Skip to Content

Generative AI Fundamentals Accreditation: What are Tokens and How are They Used in Large Language Models

Learn what tokens are and how they are used to convert natural language text into numerical representation for large language models.

Single Choice Question

In Large Language Models (LLMs), the input text is divided into pieces and converted into numeric values. What is the term used to describe each of these input chunks?

A. Token
B. Embedding Function
C. Decoding
D. Transformer


A. Token


Large Language Models (LLMs) are machine learning models that can process and generate natural language text, such as sentences, paragraphs, or documents. LLMs are trained on a large amount of text data, and learn to predict the next word or words in a sequence, given some previous words or context.

However, LLMs cannot directly work with raw text, as they need a numerical representation of the input and output. Therefore, the input text is first split up into words or word parts, called tokens, and a numerical representation of these are produced. So we started with natural language text, but now we have a lot of numbers that encode useful information, learned during training, about each word or word part in context.

A token can be a whole word, a subword, or a character, depending on the tokenization method used. Tokenization is the process of dividing the text into tokens, and assigning a unique numerical identifier to each token. For example, the sentence “I love cats” can be tokenized into three tokens: “I”, “love”, and “cats”. Each token can then be mapped to a number, such as 1, 2, and 3, respectively.

The numerical representation of the tokens is then fed into the LLM, which uses a neural network architecture, such as a Transformer, to process the input and generate the output. The output is also a sequence of numbers, which can then be converted back to text using the inverse mapping of the tokenization.

Generative AI Exam Question and Answer

The latest Generative AI Fundamentals Accreditation actual real practice exam question and answer (Q&A) dumps are available free, helpful to pass the Generative AI Fundamentals Accreditation certificate exam and earn Generative AI Fundamentals Accreditation certification.

Alex Lim is a certified IT Technical Support Architect with over 15 years of experience in designing, implementing, and troubleshooting complex IT systems and networks. He has worked for leading IT companies, such as Microsoft, IBM, and Cisco, providing technical support and solutions to clients across various industries and sectors. Alex has a bachelor’s degree in computer science from the National University of Singapore and a master’s degree in information security from the Massachusetts Institute of Technology. He is also the author of several best-selling books on IT technical support, such as The IT Technical Support Handbook and Troubleshooting IT Systems and Networks. Alex lives in Bandar, Johore, Malaysia with his wife and two chilrdren. You can reach him at [email protected] or follow him on Website | Twitter | Facebook

    Ads Blocker Image Powered by Code Help Pro

    Your Support Matters...

    We run an independent site that is committed to delivering valuable content, but it comes with its challenges. Many of our readers use ad blockers, causing our advertising revenue to decline. Unlike some websites, we have not implemented paywalls to restrict access. Your support can make a significant difference. If you find this website useful and choose to support us, it would greatly secure our future. We appreciate your help. If you are currently using an ad blocker, please consider disabling it for our site. Thank you for your understanding and support.