Since they were introduced in 2017, transformers have become a powerful tool in the field of Machine Learning, particularly in translation and autocomplete services. The popularity of transformers has only grown with the introduction of large language models like OpenAI’s ChatGPT, GPT-4, and Meta’s LLama. These models are all based on the transformer architecture and have made significant advancements in natural language understanding and generation.
Many resources explain how transformers work mathematically, but it can be challenging to understand them intuitively. After speaking with colleagues and conducting interviews, it became clear that this is a common problem. In this blog post, I will provide a high-level explanation of transformers without relying on code or mathematics. My goal is to avoid technical jargon and comparisons to previous architectures, and instead focus on providing a better intuition of how transformers work.
A transformer is a type of neural network architecture that is well-suited for tasks involving sequences as inputs. A sequence can be something like a sentence, which is an ordered set of words. The goal of transformer models is to create a numerical representation for each element in the sequence. These representations contain essential information about the element and its surrounding context. Downstream networks can then use these representations to perform various tasks like generation and classification.
Transformers excel at handling long-range dependencies within sequences and are highly efficient, capable of processing sequences in parallel. This makes them particularly useful for tasks like machine translation, sentiment analysis, and text generation.
To feed an input into a transformer, we first convert it into a sequence of tokens, which are integers representing the input. In natural language processing (NLP), a common way to convert a sentence into tokens is by defining a vocabulary that maps words to integers. We can also use special tokens to represent characteristics like the start and end of a sentence. Tokenization strategies like byte-pair encoding can be used to handle cases where words need to be broken into smaller chunks.
Transformers are not limited to text inputs and have shown good results in vision tasks as well. For example, an image can be converted into a sequence by slicing it into patches and concatenating them into a vector. In a recommender system, item IDs of recently browsed items can be used as input tokens.
Once we have a sequence of tokens, we convert them into embeddings, which are compressed representations of the tokens that can be easily processed by machine learning algorithms. Embeddings capture the meaning of the tokens as sequences of numbers. Positional encoding is applied to preserve the ordering of the tokens, and attention mechanisms handle the context in which tokens appear.
The attention mechanism is a crucial part of the transformer architecture. It helps the network understand which parts of the input sequence are relevant for the given task. By replacing each token embedding with an embedding that includes information about its neighboring tokens, attention captures the context of each token. This is achieved through a weighted average or linear combination of the embeddings.
In summary, transformers have revolutionized machine learning by enhancing the capabilities of translation and autocomplete services. They create numerical representations for elements within sequences, allowing downstream networks to better understand patterns and relationships within the input. Transformers excel at handling long-range dependencies and are highly efficient. They can handle various types of inputs, not just text. By converting inputs into tokens and then embeddings, transformers capture the meaning and context of the input sequence, leading to improved performance in various tasks.