Demystifying Large Language Models: How They Differ from Traditional ML
By Rishi Arora and Aravinda Kumar KG:
- In Part 1 (current), we primarily focus on understanding the differences between LLMs and traditional ML models.
- In Part 2, we will explore Hallucinations in LLM through simple quiz examples and techniques to reduce hallucinations.
- In Part 3, we will discuss the security concerns around the usage of LLMs
- Subsequent parts of the series will explore the limitations of LLMs such as Data Quality, Ethical Concerns, and High Computational Requirements.
How Large Language Model Differ from Traditional ML
Introduction
In today’s tech landscape, “Large Language Models (LLMs)” are a constant buzzword. But what exactly are they, and why should you care? This series delves into the world of LLMs, comparing them to traditional Machine Learning (ML) models and highlighting their potential benefits.
Let’s take a step back and scratch the surface to gain a better understanding of LLMs.
LLMs vs. Traditional ML
Before venturing into the LLM realm, let’s compare them to the AI workhorses we’re familiar with: Traditional ML models. Imagine embarking on an AI journey for your customers. To do this effectively, understanding the “activities” and “capabilities” of both LLMs and ML models is crucial.
Activities
Firstly, let’s explore the “activities” involved in each approach.
At first glance (depicted in Diagram 1), LLMs seem like the ultimate plug-and-play solution. They’re ready to use right from day one, while traditional ML models require data scientists to meticulously build them using historical data. This “build-before-use” approach adds an extra layer of complexity to the AI journey.
Capabilities
Now, let’s consider “capabilities.” If your customer needs a specific function like text classification or fraud detection, a dedicated ML model trained on historical data is the answer. However, if they require diverse functionalities, things get complicated. As shown in Diagram 2a, a custom logic needs to be built to combine the outputs of multiple ML models, potentially leading to a tangled web of functionalities.
But here’s where LLMs shine. These versatile champions boast a broad spectrum of capabilities, encompassing tasks like text generation, summarization, code generation, and translation. This eliminates the need for custom logic, as shown in Diagram 2b. It’s like having a multi-talented AI teammate who can handle multiple roles, simplifying operations and reducing management headaches.
Fast-Forwarding AI: Why LLMs Matter
So, you might be wondering: with LLMs seemingly simplifying the AI journey, what’s the catch? Well, as with any powerful tool, they come with their own set of challenges and limitations, which we’ll explore in upcoming parts. But for now, remember this: LLMs offer a faster path to achieving your AI goals, providing a broader range of functionalities under one roof.
We’ll delve into aspects like
- Hallucinations
- Data Quality
- Security Concerns
- Ethical concerns
- High computational requirements
Stay tuned for the next part, where we’ll dive deeper into the challenges of LLMs and equip you with strategies to navigate them!
