Description: Token embeddings are numerical representations that capture the meaning and semantic relationships of words or tokens in a vector space. These representations enable language models to understand and process text more effectively, as they transform words into high-dimensional vectors that reflect their contexts and similarities. Through techniques like deep learning, embeddings can capture not only the literal meaning of words but also their connotations and relationships with other words. For example, in an embedding space, words like ‘king’ and ‘queen’ will be closer to each other than words like ‘king’ and ‘dog’, indicating a stronger semantic relationship. This ability to mathematically represent language has revolutionized natural language processing (NLP), allowing large language models to perform complex tasks such as machine translation, text generation, and sentiment analysis. Token embeddings are fundamental to the functioning of modern architectures like Transformers, which are the basis of many of today’s most advanced language models.
History: Token embeddings have their roots in the 2000s, with the development of models like Word2Vec by Google in 2013, which popularized the idea of representing words as vectors in a continuous space. This approach was an evolution of earlier methods like the bag-of-words model and TF-IDF. From there, more advanced techniques such as GloVe and FastText were developed, improving the quality of embeddings by considering the context and relationships between words. The introduction of architectures like Transformers in 2017 marked a milestone in the evolution of embeddings, allowing models to capture more complex and contextual relationships.
Uses: Token embeddings are used in a variety of natural language processing applications, including machine translation, text generation, sentiment analysis, and semantic search. They are also fundamental in recommendation systems and text classification, where understanding the meaning and relationship between different words and phrases is required. Additionally, they are used in chatbots and virtual assistants to improve natural language understanding.
Examples: An example of token embeddings usage is in models like BERT and GPT, which use contextual embeddings to enhance language understanding in tasks like question answering and sentiment analysis. Another example is the use of embeddings in recommendation systems, where product descriptions and user reviews can be analyzed to provide personalized recommendations.
