This write-up aims to demystify the complex world of Large Language Models (LLMs), word vectors, and the role of word vectors in LLMs.
Large Language Model (LLM)
Large Language Models (LLM) fundamentally represent deep learning models equipped with the ability to understand the inherent structure and patterns present in a human language and are tailored not only to comprehend but also produce human language-like output, which encompasses not just text itself but also the nuances of style and tone typical of human communication.
What Exactly is LLM and how does it work?
At their core, LLMs are advanced algorithms that learn from an enormous amount of text data. Imagine feeding a computer equivalent to thousands of books, articles, and websites. This is no small feat; we're talking about training with datasets comprising a minimum of 1 billion parameters. This extensive training sets them apart, enabling these models to understand and generate language with proficiency and complexity far beyond simpler models. The size of the training dataset for an LLM encompasses a minimum of 1 billion parameters (variables used to train the model), as mentioned, where each parameter contributes to how the model understands and generates language.
The comprehensive training undertaken by LLMs equips them to predict what comes next in a sentence, understand complex queries, and even generate human-like text—helping them understand how words and sentences are structured, how ideas are expressed, and how to formulate responses. This extensive training of the model enables LLMs to anticipate the next word in a sentence, understand complex questions, and craft coherent, contextually relevant responses.
While the power of LLMs is immense, accessing this power is less direct than one might think. These models are typically embedded within user-friendly interfaces or platforms, such as ChatGPT or Kubiya.ai (overview at the end), making advanced machine learning accessible even to those without a background in AI.
Word Vectors and their role in LLM
Word vectors represent individual words in a numerical form that helps capture the word’s meanings. Every word is depicted as a vector in a multi-dimensional space, where each dimension uniquely attributes various characteristics of a word, such as its contextual usage and semantic attributes.
These vectors are constructed using word embedding algorithms (like Word2Vec, GloVe, and FastText), where a model is trained extensively on a vast corpus of data, during which it learns to assign a distinct vector to each word.
How do word vectors work?
With the help of these word vectors, Large Language Models (LLMs) have the capability to employ these vectors for executing a wide range of linguistic tasks like sentiment analysis, question-answering, code generation or completion.
Consider a prompt given to an LLM-based tool (like ChatGPT) saying, “Write a program to add two numbers in Python.”
The first step is breaking a sentence, for example, “Write a program to add two numbers in Python” into smaller pieces, known as tokens.
The procedure continues as follows:
Tokenization: The above sentence is broken down into individual words (or tokens). For instance, the above prompt would be split into tokens like ["write", "program", "to", "add", "two", "numbers", "in", "Python"].
Converting to Word Vectors: Each word or token is then converted into a word vector. Think of these vectors as a unique fingerprint for each word created through the model's training.
Contextualization: Next, the model processes these vectors using a complex architecture (known as a transformer) that understands context. Each layer updates the vector, taking the vectors of other words in the sentence into account. This is where context starts getting incorporated. For instance, the word “add” is influenced by its neighboring words like "program" and "two numbers," helping the model understand that the word "add" refers to a mathematical operation in programming, not adding ingredients in a recipe.
Attention mechanism: This mechanism allows the models to focus on important parts of data. In this case, words like "add," "numbers," and "Python" might be given more weightage because they are key to understanding the context. This is the part where the model gets smart about what to focus on.
Context-Aware Vectors: By the end of each processing layer, every word vector has been updated to reflect not just the meaning of the original word but its meaning in the specific context of the prompt.
Response generation: The model uses context-aware vectors to generate the response, which predicts the next token (or word) based on the current context of all other vectors.
Task Completion: The model continues the process of predicting the successive words until it fully addresses the prompt.
Conclusion
In summary, the combination of Large Language Models (LLMs) and word vectors is pivotal in AI's advancement. LLMs leverage their extensive training on diverse datasets to interpret and generate human-like language, while word vectors provide a nuanced understanding of individual words. Together, they enable AI to not only comprehend text but also respond with contextually relevant language. This synergy is essential in driving AI towards greater intuitiveness and sophistication.
In the upcoming write-up, we will be delving deeper into Generative AI, the role of AI in DevOps, and how ChatOps platforms like Kubiya.ai come super handy to manage your cloud infrastructure for DevOps teams where GenAI tools like ChatGpt fail to cater to such scenarios.
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