How do LLMs work? It’s a question many tech enthusiasts and AI fans have begun to ask in the last year, particularly as new AI innovations continue to emerge throughout the landscape. LLMs form the foundation of some of the most exciting AI apps ever produced, from ChatGPT to Google Bard.
They’re also the ecosystems that offer developers the tools to create solutions capable of producing unique content, automating complex processes, and even translating human language.
Since the start of 2023, we’ve seen an influx of tech giants and startups investing in the LLM landscape, from Microsoft and Google to Meta and Amazon.
So, what’s happening behind the hood of tools like ChatGPT? How do LLMs work, and how are they developed into robust solutions for businesses and consumers?
The Types of Large Language Models
Large Language Models, or LLMs, are a form of AI solution capable of mimicking human intelligence. They leverage statistical models to analyze vast amounts of data, learning patterns, and connections between phrases and words so that they can generate new content on demand.
Typically, these models are built with deep-learning techniques, using specific types of neural networks, such as the “transformer” architecture. However, there are various types of LLM on the market, all with unique functionality. For instance, standard options include:
- Zero-shot model: This large, generalized model is trained on a generic corpus of data and serves a variety of use cases without the need for additional training.
- Fine-tuned models: Additional training can lead to more domain-specific LLM models. For instance, the Open-AI Codex is a domain-specific LLM for programming.
- Language representation models: Language representation models are used for conversational purposes. For instance, the BERT solution is well-suited for NLP.
- Multimodal models: Initial LLMs were initially trained for text, but modern solutions like GPT-4, from OpenAI, can handle both text and images. Some answers also support audio.
How Do LLMs Work? Training Large Language Models
The answer to “How do LLMs work?” can vary depending on the LLM you’re examining. However, there are some standard components in the functionality of all LLMs. For instance, all LLMs require an extensive amount of training.
As “large” AI models, LLMs require massive volumes of data, although it’s unclear where this data comes from. Some companies behind LLMs don’t share much information. While others are more transparent. For instance, the research paper on the LaMDA model (powering Bard) says the data comes from public forums, tutorials, code documents, and Wikipedia.
One of the most recently developed open-source LLMs, Falcon 180B, was trained on 3.5 trillion data tokens, including web data from Amazon SageMaker and RefinedWeb.
Wherever this data comes from, it's processed through an advanced neural network, an AI engine consisting of various layers and nodes; these networks frequently adjust how they interpret and make sense of data based on multiple factors. Most LLMs use a specific form of neural network called a transformer.
Transformers in Large Language Models
The transformer neural network is particularly well-suited to training LLMs. They can read vast amounts of text, spot patterns in how words and phrases relate, and predict what kinds of comments should come next. In a way, LLMs are similar to “autofill” engines. They don’t know anything themselves, but they’re good at predicting the next step in a sequence.
One particularly crucial component of transformer networks is the self-attention mechanism, which essentially allows LLMs to train themselves. This ensures systems examine how words are related in a sentence or paragraph, not just look at isolated words.
Notably, there is some variation and randomness in the code used to build LLMs, which is why tools like ChatGPT and Bard won’t always generate the same response to the same question every time. It’s also why some answers won’t always be entirely accurate. LLMs don’t know exactly which responses are “accurate”; they simply look for what’s plausible based on the data they have.
A bot won’t always choose the word most likely to appear next in a sentence. They may choose the third or fourth most likely term or use a synonym. This leads to a greater diversity in responses, but when pushed too far, it can mean answers stop making sense. This is why LLMs need to learn and correct themselves constantly.
