Editing Embedding (section)

== Generation of Embeddings ==
There are different ways to create embeddings, depending on the task and the data. Some of the most common methods are:

=== Prediction-based embeddings ===
These embeddings are based on the prediction of words or phrases in a context, such as the neural network models Word2Vec or GloVe. These embeddings capture the syntactic and semantic information of words in a low-dimensional and dense vector space, by learning from large amounts of text data. They also allow for operations like vector addition and subtraction, which can reveal interesting linguistic properties. For example, king - man + woman = queen.

=== Contextualized embeddings ===
These embeddings are based on the representation of words or phrases in a specific context, such as the [[transformer]] models. These embeddings capture the dynamic and complex meaning of words in different situations, by using deep neural networks that can encode both the left and the right context of a word. They also allow for fine-tuning and transfer learning, which can improve the performance of downstream NLP tasks.

=== Frequency-based embeddings ===
These embeddings are based on the frequency of words or phrases in the [[training material]], such as the term frequency-inverse document frequency (TF-IDF) or the co-occurrence matrix. These embeddings capture the importance and the distribution of words in a document or a collection of documents, but they do not capture the syntactic or semantic information very well. They also tend to have high dimensionality and sparsity, which can affect the performance and the memory of the algorithms. These were used in earlier [[NLP]] tasks, but are no longer popular for LLMs.

Embeddings are very useful for NLP applications, such as text classification, sentiment analysis, machine translation, question answering, and more. They enable NLP models to understand natural language better and produce more meaningful results.