Transfomers: the engine that drives AI model evolution.
Join our daily and weekly emails to receive the latest updates on AI. Today, almost every leading-edge AI model and product uses a transformor architecture. Transformers are the underlying technology for large language models (LLMs), such as GPT-4o and LLaMA. Other AI applications, such as text to speech, automatic speech recognition and image generation, also use transformers. It’s time for transformers to be given their due. With AI hype not expected to diminish anytime soon, I’d explain how they work and why they’re so important to the growth of scalable solution. They are also the backbone of LLMs. Transformers do more than meet the eye
In short, a Transformer is a neural net architecture that models sequences of data. This makes them perfect for tasks like language translation, automatic speech recognition, and sentence completion. Transformers are the most popular architecture for sequence modeling tasks, because they can easily be parallelized. This allows for large scale training and inference. The transformer architecture was first introduced by Google researchers in their 2017 paper “Attention is All You Need”. It was designed as an encoder-decoder for language translation. Google released the bidirectional encoders representations from transforms (BERT) in 2017, which was one of the earliest LLMs, although it is now considered small. Since then, and particularly with the advent GPT models by OpenAI, the trend has been training bigger and larger models with more data and parameters. Context windows have also been extended.
To help this evolution, many innovations have been made, including: better GPU hardware for multi-GPU, and improved software to reduce memory consumption. Quantization and mixtures of experts (MoE), new optimizers like Shampoo and AdamW, and techniques for computing attention efficiently, such as FlashAttention, and KV Caching. This trend is likely to continue in the near future.
The importance of self-attention in transformers
Depending on the application, a transformer model follows an encoder-decoder architecture. The encoder component uses a vector representation to learn data, which can be used in downstream tasks such as classification and sentiment analysis. Decoders use vectors or latent representations of text or images to create new text. This is useful for tasks such as sentence completion and summary. Decoder-only models are common in many popular state-of the-art models.
Encoder-decoder models combine both components, making them useful for translation and other sequence-to-sequence tasks. The attention layer is the key component of both decoder and encoder architectures. This is because it allows models to retain context for words that appeared much earlier in a text. Attention comes in two varieties: self-attention, and cross-attention. Self-attention captures relationships between words in the same sequence. Cross-attention captures relationships between two different sequences. Cross-attention is used to connect encoder and decoder parts in a model, and also during translation. Cross-attention is a form of matrix multiplication that can be performed very efficiently by a GPU. Because of the attention layer in transformers, they can capture better relationships between words that are separated by large amounts of text. Previous models, such as recurrent networks (RNN) or long-short-term memory models (LSTM), lost track of context for words earlier in the text. The future of models.
Transformers are currently the dominant architecture in many LLM use cases and receive the most research and developments. State-space models, such as Mamba, have recently gained popularity despite the fact that this is not likely to change in the near future. This algorithm is highly efficient and can handle long data sequences, while transformers have a context window. The most interesting applications of transformers are multimodal models. OpenAI’s GPT-4o can handle text, audio, and images. Other providers are now starting to follow. Multimodal applications can be very diverse. They range from video captioning, voice cloning and image segmentation to name a few. These applications also offer an opportunity to make AI accessible to people with disabilities. A blind person, for example, could benefit greatly from the ability to use voice and audio components in a multimodal app. It’s a space that is full of possibilities for new applications.
Terrence Alsup is a senior data scientist at Finastra.
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Terrence Alsup is a senior data scientist at Finastra.
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