.
- Tuning: Selecting an appropriate learning rate is crucial for efficient training.
8. Loss Function
- Purpose: Measures the difference between the model's predictions and the actual outcomes.
- Optimization: The goal of training is to minimize the loss function.
9. Gradient Descent
- Algorithm: Used to minimize the loss function by iteratively adjusting the model's parameters.
- Variants: Includes stochastic gradient descent, mini-batch gradient descent, etc.
10. Overfitting and Regularization
- Overfitting: When the model performs well on training data but poorly on unseen data.
- Regularization Techniques: Methods like dropout, weight decay, and early stopping to prevent overfitting.
11. Evaluation Metrics
- Accuracy: The proportion of correct predictions.
- Other Metrics: Precision, recall, F1 score, BLEU score (for language tasks), etc.
12. Inference
- Definition: The process of making predictions with a trained model.
- Efficiency: Optimizing inference speed and resource usage is crucial for practical applications.
13. Ethics and Bias
- Fairness: Ensuring the model does not perpetuate or amplify biases present in the training data.
- Ethical Considerations: Addressing issues like privacy, misinformation, and the potential misuse of AI.
14. Embedding
Definition: Embeddings are continuous vector representations of tokens that capture their semantic meanings. Techniques:
- Word Embeddings: Fixed-size vectors for words, e.g., Word2Vec, GloVe.
- Contextualized Embeddings: Vectors that vary based on context, e.g., those generated by transformers. Purpose: Embed### 1. Tokenization Definition: Tokenization is the process of converting raw text into smaller, manageable units called tokens. Types:
- Word-level: Splits text into individual words.
- Subword-level: Splits text into meaningful subword units, like using Byte Pair Encoding (BPE) or WordPiece. This helps handle rare words and morphological variations.
- Character-level: Splits text into individual characters, useful for languages with complex morphology. Purpose: Tokenization is the first step in text processing, making the text data ready for model input.
Embedings convert tokens into numerical form that models can process while preserving semantic relationships.
14. Pretraining
Definition: Pretraining involves training a model on a large corpus of text to learn general language patterns and representations. Approach:
- Unsupervised Learning: The model learns from the raw text without task-specific labels. Common objectives include predicting the next word (causal language modeling) or filling in masked words (masked language modeling). Purpose: Pretraining provides the model with a strong foundation in language understanding, which can be adapted to specific tasks.
15. Transfer Learning
Definition: Transfer learning involves taking a pretrained model and fine-tuning it on a specific task or domain. Steps:
- Fine-Tuning: The pretrained model is further trained on a smaller, task-specific dataset. This adjusts the model's weights to optimize performance for the new task. Purpose: Transfer learning allows leveraging the general language knowledge acquired during pretraining to excel in specialized tasks with less data and computational resources.
LLMs can help with
They are called Large Language Models, because they are trained using Billions of paramters
Core Element