Learn AI Large Language Model Terms with ChatGPT

Last month, I read Nichole Sterling’s LinkedIn post where Kavita Tipnis-Rasal prompted ChatGPT to “Explain the top 10 terms in LLMs to a non technical audience with funny examples” and laughed out loud. The explanations created by AI about AI were great for a non-technical audience.

So, I fed the same prompt into ChatGPT. I ran it three times. Then, I curated, edited, and combined it into this list of terms used in Large Language Models (LLMs). I hope you enjoy it as much as I enjoyed what Tipnis-Rasal shared via Sterling.

Large Language Models (LLMs): Imagine you have a super-smart friend who knows everything about a wide range of topics, from ancient history to modern technology. Now, imagine if you could shrink your friend down and put them inside your computer. That’s essentially what a Large Language Model (LLM) is—a super-smart program trained on massive amounts of text data to understand and generate human-like language.

Natural Language Processing (NLP): Imagine you have a magical translator that can instantly convert your dog’s barks into human language, allowing you to understand exactly what they’re saying. NLP is like that magical translator, but for computers—it helps them understand, interpret, and generate human language, enabling tasks like translation, sentiment analysis, and chatbots to communicate with us in a way that feels natural.

Tokenization: Imagine you’re making a sandwich, but instead of cutting it with a knife, you break it into smaller, manageable pieces with your hands. Tokenization does something similar with text, breaking it down into smaller units, like words or even smaller parts.

Embedding: This is like putting different ingredients into your sandwich to give it flavor. Embedding takes words or phrases and converts them into numerical representations, which helps the model understand their meaning and context.

Attention Mechanism: Picture a teacher in a classroom paying extra attention to some students while explaining a lesson. Similarly, an attention mechanism in LLMs helps them focus on different parts of the text, giving more weight to important words or phrases.

Fine-tuning: Imagine you’ve mastered the art of making a grilled cheese sandwich, but now you want to tweak it a bit by adding different cheeses or toppings. Fine-tuning in LLMs is like adjusting the model’s parameters or training it on specific data to improve its performance for a particular task, like translation or summarization.

Transformer Architecture: Think of a group of robots working together to assemble a giant puzzle. In LLMs, the transformer architecture organizes layers of neural networks to efficiently process and understand large amounts of text data.

Beam Search: Imagine you’re exploring a maze with multiple paths, trying to find the quickest way out. Beam search in LLMs is like looking ahead and considering several possible sequences of words to generate the most coherent and accurate output.

Loss Function: This is like a scoreboard that tells you how well you’re doing in a game. In LLMs, the loss function measures the difference between the predicted output and the actual output, helping the model learn and improve over time.

Gradient Descent: Picture a hiker trying to find the quickest route down a mountain by following the steepest slope. Gradient descent in LLMs is an optimization algorithm that adjusts the model’s parameters in the direction that reduces the loss, helping it converge towards better performance.

Overfitting: Imagine a tailor making a suit that fits one person perfectly but doesn’t look good on anyone else. In LLMs, overfitting occurs when the model learns to perform well on the training data but struggles to generalize to new, unseen data.

Bias-Variance Tradeoff: Think of Goldilocks trying different bowls of porridge—not too hot, not too cold, but just right. In LLMs, the bias-variance tradeoff involves finding the right balance between flexibility (variance) and simplicity (bias) to build a model that generalizes well to new data without overfitting.

Fine-tuning: Suppose you’ve mastered the art of baking cookies, but now you want to experiment with new flavors like bacon or wasabi. Fine-tuning in LLMs is like tweaking a pre-trained model to specialize in specific tasks or domains, much like adding unique ingredients to a familiar recipe.

Pre-training: Imagine you’re a student preparing for a big exam by studying a wide range of topics beforehand. Pre-training in LLMs involves training the model on vast amounts of text data, teaching it general language understanding skills before fine-tuning it for specific tasks like translation or summarization.

Attention Mechanism: Think of attention as a spotlight on a stage, highlighting different actors as they perform. Similarly, an attention mechanism in LLMs directs the model’s focus to relevant parts of the input text, helping it understand context and relationships between words more effectively.

Generative Models: Picture a magician pulling rabbits out of a hat, except instead of rabbits, they’re generating realistic-looking images or text. Generative models in LLMs create new content based on patterns learned from the training data, allowing them to generate coherent paragraphs, poems, or even stories.

Perplexity: Imagine trying to decipher a secret code written in a language you don’t understand. Perplexity in LLMs measures how well the model predicts the next word in a sequence, with lower perplexity indicating better performance, much like cracking a code with fewer guesses.

Backpropagation: Picture a team of detectives trying to solve a crime by retracing their steps and identifying clues along the way. Backpropagation in LLMs is an algorithm that calculates how much each parameter in the model contributed to its error, allowing it to adjust and improve its predictions over time, similar to detectives refining their investigation based on new evidence.

Long Short-Term Memory (LSTM): Imagine you have a forgetful friend who struggles to remember things from the past. Now, picture giving them a magical notebook that helps them selectively remember important information and forget irrelevant details. LSTMs are like that magical notebook for language models, allowing them to maintain long-term context and selectively retain or forget information, making it easier to understand and generate coherent text.

Neural Network: Picture a bustling city with interconnected roads and highways. Now, imagine each intersection is a neuron, and the roads are the connections between them. A neural network in LLMs is like this city, with layers of interconnected nodes working together to process and analyze text data, much like how traffic flows through the city to reach its destination. The more complex the network (or city), the more sophisticated the language understanding and generation capabilities of the model.

I hope these playful examples help demystify the technical jargon surrounding Large Language Models!

Yolanda Lau is an experienced entrepreneurship consultant, advisor, and Forbes Contributor. She is also an educator, speaker, writer, and non-profit fundraiser.

Since 2010, she has been focused on preparing knowledge workers, educators, and students for the future of work.

Learn more about Yolanda here.

Yolanda is also a Founding Board Member of the Hawai’i Center for AI (HCAI), a non-profit organization. HCAI envisions a future in which all of Hawaiʻi’s residents have access to AI technology that effectively and safely serves their individual and collective well-being. Hawai’i Center for AI promotes the beneficial use of AI to empower individuals, communities, and industries throughout Hawai’i. We are committed to understanding the ways AI will help grow the state’s economy, help our institutions evolve, and transform our society. Through collaboration, education, and service, we drive research, innovation, and community partnerships to build a sustainable, prosperous, and policy-driven future for Hawai’i.

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