No, Really, What are LLMs?
Demystifying LLMs: What's All the Hype About?
In recent times, there's been an undeniable buzz around Large Language Models (LLMs). From tech enthusiasts to industry professionals, everyone seems to be talking about them. However, amidst this explosion of interest, many companies are rushing to integrate GenAI applications without a comprehensive grasp of their capabilities, limitations, and potential risks.
Given this landscape, I felt the need to pen a two-part series. In this first installment, we'll dive deep into the foundational aspects of LLMs. The subsequent piece will navigate the ever-evolving LLM terrain, highlighting its applications and potential pitfalls.
Understanding Large Language Models
At their core, LLMs are deep learning models. They're trained on vast volumes of text data with a primary goal: predicting the subsequent word in a sentence. For instance, given "The cat is on the ____," the model predicts "mat."
Once adequately trained, these models can craft human-like text, answer queries, aid in writing, code, and much more. Their versatility is evident in their ability to execute tasks even without task-specific training data.
The Transformer Architecture: Why It Matters
"Attention is All You Need." This phrase isn't just a catchy statement but encapsulates the essence of transformers, a pivotal architecture in deep learning, especially for sequence data and natural language processing (NLP).
The Magic of Attention Mechanism
Central to the transformer model is the attention mechanism. It empowers the model to gauge the significance of each word in the input while crafting each word in the output. The brilliance lies in its capacity to consider the entire sentence's context rather than just a fixed window around a word. This capability ensures a robust understanding of long-term dependencies between words.
Tokens and Tokenization: The Building Blocks
Tokens are the basic units of text. They are processed and extracted from a corpus. Tokenization can occur at various levels: sentence, word, character, or subword. Among these, subword tokenization stands out for its flexibility and potency. For instance, "UnFriendly" can be tokenized as "Un + Friend + ly." This approach is employed in architectures like BERT and GPT.
Model Families: Predicting the Next Token
Broadly, there are two primary model types: Autoencoding and Autoregressive models.
Autoencoding models predict tokens based on both past and future contexts. For example, "He ate the entire ___ of Pizza." These models, like the BERT architecture, are efficient for encoding and understanding natural language (NLU).
Autoregressive Models focus on predicting future tokens. Consider, "The joke was funny, she couldn’t stop ___." These models, such as the GPT architectures, are adept at both encoding and generating natural language (NLG), though they might be slower at encoding.
Dive Deeper
For those keen on delving further into transformers, I recommend the seminal paper: Attention Is All You Need. Other insightful resources include The Annotated Transformer and The Illustrated Transformer.
Stay tuned for the next installment, where we'll explore the dynamic world of LLMs, their applications, and the challenges they pose in real-world scenarios.
