Negative sampling is a training technique used to improve the efficiency of models like Word2Vec by focusing on meaningful comparisons during optimization. Instead of calculating gradients for all possible outputs, negative sampling trains the model on a small subset of "negative" examples that are not true associations with the input.
For instance, when training word embeddings, the model learns to associate "king" with "queen" while distinguishing it from unrelated words like "table" or "dog." Negative samples are selected randomly or based on their frequency, ensuring the model learns meaningful distinctions without unnecessary computations.
Negative sampling simplifies the computational requirements of training large embedding models while maintaining high-quality representations. It is particularly effective for tasks like language modeling and recommendation systems, where the dataset size makes full optimization impractical.