Deep Dive: Demystifying LLM Technology Volume 1

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Today’s Deep Dive: 🤖Demystifying LLM Technology Volume 1 - Understanding the Fundamentals💻

Demystifying LLM Technology Volume 1 - Understanding the Fundamentals

In a world filled with non-stop technological advancement, with more and more inventive creations being made every day, certain breakthroughs have emerged that not only redefine the boundaries of innovation but also leave an indelible mark on our tech-heavy society. Large language models (LLMs) stand as one of these breakthroughs, integrating itself into the ever-evolving digital landscape.

It is essential to be aware of large language models and how they work to understand and keep up with our tech-driven world. This goliath form of artificial intelligence has transcended its initial role as a mere language generator, rapidly transforming industries with its ability to understand, communicate, and analyze human language. From creating creative content to deciphering data for automation, the influence of LLMs has been intricate and impactful. In this deep dive series, we will uncover the powerful form of technology that is the large language model, starting with the fundamentals.

The What and How

Essentially, large language models are deep learning algorithms that perform a variety of natural language processing tasks. These technical terms can be intimidating to understand, so let’s go through them one by one.

Deep learning is a type of artificial intelligence that utilizes multiple layers of artificial neural networks to mimic the learning process of the human brain. These networks attempt to simulate the behavior of the human brain, allowing it to “learn” from vast amounts of data, hence “large” in the name, by analyzing said data and learning the patterns and connections between words and phrases. Different from machine learning, a subset of AI also often circulated around in our tech-heavy society, deep learning can learn on its own and perform analytical tasks without much or any human intervention.

With deep learning’s ability to mimic the human brain, it can generate human-like text in ways that make contextual sense, and it can perform natural language processing tasks, which are tasks that involve understanding, interpreting, comprehending, and responding to text or voice data of human language. This can include speech recognition, word sense disambiguation, sentiment analysis, and natural language generation.

Understanding the concept of LLMs might make it sound more intimidating than ever. Right now, LLMs are like robots that understand humans and are slowly becoming “human” in a way, even. The knowledge that all humans have gained for centuries is in the hands of this AI technology, and that has scared some individuals known as “AI Doomers.” Understanding how they work, however, might ease the fear.

So, how do LLMs do the things they do? Simply put, they are trained on terabytes of data to guess the next words in a sentence by guessing “tokens.” Giant racks of GPUs are run for months to examine training data, identify patterns, and create tokens. Tokens are integer numbers between 1 and about 30,000 that correspond to words or common sequences of words and characters found in the text. Texts are converted into integer tokens, and these LLMs predict which tokens should come next.

An easier way to understand the concept of tokens is to try out GPT’s token encoder and decoder, where you can either enter text to tokenize it or convert tokens to text.

The site also provides a list of tokens and their corresponding integers.

LLM Development

Despite only being a big technological phenomenon in the past year or so, LLMs were first developed almost a decade ago. The big entity behind ChatGPT, OpenAI, was founded in 2015, mainly focusing on reinforcement learning, a machine learning training method based on rewarding desired behaviors and/or punishing undesired ones. OpenAI applied this method on Atari game demos and only started directing their technology to language-related use in 2018.

In 2018, OpenAI released GPT-1, the company’s very first basic language model. This model was released following Google’s paper describing Transformer architecture in 2017, a paper essential in regard to language models due to its exposing scale training across multiple machines. A year after, OpenAI released GPT-2 as a scale-up of its predecessor with an increase in parameter count and the size of its training dataset, allowing it to translate texts, answer questions about a topic from a text, summarize passages from a larger text, and generate text sometimes indistinguishable from that of humans.

OpenAI released GPT-3 in 2020, allowing for even more groundbreaking features. Then, history was made on the 30th of November 2022 when ChatGPT came out. The release of ChatGPT created a ripple effect, pushing more developers to create various large language models. In 2023, numerous companies released their own LLMs, including LLaMA, Alpaca, PaLM2, Claude, Bard, Falcon, MPT-30B, and Meta’s most recent LLaMa 2.

LLM Architectural Components and Applications

The abilities LLMs have are considered revolutionary, deservingly so. LLMs have reached these capabilities with key components composed of multiple neural network layers, which include recurrent layers, feedforward layers, embedding layers, and attention layers. These layers combine to process input texts and generate output contents.

The embedding layer creates embeddings from the input text by capturing the semantic and syntactic meaning of the input so the model can understand context. The feedforward layer, the FFN for short, transforms the input embeddings so the model can extract higher-level abstractions from various sources, allowing the model to understand the user’s intent through the text input.

The recurrent layer interprets the input text in sequence and captures the relationship between words in a sentence. The final layer, the attention mechanism, allows the model to focus on single parts of the input text relevant to the task given. This final layer lets the model generate the most accurate outputs.

LLMs’ aptitude and immense range of capabilities have allowed various companies to utilize the technology for a large number of different things. LLMs' ability to understand human intent has made big tech companies, like Google and Bing, utilize LLMs to offer better user results in their search engines. LLMs are used to automate and streamline customer support systems, as well.

Though generally useful and impactful to so many different industries, with big and small use cases here and there, plenty of sectors have also integrated LLMs into their workspace deeply, practically transforming the way they work for the sake of incorporating the model. This includes the medical industry.

The industry’s embrace of LLMs can be seen in Hippocratic, a startup creating the first safety-focused LLM designed specifically for healthcare, aiming to improve healthcare accessibility and health outcomes. Hippocratic is focused on creating a consumer-facing LLM, aimed to explain benefits and billing, provide dietary advice and medication reminders, answer pre-op questions, and onboard patients. Prioritizing safety and comfort for patients, the model is designed with a bedside manner benchmark and signs of humanism.

The rapid growth of LLMs our society has witnessed shows their impeccable potential, and it is exciting to think about how far its developments can go.

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