Welcome to this series on Generative AI and Large Language Models (LLMs). This series focuses on building a foundational understanding of the technical aspects behind Generative AI and LLMs. While it might not delve deeply into professional-level intricacies, it aims to provide technical awareness for individuals, students, application developers, and Dev/AI/ML/CloudOps engineers. This series will equip you with the knowledge needed to develop, deploy, or contribute to Generative AI applications.
Each blog in this series is designed to be concise, offering a theoretical overview and working awareness. For those interested in a deeper dive, I encourage further exploration based on the provided foundations.
LLMs : the basics
What is a Language Model?
Language Models (LMs) are probabilistic models of text. They predict the probability of a sequence of words and can generate new sequences based on learned patterns. LMs are foundational in natural language processing (NLP) tasks because they help machines understand and generate human language by estimating the likelihood of different word combinations.
What are Large Language Models?
Large Language Models (LLMs) are a subset of language models characterized by their vast number of parameters. These parameters allow LLMs to capture more complex patterns and nuances in language. There's no strict threshold for what constitutes "large," but LLMs often have hundreds of millions to billions of parameters, making them capable of performing a wide range of sophisticated language tasks. Examples of LLMs include BERT, Cohere, GPT-3, GPT-3.5, GPT-4o, Gemini, Gemma, Falcon, Lambda, Llama.
LLMs : the architectures
The Transformer architecture is a foundational framework in modern natural language processing (NLP). It is composed of encoders and decoders, which can be used independently or together to handle various NLP tasks.
The Transformer architecture, introduced in the paper "Attention is All You Need" by Vaswani et al. in 2017, revolutionized the field of natural language processing (NLP). Unlike previous architectures that relied heavily on recurrence or convolution, Transformers use self-attention mechanisms to process sequences of words in parallel, leading to significant improvements in efficiency and performance.
The architecture can be divided into three main configurations: encoder-only, decoder-only, and encoder-decoder.
Encoders
Encoders are responsible for processing input text and converting it into a meaningful vector representation (embedding). They capture the context and relationships within the input sequence.
Key Components:-
Self-Attention Mechanism: Allows the model to focus on different parts of the input sequence when encoding each word.
Feed-Forward Networks: Apply transformations to the embeddings to capture more complex features.
Layer Normalization and Residual Connections: Improve training stability and model performance.
Usage:: Encoders are typically used for tasks that require understanding and analyzing text, such as text classification, sentiment analysis, and extractive question answering.
Example Model: BERT (Bidirectional Encoder Representations from Transformers) uses multiple encoder layers to capture the context of words bidirectionally.
Decoders
Decoders take a sequence of words or embeddings and generate the next word in the sequence. Decoders work by taking a sequence of words (or embeddings) and predicting the next word in the sequence. This process continues iteratively to generate full sentences or paragraphs. Decoders are crucial for tasks that require text output, such as chat responses or story generation.
Key Components:-
Masked Self-Attention Mechanism: Ensures that the prediction for each word depends only on the previously generated words, not future words.
Feed-Forward Networks: Similar to those in the encoder, used to transform embeddings.
Cross-Attention Mechanism: When used in an encoder-decoder framework, decoders include a cross-attention layer that focuses on the encoder's output.
Usage: Decoders are used for tasks that require generating text, such as chatbots, creative writing, and forecasting.
Example Model: GPT-3 (Generative Pre-trained Transformer 3) uses multiple decoder layers to generate human-like text based on input prompts.
Encoder-Decoder Architecture
The encoder-decoder architecture combines both encoders and decoders. The encoder processes the input sequence to generate embeddings, which are then used by the decoder to produce an output sequence.
Key Components:-
Encoder: Processes and encodes the input sequence.
Decoder: Generates the output sequence based on the encoder's embeddings and previously generated words.
Cross-Attention Mechanism: In the decoder, this mechanism attends to the encoder's output to incorporate contextual information.
Usage: The encoder-decoder architecture is used for tasks that require both understanding and generating text, such as translation, abstractive summarization, and abstractive question answering.
Example Model: T5 (Text-To-Text Transfer Transformer) uses an encoder-decoder structure to perform a variety of text-to-text tasks.
Tasks and Architectures
Encoders and decoders are applied differently depending on the task:
Embeddings: Used to convert text into numerical vectors that capture semantic meaning.
Text Generation: Involves producing coherent and contextually appropriate text.
Process of Text Generation
Text generation in LLMs involves the following steps:
Input Encoding: The input text is converted into embeddings using an encoder.
Contextual Understanding: The model captures the context and semantics of the input text.
Sequence Generation: A decoder takes the contextual embeddings and generates the next word or sequence of words, predicting each subsequent word based on previously generated ones.
Why Do We Need Embeddings?
Embeddings, or vector representations, convert words and phrases into dense vectors that capture semantic meaning. They are essential because:
Numerical Representation: Embeddings provide a way to represent textual data numerically, which is necessary for machine learning models.
Semantic Relationships: They capture the semantic relationships between words, allowing models to understand context and meaning.
Efficient Computation: Vector representations enable efficient computation and comparison, which is critical for tasks like semantic search and recommendation systems.
Role of Vector Databases
Vector databases store and manage embeddings, enabling efficient retrieval and comparison of text data. They are crucial for applications like:
Semantic Search: Matching user queries with relevant documents based on vector similarities.
Recommendation Systems: Finding similar items or content based on their embeddings.
Examples:-
Pinecone: A managed database service designed for storing and querying large-scale vector data.
FAISS (Facebook AI Similarity Search): A library for efficient similarity search and clustering of dense vectors.
Task Classification
Here's a classification of various NLP tasks and the corresponding architecture needed:
Explanation:
Embedding Text: Requires an encoder to transform text into vector embeddings.
Abstractive QA (Question Answering): Needs an encoder-decoder to understand the context and generate a concise answer.
Extractive QA: Uses an encoder to identify and extract relevant text from the input.
Chat: Utilizes a decoder to generate conversational responses.
Forecasting: Uses a decoder to predict future sequences based on patterns.
Translation: Requires an encoder-decoder to translate text from one language to another.
Creative Writing: Uses a decoder for generating creative and coherent text.
Summarization: Utilizes an encoder-decoder to condense and summarize long texts.
Code Generation: Uses a decoder to generate and understand code snippets based on context. Uses a decoder to generate and understand code snippets based on context. Models like GitHub Copilot and OpenAI's Codex are trained on large datasets of code and are capable of assisting developers by suggesting code completions, generating code from comments, and understanding context to improve programming productivity.
Conclusion:
In this series on Generative AI and Large Language Models (LLMs), we have explored the fundamental concepts and architectures that underpin modern NLP technologies. By understanding the basics of language models and their large-scale counterparts, we gain insight into how these models can predict and generate human-like text. We delved into the versatile Transformer architecture, which leverages self-attention mechanisms to efficiently process and generate text, highlighting the distinct roles of encoders, decoders, and encoder-decoder structures.
We examined the significance of embeddings and vector representations in transforming text into numerical data that models can understand and manipulate. Vector databases play a crucial role in storing these embeddings, enabling efficient retrieval and application in tasks such as semantic search and recommendation systems.
Furthermore, we classified various NLP tasks based on the required architecture—whether it involves encoding, decoding, or a combination of both. From text embedding and question answering to chatbots and code generation, we have seen how specific models and configurations are tailored to address these challenges.
In the upcoming blogs of this series, I'll cover the training and prompting aspects of LLMs.
Thanks. Stay tuned, aware & ahead!
Top comments (0)