Building basic intuition for Large Language Models (LLMs)

9 min readDec 4, 2023

Introduction

While composing this article, I find myself enveloped in a heavy downpour, comfortably nestled in a cozy nook with a cup of coffee in hand, pondering the future decade. This relentless rain is much like the surge of innovations in the Generative AI (GenAI) realm, reminiscent of how Large Language Models (LLMs) are steadily revolutionizing their domain. My intent with this article is to unravel this seeming magic of LLMs and Generative AI. It’s for everyone who has used products like ChatGPT, felt awe at their capabilities, yet harbors concerns about their implications. Will these technologies take away jobs? Could they dominate our world? These are valid questions, and while this article draws from my current understanding and interpretations of GenAI, intertwined with philosophical musings, I would suggest you do due research if you want to deep dive into the technical aspects of Gen AI.

To understand this article, no prior knowledge is required. If you are someone with no background in coding or data science, consider it an advantage. A clean slate, unburdened by the need to unlearn, can often grasp new concepts with greater ease and clarity. I aim to simplify these complex technologies to build intuition, to make understanding them a more humane and approachable process in the context of LLMs. These technologies are not just fleeting trends in the digital landscape; they represent a significant shift in how we interact with information, process data, and even perceive creativity. In an era where digital transformation is ubiquitous, understanding the relevance and significance of LLMs as a part of Generative AI becomes crucial. These technologies are reshaping industries, altering job roles, and even redefining the boundaries of human and machine collaboration. As we delve into this article, we’ll explore not just the hows and whys of these technologies but also aim to demystify them, bringing a sense of familiarity to what may initially appear as nothing short of wizardry. So, grab your cup of coffee, settle in, and let’s embark on this journey of understanding the nuanced world of LLMs.

Why Understanding AI, GenAI, and LLMs is Important

The journey of technological evolution has been marked by significant milestones that have reshaped human civilization. Consider the historical analogies that underscore the importance of these technological leaps. The invention of fire, the harnessing of electricity, and the proliferation of the internet each revolutionized human life. AI is poised to be the next frontier, akin to an inflection point similar to the Industrial Revolution, with the potential to redefine our everyday lives and work. Understanding AI and LLMs is not merely about keeping pace with technological advances; it’s about comprehending a fundamental shift in human capabilities and interactions. This is exactly the point where we’ll seamlessly start unifying Artificial, Business, and Human intelligence.

The evolution of AI can be compared to how humans adopted cars for transportation. We didn’t abandon walking in favor of cars; we embraced them for their efficiency and convenience. Similarly, AI isn’t here to replace human intelligence but to augment it, making tasks simpler and more innovative. As AI grows more powerful, understanding its mechanisms becomes crucial for fostering a harmonious relationship between humans and AI.

Reflecting on my childhood in India, I remember the high costs associated with internet access, a stark contrast to today where India is known for its cheapest internet rates across the world. Looking at the pace of development that has happened since a year, such democratization of access will soon be echoed in the AI sector too, making it more accessible than ever like it happened for the Internet via smartphones.

With LLMs, the way we interact with machines is undergoing a revolutionary change. Previously, interactions with machines were mediated through applications and programming languages. Now, machines can understand and respond in natural languages like English. This leap isn’t just about convenience; it’s about opening a new chapter in human-machine interaction. With the emergence of Large Multimodal Models (LMMs), which I’ll discuss in a separate article, machines will not only understand text but will also see, hear, and speak, further blurring the lines between human and machine intelligence.

Understanding LLMs: Basic Assumptions

Human Behavior Patterns can be generalized: Humans, despite their diversity, exhibit certain common patterns in behavior. These patterns stem from various factors: genetic similarities, shared experiences like pandemics, the influence of religions, governmental systems, and our collective interaction with nature. When we look at a large sample of people, these patterns become more apparent. Our actions, reactions, and interactions often follow predictable paths, influenced by our shared human experience.

The Internet is a collective Human Projection: Internet has been a part of our lives for over two decades, becoming a digital repository of human expression and interaction. Every search query, social media post, blog, and online transaction has contributed to an immense accumulation of data. This data is a reflection of humanity — a digital projection encompassing a wide array of human experiences, thoughts, and behaviors. It’s a rich, complex tapestry of the digital human footprint.

Neural Networks are powerful: Neural Network is a technical term for the building blocks of complex AI systems. To understand neural networks in the simplest terms, imagine a small child learning to recognize animals. The child sees different pictures of dogs and gradually begins to understand the general pattern of what makes a dog a dog. Neural networks in AI work similarly. They analyze vast amounts of data (like our internet data) and learn to identify patterns and make predictions. These networks are made up of layers of ‘neurons,’ small computational units that work together to process and interpret data. Through this process, neural networks can learn from the data and get better over time at tasks like language understanding, image recognition, and more. For now we just assume that neural networks are powerful enough for our LLMs. Ref Video to understand Neural Networks in a min

Understanding LLMs: In Simple English

Understanding how Large Language Models (LLMs) function can be simplified into a few key concepts:

Neural Networks and Language Training

At the core of LLMs are neural networks. Think of these networks as a highly advanced, complex form of pattern recognition. When LLMs are being developed, they are trained on vast amounts of text data. This training process involves feeding the model with sentences and teaching it to predict what word comes next. For instance, if you give an LLM the beginning of a sentence like “Once upon a time a fox,” it uses its training to predict the next word in the sequence.

Transformer Architecture

The real magic behind LLMs lies in a special type of neural network architecture known as the Transformer. This architecture is designed to not only predict the next word in a sentence but to also keep refining its predictions as new words are added. So, when an LLM predicts the next word after “Once upon a time a fox,” it doesn’t stop there. It continues to generate words, building out the sentence further, either until it completes the thought (the sentence’s context) or it reaches a pre-set limit on the length of the text (number of words or tokens).

Training for Practical Use — Fine-Tuning

Simply training an LLM to predict the next word isn’t enough to make it genuinely useful for human interaction. For that, the model needs to be fine-tuned — or, as it’s technically known, undergo ‘instruct tuning.’ This process involves shaping the LLM to understand tasks, use its knowledge base effectively, and interact in a safe, non-toxic manner with humans. Fine-tuning adjusts the LLM’s responses so that they are aligned with specific goals and values, like providing helpful, accurate information without causing harm or offense.

Think of an LLM as a highly intelligent, continually learning system that starts with the basic ability to predict words and, through complex processes like Transformer architecture and fine-tuning, becomes capable of understanding and performing tasks in a way that’s helpful and safe for human interaction. This combination of advanced technology and careful training is what makes LLMs such powerful tools in the field of AI and Generative AI.

Connecting the Dots: The Impact and Potential of LLMs

As we explore the capabilities of Large Language Models (LLMs), it becomes clear that their function extends far beyond the basic prediction of the next word in a sentence. These models, when trained on the expansive and diverse data available on the internet, don’t just learn a language; they absorb a vast spectrum of human knowledge and culture. This includes our history, philosophy, values, wisdom, and even our economic systems. The task of next-word prediction, seemingly simple at first, becomes a gateway to understanding the complex web of human thought and society.

With such a profound depth of knowledge embedded within them, LLMs pose both an opportunity and a responsibility. It becomes imperative to guide these models to apply their vast knowledge constructively and ethically. The goal is to align their capabilities with the betterment of society. This isn’t just about fine-tuning for accuracy but also ensuring that the LLMs’ responses are contextually appropriate, ethically sound, and socially beneficial. It’s about teaching these models not just to understand human language, but to interpret and interact with human values and societal norms.

Moreover, LLMs present a unique advantage over human cognition, particularly in their ability to process and make associations among a vast array of data points. The human mind, for all its complexity and ingenuity, has its limitations in data processing and retention. We are naturally constrained by the capacity of our memory and the bandwidth of our cognitive processing. LLMs, on the other hand, are not bound by these limitations. They can analyze and correlate data at a scale and depth that is unattainable for the human brain. For example, consider the task of assessing the health of a plant based on numerous properties. A human might focus on a few apparent aspects like appearance, smell, or texture. An LLM, however, can simultaneously process a multitude of properties, drawing far more nuanced and comprehensive conclusions.

This ability to process extensive and complex data sets gives LLMs an extraordinary edge. They’re not just tools for language translation or text generation; they’re powerful analytical machines capable of offering insights that might be beyond human reach. The journey ahead involves leveraging this potential responsibly, ensuring that these advancements in AI are aligned with the values and needs of human society.

LLMs are not new, they have been consistently growing since 2017

Conclusion

As we conclude our journey through the realm of Large Language Models (LLMs), it’s clear that these advanced AI systems represent more than just technological marvels; they are harbingers of a profound shift in how we interact with information, language, and perhaps even each other.

The future with LLMs promises a landscape where human creativity and AI-driven efficiency coexist, where complex problems find simpler solutions, and where the boundaries of knowledge continuously expand. As we step into this future, it is our collective responsibility to ensure that these technologies are developed and utilized in ways that benefit society as a whole, respecting and upholding the principles of ethical AI.

In summary, building a basic intuition for LLMs is not just about understanding a technological phenomenon; it’s about preparing ourselves for a future where humans and artificial intelligence collaborate more closely than ever before. It’s a future full of possibilities, challenges, and opportunities — a future we are just beginning to imagine.

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