In the world of software development, a paradigm shift is underway. Large language models (LLMs) are poised to transform the way we code, offering a glimpse into a future where machines augment human programmers and streamline the creation of software. But how do these intelligent systems achieve this remarkable feat? Let’s dive into the fascinating mechanisms behind LLMs’ code-writing process.
But before moving on to that we need to first understand how LLMs work and generate text so efficiently.
So how does a LLM work ?
At the core of large language models lies a powerful architecture called the transformer. But don’t get scared by this complex word, we will breakdown everything step by step.
Imagine the transformer as the brain of AI system. It’s like a super-smart person that excels in understanding and using language. Unlike its predecessors, the transformer can process words in a sentence simultaneously, making it highly efficient for language-related tasks.
The transformer’s potential comes from its ability to pay attention to different parts of a sentence. Think of it like highlighting words in a text to understand how they relate to each other. This process is called self-attention, and it allows the model to grasp the meaning of words in context.
For example, if we have the sentence “The cat sat on the ___,” the model can use self-attention to figure out that the missing word might be “mat.” It looks at the whole sentence, not just individual words, to make predictions. But how does it comes to the conclusion that “mat” is the correct word. So let’s talk about how these models learn !
Phase 1: Learning from Books (Pre-training)
Imagine the AI reading a massive collection of books, articles, and stories from the internet. This huge collection of text is what we call the “pre-training corpus.” It doesn’t have a teacher telling it what each word means. Instead, it learns on its own through a process called unsupervised learning. This means the model learns to predict the next word in a sentence based on what it has seen before. It’s like guessing the next word in a story without knowing the ending.
The brainpower behind large language models comes from their neural network architecture. This architecture, inspired by the human brain, helps the model understand the complex patterns and relationships between words. It’s like the student’s brain getting really good at predicting what happens next in a story by seeing a lots of examples. To give an idea a human brain has around 100 billion neurons while a model like GPT-3.5 has around 175 billion neurons (parameters). So now you can imagine how powerful a LLM is !
Now that the LLM has it’s knowledge base, it can predict next words based on the numerous examples in the data. And as it can predict individual words, we can loop the extended sequence back into the Large Language Model (LLM) to predict subsequent words, creating a continuous generation of text. In essence, by training the LLM, we’ve enabled it to generate text one word at a time.
It’s essential to note an interesting aspect of this process. We’re not bound to always predict the most probable word. Alternatively, we can choose to sample from, let’s say, the top three or five likely words at any given moment. This introduces an element of creativity to the LLM’s output. Some LLMs even offer a control over the level of determinism or creativity in the generated text. You must have noticed that when we ask the same question at different instances, the LLM gives different answers each time. Now you know the reason why !
Phase 2: Getting Specialized (Fine-tuning)
Now that AI has become quite the language expert, it’s time to give it some extra lessons for specific tasks. This phase is called fine-tuning, where the model hones its skills for particular jobs.
Now armed with general language knowledge, getting additional training for specific tasks. We provide smaller sets of data designed for those tasks, such as translating languages or summarizing text. It’s like attending special classes to become an expert in certain subjects.
Here’s where the magic of transfer learning happens. The knowledge gained from reading all those books(data on the internet) is transferred to the new tasks. It’s like a person using it’s general understanding of language to excel in special classes. This transfer of knowledge is what makes large language models so versatile.
During fine-tuning, some settings are adjusted to perform even better on specific tasks. These settings, called parameters, are like us fine-tuning our strategies for different challenges. It helps the model become more accurate and efficient in generating responses for specific applications.
Phase 3: Putting Knowledge to Use (Inference)
The final phase, inference, is when the model uses its knowledge to respond to prompts or questions.
What sets large language models apart is their ability to consider the whole context. Instead of just looking at individual words, the model looks at the entire sentence. It’s like understanding the full story before responding, capturing the subtleties and nuances of language like a real person.
When given a question or prompt, the model generates responses that sound remarkably human-like. It uses the patterns and relationships it learned during training to provide contextually relevant answers. It’s like having a conversation with a super-smart person, and they respond in a way that makes sense based on what they’ve learned.
Now that we have understood how a LLM generates text, let’s move on to our question “How LLM writes code”
So how does a LLM actually writes code ??
The process of generating code is very much similar to that of generating text.
First, LLMs for code are trained on massive amounts of code, often collected from open-source repositories like GitHub, stack overflow. code snippets from websites, and even software documentation. This exposure to diverse code examples helps them learn the patterns, structures, and syntax of different programming languages.
During training, the LLM builds complex internal representations of code, capturing its semantics (meaning), syntax (structure), and relationships between different code elements. These representations are stored in a vast network of interconnected neurons, enabling the LLM to understand and manipulate code concepts. This process is very similar to the process of predicting the next word based on learnings from data collected that we learned just above
When prompted with a code-related task, the LLM leverages its understanding of code to generate new code snippets or complete programs.
Here’s a simplified overview of the process:
- Understanding the Prompt: The LLM first analyzes the user’s input, breaking it down into components to understand the desired functionality, programming language, and any constraints.
- Retrieving Relevant Code Patterns: It searches its internal representations for matching code patterns and structures that align with the prompt’s requirements.
- Assembling Code Fragments: The LLM intelligently combines retrieved code fragments and adapts them to fit the specific context of the prompt.
- Generating Code: It outputs the generated code, often with multiple variations or suggestions to provide flexibility to the user.
So this is the whole process that a LLM has to through to generate a piece of code. Here are the top 5 code generation LLMs. You can run it on your local machine and play around with it to see which works for you best:-
Codellama – It is an LLM trained by Meta for generating and discussing code. It is built on top of Llama 2. It comes in a variety of sizes: 7B, 13B, and 34B, which makes it popular to use on local machines as well as with hosted providers. Currently, it is the most well-known open-source base model for coding.
Wizardcoder – It is an LLM built on top of Code Llama by the WizardLM team. The Evol-Instruct method is adapted for coding tasks to create a training dataset, which is used to fine-tune Code Llama.
Phind-CodeLlama – It is an LLM built on top of Code Llama by Phind. A proprietary dataset of ~80k high-quality programming problems and solutions was used to fine-tune Code Llama. That fine-tuned model was then further fine-tuned on 1.5B additional tokens.
Mistral-7b – It is a 7B parameter LLM trained by Mistal AI, a France based company . It is the most recently released model on this list, having dropped at the end of September 2023.
Starcoder – It is a 15B parameter LLM trained by BigCode, released in May 2023. It was trained on 80+ programming languages from The Stack (v1.2). It is the east powered model in this list.
To learn more about LLMs in detail we have attached some links below which can be very helpful for further research:-