SoftMax is a mathematical function that converts a vector of real numbers into a probability distribution. It is frequently used as the output layer activation function in neural networks for classification tasks.

The SoftMax function takes the exponentials of each element of the input vector and then normalizes them by dividing by the sum of exponentials. It results in probability distribution over multiple classes, making it useful for determined the likelihood of each class being the correct one.

SoftMax is a function used to transform a vector of arbitrary real-valued scores into a probability distribution classes..

The formula applied to the vector.

(\mathbf(z)=z_1, z_2, …, z_n):

SoftMax ensures that the output values are non-negative and sum up to #1. (Making it a probability distribution.) It exponentiates the input scores (amplifies differences between scores). It is differentiable except at the point where two classes have the same score.

It transforms raw scores into probabilities. It is useful in various ML algorithms. The raw scores at the output layer refer to activations produced by neurons in the output layer before applying any activation function. These raw scores are often represented as a vector of real numbers. (Each element corresponds to a different class in the classification task).

In an image classification network, there are 10 classes. The output layer may have 10 neurons. Each produces a raw score representing the network’s confidence or likelihood that the input image belongs to a particular task. These raw scores are subjected to SoftMax function to convert them into probabilities.

SoftMax is used during training the LLMs and other neural networks. It is a common activation function used in the output layer of neural network for classification tasks.

In LLMs, SoftMax is used in the output layer to compute probability distribution over the vocabulary of words. During training, the model learns to predict the next word in a sequence bases on the input context. SoftMax helps normalize model’s output into a probability distribution.

LLMs generate text, but here SoftMax is used in the output layer to compute probability distribution over the vocabulary of words. During training, the model learns to predict the next word in a sequence based on the input context. SoftMax helps normalize model’s output into a probability distribution.

LLMs generate text, but here SoftMax is not used. A sampling strategy is used to select the next word based on the probabilities predicted by the model. There are techniques like greedy sampling, beam search or nucleus sampling. During training, SoftMax is used to teach the model to produce these probabilities, but during generation the model uses these learned probabilities to guide the sampling process.