One question many people have as they are learning more about generative AI technologies is the difference between transformers and LLMs. The simplest way to explain the difference is that transformers describe a particular architecture pattern while LLMs describe a particular type of AI model. Some LLMs leverage the transformer architecture in their implementation which sometimes makes it easy to confuse the two concepts or think of them as synonymous. For example, the GPT in ChatGPT stands for generative pre-trained transformer. However, not all LLMs are built using a transformer architecture.
An LLM, or Large Language Model, is a type of AI model designed with the intention of understanding and generating human language. LLMs are trained on massive datasets comprising a wide range of texts. Because of this, an LLM can predict the next word in a sequence, allowing it to produce natural sounding and contextually appropriate sentences. This capability extends beyond simple text generation. LLMs can also be used to power applications such as text summarization, translation, and even assisting in coding tasks.
Transformers, on the other hand, can handle a wider range of use cases, including ones that are much more narrow. For instance, there are transformers which are intended to perform sentiment analysis, identify named entities in a text, or even translate between languages. BERT, or Bidirectional Encoder Representations from Transformers, is a well-known transformer model which excels at understanding the context of words in search queries, and has been revolutionary for improving search engine results. Transformers are also widely used for tasks such as summarization and question answering.