Earlier this year, I had the opportunity to collaborate with the Computer History Museum on an exciting project focused on large language models (LLMs). As a frequent creator of educational content on this subject, it was a delight to contribute to this exhibit for a museum I hold in high regard. Initially, I imagined the project would be a simplified version of my existing detailed explainers, but it evolved into an enriching experience that allowed me to highlight crucial concepts often overlooked in more technical discussions.
The aim of this article is to provide a comprehensive yet digestible overview of large language models, explaining their functionality, training processes, and underlying technologies.
Consider a scenario where you discover a partial movie script featuring a dialogue between a person and their AI assistant. The script includes the person's queries, but the responses of the AI are missing. Imagine you possess a magical machine capable of predicting the next word based on the provided text. You would feed the script into this machine and, by repeating the process, gradually complete the interactions. This is fundamentally how chatbots operate using large language models.
An LLM functions as a mathematical entity that predicts the subsequent word for any text given. Rather than delivering a single definitive word, these models generate probabilistic predictions for all potential next words. Building a chatbot involves inputting a scripted interaction alongside user input while prompting the model to compute the next word iteratively. This method produces outputs that reflect a more natural conversation style, especially when it randomly selects from less likely options.
The Training Process
To create an LLM, massive datasets—most often sourced from the internet—are processed. For instance, the training dataset for GPT-3 would take over 2,600 years for an average human to read continuously. Modern models train on exponentially more data.
The training can be imagined as adjusting various dials on an extensive machine, where the model's behavior is shaped entirely by numerous continuous values known as parameters or weights. Each model can possess hundreds of billions of these parameters, which no human explicitly sets. Instead, they start at random and are refined through an extensive learning process involving large sets of text.
The training method employs an algorithm known as backpropagation, which adjusts the parameters to enhance the model's accuracy. After being provided with a training example—irrespective of its length—the model predicts what the next word should be and is adjusted based on its accuracy. This iterative process leads to improved predictions on unseen text.
Training large language models requires staggering computational resources. To understand the scale, consider that performing a billion additions and multiplications per second would still take over 100 million years to complete all computations involved in training the largest models. This extraordinary feat is achievable only with specialized hardware, such as GPUs, optimized for parallel computing.
Historically, language models processed data sequentially—one word at a time—until 2017, when Google introduced the transformer model. This revolutionary architecture allows models to ingest text all at once and in parallel, significantly improving processing efficiency.
Transformers represent a significant leap in the way language models operate. The first step in a transformer involves encoding each word as a list of numbers, essential for processing language mathematically. This encoding allows the model to handle the training process using continuous values.
A key feature of the transformer model is its "attention" mechanism. This process allows the numerical representations of words to communicate and adjust their meanings based on surrounding context. For example, the meaning of the word "bank" could be refined to represent a "riverbank" depending on adjacent words in a sentence. Additionally, transformers typically utilize feed-forward neural networks to enhance the model's ability to store information about language patterns gleaned during training.
Within this framework, data flows through iterative interactions of attention and feed-forward operations, enriching the model's knowledge. The final step involves generating a prediction based on the adjusted representation of context and learned information.
The Emergent Nature of Predictions
Despite the framework developers create, the unique behavior of LLMs arises from the emergent outcomes of their vast parameters. This complexity makes it particularly challenging to explain why a model arrives at specific predictions.
Nevertheless, the results of using large language models for generating text are often astonishingly fluent, relatable, and practical. Anyone in the Bay Area should consider visiting the Computer History Museum to engage with this fascinating exhibit on large language models.
For those curious to dive deeper into transformers and the mechanics of attention, a variety of resources are available. I urge you to explore my comprehensive series on deep learning that visualizes and elaborates on these intricate concepts, or check out my recent presentation on the topic for TNG in Munich.
By sharing this knowledge, I hope to shed light on the complexities of large language models and inspire curiosity about their future potential.
Part 1/8:
Understanding Large Language Models: A Deep Dive
Earlier this year, I had the opportunity to collaborate with the Computer History Museum on an exciting project focused on large language models (LLMs). As a frequent creator of educational content on this subject, it was a delight to contribute to this exhibit for a museum I hold in high regard. Initially, I imagined the project would be a simplified version of my existing detailed explainers, but it evolved into an enriching experience that allowed me to highlight crucial concepts often overlooked in more technical discussions.
The aim of this article is to provide a comprehensive yet digestible overview of large language models, explaining their functionality, training processes, and underlying technologies.
Part 2/8:
Conceptualizing Large Language Models
Consider a scenario where you discover a partial movie script featuring a dialogue between a person and their AI assistant. The script includes the person's queries, but the responses of the AI are missing. Imagine you possess a magical machine capable of predicting the next word based on the provided text. You would feed the script into this machine and, by repeating the process, gradually complete the interactions. This is fundamentally how chatbots operate using large language models.
Part 3/8:
An LLM functions as a mathematical entity that predicts the subsequent word for any text given. Rather than delivering a single definitive word, these models generate probabilistic predictions for all potential next words. Building a chatbot involves inputting a scripted interaction alongside user input while prompting the model to compute the next word iteratively. This method produces outputs that reflect a more natural conversation style, especially when it randomly selects from less likely options.
The Training Process
To create an LLM, massive datasets—most often sourced from the internet—are processed. For instance, the training dataset for GPT-3 would take over 2,600 years for an average human to read continuously. Modern models train on exponentially more data.
Part 4/8:
The training can be imagined as adjusting various dials on an extensive machine, where the model's behavior is shaped entirely by numerous continuous values known as parameters or weights. Each model can possess hundreds of billions of these parameters, which no human explicitly sets. Instead, they start at random and are refined through an extensive learning process involving large sets of text.
The training method employs an algorithm known as backpropagation, which adjusts the parameters to enhance the model's accuracy. After being provided with a training example—irrespective of its length—the model predicts what the next word should be and is adjusted based on its accuracy. This iterative process leads to improved predictions on unseen text.
Scaling Computational Power
Part 5/8:
Training large language models requires staggering computational resources. To understand the scale, consider that performing a billion additions and multiplications per second would still take over 100 million years to complete all computations involved in training the largest models. This extraordinary feat is achievable only with specialized hardware, such as GPUs, optimized for parallel computing.
Historically, language models processed data sequentially—one word at a time—until 2017, when Google introduced the transformer model. This revolutionary architecture allows models to ingest text all at once and in parallel, significantly improving processing efficiency.
The Transformer Revolution
Part 6/8:
Transformers represent a significant leap in the way language models operate. The first step in a transformer involves encoding each word as a list of numbers, essential for processing language mathematically. This encoding allows the model to handle the training process using continuous values.
A key feature of the transformer model is its "attention" mechanism. This process allows the numerical representations of words to communicate and adjust their meanings based on surrounding context. For example, the meaning of the word "bank" could be refined to represent a "riverbank" depending on adjacent words in a sentence. Additionally, transformers typically utilize feed-forward neural networks to enhance the model's ability to store information about language patterns gleaned during training.
Part 7/8:
Within this framework, data flows through iterative interactions of attention and feed-forward operations, enriching the model's knowledge. The final step involves generating a prediction based on the adjusted representation of context and learned information.
The Emergent Nature of Predictions
Despite the framework developers create, the unique behavior of LLMs arises from the emergent outcomes of their vast parameters. This complexity makes it particularly challenging to explain why a model arrives at specific predictions.
Part 8/8:
Nevertheless, the results of using large language models for generating text are often astonishingly fluent, relatable, and practical. Anyone in the Bay Area should consider visiting the Computer History Museum to engage with this fascinating exhibit on large language models.
For those curious to dive deeper into transformers and the mechanics of attention, a variety of resources are available. I urge you to explore my comprehensive series on deep learning that visualizes and elaborates on these intricate concepts, or check out my recent presentation on the topic for TNG in Munich.
By sharing this knowledge, I hope to shed light on the complexities of large language models and inspire curiosity about their future potential.