An introduction to Transformers

In the world of AI-powered chatbots and deep learning initiatives, transformers have become the backbone of many generative AI powered applications. Transformers can effectively model patterns in large volumes of data, making them the powerful component behind large language models (LLMs). Transformers were first introduced by Google in 2017, but have since become more powerful thanks to the increase in availability of compute resources and model sizes. This article introduces what transformers are, their role in machine learning and other applications as well as an overview of the concepts and architecture behind them.

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What Are Transformers?

Transformers are a type of neural network architecture that analyses patterns in large volumes of data. They’re most commonly used for Natural Language Processing (NLP) tasks, where their ability to process data all at once makes them an efficient and highly accurate tool. Transformers, as their name suggests, are designed to solve tasks that involve transforming an input into an output. They can analyse patterns in data by looking at the relationships between different elements. This is what makes them especially proficient as a base for language models.

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Transformer in Machine Learning

1. Breakthrough in natural language processing (NLP)

Transformers were first conceived by Ashish Vaswani et al in 2017 in their research paper, “Attention Is All You Need.” Since then, transformer models have emerged as a state-of-the-art process for NLP due to their ability to analyse textual data as an input.

In the past, language bots have faced challenges regarding words that have multiple meanings. Think of translation software such as Google Translate, for example. But with the help of transformers, language models like Chat-GPT (with the ‘T’ standing for transformer) have never been more accurate. The transformer’s ability to discern context between words makes it a highly powerful tool and has increased its accuracy for NLP-based tasks.

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2. Role in various machine learning tasks beyond NLP

While NLP tasks are where transformers work best, they also have plenty of applications outside of text processing. They can be used for things such as speech recognition and transcription to provide more accurate results. Another use is for image captioning through analysing images to create a relevant description.

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Core Concepts of Transformers

Attention mechanism

One of the most important components of a transformer is its ability to understand context, which is derived through ‘attention.’ Attention is simply a mathematical process that captures relationships with the input data. For example, it can look at how words fit together in a sentence to find meaning. This allows it to process sequential information and work out what would go next. This is one of the key features of generative AI models.

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Self-attention

Another important part of transformers is their ‘self-attention’. This is where the model weighs up the importance of certain components of the input data.

While this might sound similar to attention, self-attention is slightly more complex. The ‘self’ part refers to the model’s ability to refer back to itself and what it already understands about the data. This allows it to recognise patterns in the data, making it effective for long-range dependencies.

Transformer Architecture

Encoder-Decoder Structure

Encoding and decoding are two of the key components of the transformer structure. They are how the model processes the input you give it.

The encoder produces a sequence of vectors based on the input you give it. This is where the model attempts to understand the data that it’s been given, and turns it into a numerical representation.

On the other hand, the decoder is what uses the vectors generated by the encoder. It converts the vectors back into something you can understand.

So. for example: if you’re translating a sentence, the decoding part is where the model turns it into the language you’re looking for!

Multi-Head Attention

One of the main aspects of a transformer is its ability to measure the relationship between ‘tokens’ (e.g. words in a sentence) to determine the correct context. This is known as ‘attention,’ which we discussed earlier.

Multi-head attention is basically when the transformer runs several of these attention tests at once.

This allows it to find nuances in certain tokens, and what makes transformers exceptionally powerful at discerning context.

Applications Beyond NLP

Computer Vision

Transformers have many applications outside of natural language processing tasks.

For example, they’re useful for object detection and image classification. They can quickly process images and derive information from them, creating descriptions or captions as needed.

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Speech Recognition

Another way transformers are used outside of NLP is for speech recognition. Transformers have proved to be a useful addition for voice assistants like Siri, Alexa, and Google Assistant. They can recognise patterns and contexts, making it easier for bots to transcribe spoken text and respond accurately.

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Conclusion

Transformers have revolutionised the world of natural language processing and are behind the success of generative AI applications such as ChatGPT and Vault. Their accurate ability to recognise patterns and context in data makes them an invaluable tool. With the continued development of LLMs, we can expect to see even more progress in the future.

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About TextMine

TextMine is an easy-to-use data extraction tool for procurement, operations, and finance teams. TextMine encompasses 3 components: Legislate, Vault and Scribe. We’re on a mission to empower organisations to effortlessly extract data, manage version controls, and ensure consistency access across all departments. With our AI-driven platform, teams can effortlessly locate documents, collaborate seamlessly across departments, making the most of their business data.