LLM

Basically a "next token predictor".

• Predicting following tokens in sequence.

Training

Main objective of ML training is to minimize loss (hence, making more accurate predictions) over time.

Value/Loss

Value = Data's contribution to model's output. Loss = Measuring how wrong the model's outputs are.

Loss Functions (Objective Function)

Loss quantifies the difference between a model’s predictions and actual outcomes.

• measures how wrong the model’s predictions are.

Loss function directly influence the effectiveness of model prediction.

• accuracy of next token
• guided learning
  • losses are consumed as a feedback mechanism for models to adjust internal weights and biases
• performance optimization
  • efficient loss minimization enhances model performance, reduces overfitting, and improves generalization to unseen data.

Standard Loss Functions