What is Training in AI

In this informative lecture, Mr. Gyula Rabai explains the critical differences between the processes of inference and training in large language models. Whether you're new to AI or an experienced practitioner, understanding these concepts is key to mastering how modern language models work and how they are used in applications like text generation, chatbots, and more.

What is Inference in Language Models?

Inference refers to the process where a pre-trained language model generates the most likely next word based on a given input. For example, if the input is "Hello," the model will output the most likely next word, such as "World," without altering its internal parameters. This process is straightforward and involves simply feeding the input into the trained model and getting an output based on the pre-existing numbers (parameters) within the model.

Key characteristics of inference:

  1. The model uses predefined numbers (parameters).
  2. No changes are made to the model during inference.
  3. The output is generated based on the existing data learned during training.

What is Training in Language Models?

Training, on the other hand, is the much more computationally expensive process of adjusting the model’s internal parameters so that it can generate accurate outputs. Training involves starting with random numbers and asking the model to predict the next word. When the model generates an incorrect word, such as "The," it adjusts its internal parameters to correct that prediction. This process involves updating every parameter in the model—sometimes billions of parameters—until the model can consistently generate accurate outputs.

Key characteristics of training:

  • Involves adjusting billions of parameters to improve accuracy.
  • It is computationally intensive and time-consuming.
  • Training aims to ensure the model predicts the correct word during inference.

Why Both Inference and Training Are Necessary

While inference uses a model's trained parameters to generate outputs, training is what allows a language model to learn and improve over time. In other words, inference is the application of a trained model, while training is the process of creating that model.

Inference: Applies the knowledge from training to generate predictions.

Training: Builds and refines the model so that it can generate accurate predictions.

Key Takeaways:

  • Inference is the process of generating the next word based on the pre-trained model without changing the model’s parameters.
  • Training adjusts the model’s parameters so that it can generate more accurate predictions over time.
  • Training is computationally expensive and takes a lot of time, while inference is fast and uses the model’s already learned data.

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