1. Novelty of LLMs
Previously, we highlighted the role of Large Language Models (LLMs) in the AI landscape.
Now, we will explore their distinctive capabilities and key learning techniques.
2. Using text data
Recall that LLMs use text data in various ways.
Text data used in sentiment analysis, spam classification, and digital assistants is unstructured and can be messy and inconsistent.
3. Machines do not understand language!
Besides, computers cannot understand language in the same way that humans do.
For example, a computer does not know how to process raw text like "I am a data scientist".
4. Need for NLP
This is because they don't read the text as we do. Instead, they read in numbers, which is the language of computers.
Natural Language Processing (NLP) techniques address this challenge by converting the text into numerical form, enabling machines to identify patterns and structures.
These NLP techniques are the foundations of LLMs.
5. Unique capabilities of LLMs
After exploring NLP's role in language learning, let us understand the unique capabilities of LLMs.
The novelty of an LLM is the ability to detect linguistic subtleties like irony, humor, pun, sarcasm, intonation, and intent.
6. What's your favorite book?
For example, an LLM can provide a human-like response when asked about its favorite book.
It may start with a natural response like "Oh, that's a tough one," followed by a personal recommendation, "My all-time favorite book is To Kill a Mockingbird by Harper Lee".
To support its choice, it may highlight a key theme and initiate a further exchange by asking, "Have you read it?" making the conversation more natural.
7. Linguistic subtleties
Let's explore how LLMs understand linguistic subtleties better than traditional language models.
Consider the sarcastic statement, "Oh great, another meeting."
The traditional model responds neutrally with "What's the meeting about?" and fails to capture the underlying sarcasm,
while an LLM generates "Sounds like you're looking forward to it", a playful and engaging response that matches the sarcastic tone.
8. How do LLMs understand
These examples demonstrate the impressive ability of LLMs to understand language.
But what makes this possible?
As we learned earlier, LLMs are considered "large" because they are trained on vast data. Another key factor contributing to their "largeness" is parameters.
Parameters represent the patterns and rules learned from training data. More parameters allow for capturing more complex patterns, resulting in sophisticated and accurate responses.
9. Parameters
The concept is similar to building with Lego bricks, where a few bricks only allow for a simple structure, while a larger number of bricks can create detailed structures.
10. Emergence of new capabilities
These massive parameters also give rise to LLMs' emergent capabilities, unique to large-scale models like LLMs and not found in smaller ones.
Scale is determined by two factors: the volume of training data and the number of model parameters.
As the scale increases, the model's performance can dramatically improve beyond a certain threshold, resulting in a phase transition and a sudden emergence of new capabilities,
11. Emergence of new capabilities
such as music and poetry creation, code generation, and medical diagnosis.
12. Building blocks of LLMs
To reach this threshold, LLMs and their parameters undergo a training process - text pre-processing, text representation, pre-training, fine-tuning, and advanced fine-tuning.
We will cover each of these in the upcoming videos.
13. To recap
In this video, we learned how LLMs improve text data applications, from digital assistants to sentiment analysis, despite the data's unstructured nature.
LLMs outperform traditional models by comprehending complex linguistic subtleties and generating detailed responses.
This performance boost is a result of the "largeness" of LLMs, which is due to extensive training data and many parameters.
These factors also enable "emergent abilities", which unlock advanced capabilities and expand LLMs' use cases, making them a potent tool in Natural Language Processing.
14. Let's practice!
Time to practice.