How are LLMs trained? And AI Landscape

Large Language Models are trained on massive amounts of text data using transformer-based neural networks comprising many layers and connections.

Here's a simple breakdown.

The network has "nodes" connected across layers. Each connection has a weight (importance) and bias (adjustment).

Together with embeddings (how words are represented as vectors), these form the model's parameters. LLMs have billions of these parameters.

The model looks at text, one part at a time, and predicts the next word or token in the sequence.

It adjusts its parameters (weights and biases) to improve predictions during each training iteration, using feedback to learn better patterns.

Once trained, LLMs can handle different tasks by adapting in the following ways:

• Zero-shot Learning The model performs tasks it wasn’t specifically trained for, based only on the instructions (prompts) given to it. Accuracy may vary.
• Few-shot Learning Adding a few examples improves its understanding and performance for specific tasks.
• Fine-tuning The model is further trained with more data tailored to a specific task, making it highly accurate for that application.

Applications of LLMs Beyond ChatGPT