Editing Embedding (section)

=== 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.