Language Models: Everything You Need To Know

Today is Day 4 of my 100-day challenge in AI Engineering, sharing daily insights, concepts, and hands-on tips to master AI & ML.

Let’s Talk About Language Models (LMs)

AI has come a long way from simply classifying text and analyzing sentiments to generating human-like text, writing stories, holding conversations, and even composing poetry. These capabilities are powered by Language Models (LMs) AI models trained on? text data.

The roots of Language Models (LMs) trace back to the 1960s, when the first chatbot, ELIZA, was created by Joseph Weizenbaum,1965. ELIZA could simulate human-like conversations and was one of the earliest applications of Natural Language Processing (NLP). Fast forward to 1997, the introduction of Long Short-Term Memory Networks (LSTMs) allowed AI to remember context in sentences, improving tasks like auto-correct in keyboards. Then, in 2011, Google Brain made significant advancements in scaling up Language Models.

However, the true breakthrough came in 2017 with the introduction of the Transformer architecture, which revolutionized NLP. Transformers enabled AI to process and generate text with remarkable fluency, laying the foundation for modern LLMs like GPT and BERT.

Language models encode statistical information from text in one language or more, they try to understand context in words and here’s the process and stages they follow to do this:

Tokenization: Digestion Starts ??

A token is the basic unit of a Language Model (LM) . It can represent a word, subword, or character, depending on how the model processes text. Tokenization is the process of breaking down text whether a sentence, paragraph, or document into smaller units (tokens) that the model can understand and analyze in context.

Example: Tokenization in Action

Let’s say we provide ChatGPT with the following input:

?? Input: "A man wore a black cloth to the funeral."

In the background, the model breaks this sentence into tokens. For instance, using OpenAI’s GPT tokenization method, this might be split into:

??Output:? ["A", "man", "wore", "a", "black", "cloth", "to", "the", "funeral"]

Here, each word is treated as a separate token because GPT-3 primarily uses subword-based tokenization. However, the number of tokens may vary based on the tokenizer used (word-based, subword-based, or character-based).

LM Vocabulary & Token Limits ??

Language models (LMs) have a vocabulary; a predefined set of tokens learned during training. This determines the words, subwords, or characters the model can recognize and process. However, a larger LM doesn’t always mean a larger vocabulary, the vocabulary size is predefined based on the tokenizer used ??

Additionally, OpenAI’s GPT-3 has a token limit to control how much of a prompt can be processed at once.

Types of Tokenization ???

There are different types of tokenization methods, including:

? Sentence-level tokenization (splitting text into sentences)

? Word-level tokenization (splitting text into words)

? Subword tokenization (splitting words into smaller meaningful parts)

? Character-level tokenization (splitting text into individual characters)

I’ll be diving deeper into tokenization in upcoming posts

Word Embeddings: The Nerve of LMs ???

Our nerves break stimuli into electrical signals, embeddings break tokens into vectors

Just like our nerves break down stimuli that’s how embeddings work for LMs?

Neural networks (the engine of LMs) don’t understand words, they work with numbers, specifically vectors. That’s where word embeddings come in. At this stage, tokens are transformed into vector embeddings, allowing the model to process and understand relationships between words.

?? (If you missed my post on embeddings, check it out here: https://www.dhirubhai.net/posts/paulfruitful_100daysofaiengineering-100daysofaiengineering-activity-7295018392615321602-AyT8)

Word embeddings capture semantic relationships between tokens, representing them in a high-dimensional vector space. There are two main types:

?? Fixed Embeddings:

Used in smaller language models, these embeddings remain fixed once trained. They assign the same vector to a word, regardless of context.

Examples: GloVe, Word2Vec

?? Dynamic Embeddings:

More advanced and used in modern LMs like GPT and BERT. These embeddings adapt based on context, meaning the same word can have different vector representations depending on how it’s used. ?? Example:

- Input: “The king is thirsty.”

-Similar embedding to: “The ruler was thirsty.” (since "king" and "ruler" have similar meanings in this context)

Dynamic embeddings enable better contextual understanding, making them essential for state-of-the-art LMs.?

Neural Networks: The Engine of LMs ??

This is where the real magic happens! ??

Neural networks are the engines behind language models (LMs), responsible for processing embeddings, recognizing patterns, and making predictions.

At this stage, the model analyzes embeddings using neurons, which are the fundamental units of a neural network. These neurons are interconnected through multiple layers, allowing the network to extract meaning, identify relationships, and generate coherent responses.

How It Works:

1?? Embeddings Enter the Neural Network → The input tokens are converted into embeddings and fed into the network.

2?? Pattern Recognition & Processing → The network processes embeddings through layers, applying mathematical functions (probability, statistics, linear algebra, etc.).

3?? Prediction & Output Generation → After multiple transformations, the processed data reaches the output layer, where the model predicts the most likely next token(s) based on the given prompt.

?? Think of the neural network as the "brain" of the LM, it continuously refines its predictions, ensuring contextual accuracy and fluency. Unlike traditional text generation, LMs don’t? produce words; they predict the next most relevant tokens based on context.

Types of Language Models

Language models can be classified based on how they process input and how they generate output. Understanding these categories helps in selecting the right model for different NLP tasks.

1?? Language Models by How They Process Input

Different models process input text in distinct ways, influencing their ability to capture context and meaning.

a. Unidirectional Models

• These models process text in a single direction, usually left to right or right to left.
• They generate words sequentially, considering only past tokens.
• Example:GPT series (GPT-2, GPT-3, GPT-4, GPT-Neo, GPT-J) – Predicts the next word/token based on previous ones.Transformer-XL – Improves long-range dependencies in unidirectional models.

Use Cases: Text generation, story completion, and chatbot responses.

b. Bidirectional Models

• These models process text in both directions, considering both past and future context before making predictions.
• They are better at understanding context but are not well-suited for text generation.
• Example:BERT (Bidirectional Encoder Representations from Transformers) – Trains on masked words by considering both left and right context.RoBERTa – An optimized version of BERT with improved pretraining techniques.

Use Cases: Sentiment analysis, named entity recognition (NER), and text classification.

c. Causal Models

• These models only use past tokens to generate new text, making them predictive rather than reconstructive.
• They are autoregressive, meaning they predict the next token based on previously generated ones.
• Example:GPT series (GPT-3, GPT-4, Claude, LLaMA) – Used in chatbots and AI writing assistants.

Use Cases: Creative writing, conversational AI, and code generation.

d. Seq2Seq Models (Encoder-Decoder Architecture)

• These models first encode input into a latent representation and then decode it into output.
• They are commonly used for tasks requiring transformation of input to output (e.g., translation, summarization).
• Example:T5 (Text-to-Text Transfer Transformer) – Converts NLP tasks into a text-to-text problem.BART (Bidirectional and Auto-Regressive Transformer) – Used for text generation, summarization, and translation.

Use Cases: Machine translation, text summarization, and question-answering.

2?? Language Models by How They Generate Output

Language models also differ in how they produce responses, impacting their efficiency and accuracy.

a. Autoregressive Models (AR)

• These models generate text one token at a time, using previously generated tokens to predict the next.
• They are typically unidirectional and work well for generative tasks.
• Example:GPT-3, GPT-4, Claude, LLaMA – Used in AI chatbots and text generation.XLNet – Uses permutation-based training for more contextual accuracy.

Use Cases: Conversational AI, content creation, and storytelling.

b. Autoencoding Models (AE)

• These models learn by masking or corrupting parts of the input text and attempting to reconstruct the original version.
• They are bidirectional and excel at text understanding rather than generation.
• Example:BERT, RoBERTa, DistilBERT – Used in search engines and text classification.

Use Cases: Sentiment analysis, text classification, and information retrieval.

c. Text-to-Text Models

• These models treat all NLP tasks as text input → text output problems, making them highly flexible.
• They can handle translation, summarization, question answering, and more under a unified framework.
• Example:T5, FLAN-T5, mT5 (multilingual T5) – Converts all NLP tasks into text-to-text learning.

Use Cases: Machine translation, document summarization, and chatbots.

d. Diffusion-Based Models

• These are experimental models inspired by diffusion models used in image generation.
• They refine text over multiple iterations, leading to more coherent and context-aware output.
• Example:Mistral (not the Mistral AI LLM, but a diffusion-based NLP model) – Explores new techniques in text generation.

Use Cases: High-quality text generation, advanced creative writing.

Language models vary widely in how they process input and generate output, each suited to different applications. Whether you're working on chatbots, translation, or sentiment analysis, understanding these distinctions can help you choose the best model for your needs.

Language Models (LMs) have come a long way from simple rule-based chatbots to powerful AI models capable of generating human-like text. Understanding tokenization, embeddings, and neural networks is key to mastering how LMs process and generate language.

Thanks for reading, I'll see you on the next one