Humans communicate in natural language by placing words in sequences; the rules about the sequencing and specific form of a word are dictated by the specific language (e.g., English). An essential part of the architecture for all software systems that process text (and therefore for all AI systems that do so) is how to represent that text so that the functions of the system can be performed most efficiently. Therefore, a key step in the processing of a textual input in language models is the splitting canada whatsapp number data of the user input into special “words” that the AI system can understand. Those special words are called “tokens.” for that is called a “tokenizer.” There are many types of tokenizers. For example, OpenAI and Azure OpenAI use a subword tokenization method called “Byte-Pair Encoding (BPE)” for their Generative Pretrained Transformer (GPT)-based models. BPE is a method that merges the most frequently occurring pairs of characters or bytes into a single token, until a certain number of tokens or a vocabulary size is reached. The larger the vocabulary size, the more diverse and expressive the texts that the model can generate.
Once the AI system has mapped the input text into tokens, it encodes the tokens into numbers and converts the sequences that it processed as vectors referred to as “word embeddings.” A vector is an ordered set of numbers – you can think of it as a row or column in a table. These vectors are representations of tokens that preserve their original natural language representation that was given as text. It is important to understand the role of word embeddings when it comes to copyright because the embeddings form representations (or encodings) of entire sentences, or even paragraphs, and therefore, in vector combinations, even entire documents in a high-dimensional vector space. It is through these embeddings that the AI system captures and stores the meaning and the relationships of words from the natural language.