Introduction to Embeddings in Large Language Models
Training embeddings is the process of optimizing the weights of the neural network to learn embeddings that best capture the semantic and syntactic relationships in a given corpus. The basic idea is to minimize the difference between predicted and actual context vectors for each word in the corpus. The training set is usually a large corpus of text that has been preprocessed to remove stop words and other noise. Some popular algorithms used for training embeddings include Word2Vec, GloVe, and BERT.
One common approach for training embeddings is to use a neural network that has a single hidden layer. The input to the network is a one-hot encoded vector that represents a word, and the output is a vector that represents the context of the word. The weights of the hidden layer are the embeddings that we are trying to learn. During training, the weights are updated using backpropagation to minimize the loss function, which is typically the mean squared error between predicted and actual context vectors.
Another approach for training embeddings is to use a skip-gram model, which tries to predict the context words for a given input word. In this case, the input is a word and the output is a probability distribution over the context words. The weights of the input layer are the embeddings that we are trying to learn. During training, the weights are updated using stochastic gradient descent to maximize the log-likelihood of the observed context words.
Training embeddings can be a computationally expensive process, especially for large corpora. To speed up training, techniques like subsampling and negative sampling can be used to reduce the number of training examples and the number of computation required for updating the weights.
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