LLMs 101: Large language models explained

The world of artificial intelligence (AI) is full of acronyms. NLP, ML, GPT, RLFH – you name it, there’s probably an acronym for it. One of the most common that you might come across is LLMs, shorthand for ‘large language models’. 

But what exactly are LLMs and how do they work? We’re here to give you the basics of LLMs and demystify one of the fundamental building blocks of AI.  

What are LLMs? 

LLMs are powerful AI systems that can read, understand and create human-like text. They are part of the field of AI called Natural Language Processing (NLP) and are used for tasks such as writing, translating, or answering questions, having learned from massive amounts of text data. 

Among other applications, LLMs are used in chatbots, for content creation, translation, summarization, and making various tasks more efficient. They can also assist in coding, research, idea generation, and creative projects.

Examples of LLMs include Open AI’s GPT-4, Meta’s LLaMa, Google’s Gemini, LLM360’s K2, and many more. These models are primarily in English, but recently MBZUAI has developed LLMs for the Arabic Language (Jais), and Hindi (Nanda). 

How do LLMs work? 

LLMs work by processing and generating text in a way that mimics human understanding. When you submit text to an LLM, it breaks the text into smaller units called ‘tokens’ – words or parts of words – to help analyze the language efficiently. The model then uses ‘transformer architecture’ to understand the relationship between these tokens in the context of the input text.

To help them do this, LLMs are trained on vast amounts of data. This training allows them to learn general language settings and patterns, so that they can then predict the next word (or token) in a sequence. This enables them to generate coherent and contextually appropriate text for various applications. 

What different types of LLMs are there? 

There are various types of LLMs, each designed for specific uses. General-purpose LLMs, like ChatGPT, PaLM and LLaMa, are used to handle a wide range of tasks, while task-specific models are fine-tuned for particular applications, such as translation or summarization. Multimodal models combine text with other data types, such as images or audio, enabling more diverse and advanced capabilities. 

There is also a distinction to be made between closed-source and open-source LLMs. Closed-source LLMs are developed, owned and controlled by specific organizations, who have restricted access to their products — usually to protect intellectual property and control their model’s usage. This approach, however, offers a lack of transparency that sometimes raises concerns about accountability and bias.  

Open-source LLMs, meanwhile, are models whose code, weights and sometimes data are publicly available for anyone to use and modify. This approach offers transparency, accessibility and collaboration opportunities, enabling researchers and developers to understand how the models work and build on them, improve them, or use them for different applications. 

How do you build and use an LLM?  

Building and using LLMs involves several key steps. First is data collection and preparation. This is the information the model will learn from. The next step is training, where the model is exposed to this data to learn language patterns and structures. Once trained, LLMs are fine-tuned for specific tasks or domains. This is done by tweaking them according to targeted datasets. The model is then deployed, meaning it is integrated into applications for real-world use. Finally, the model is monitored and updated to ensure it remains accurate and adapts to new information or changes in language. 

What are the key considerations when building and using an LLM? 

There are some important things to consider when building and using LLMs, especially from an ethical and societal perspective. Bias, fairness and misinformation are primary concerns, as LLMs can influence and direct people’s thoughts, opinions and actions. For this reason, transparency and accountability are important for LLMs, ensuring models’ decisions and reasoning can be understood and traced. There are also concerns over energy consumption, and efforts are underway to find more energy-efficient ways to build, train and use LLMs.

What does the future look like for LLMs?

In the years ahead, there are many possibilities and challenges for LLMs. Energy efficiency is high on this list, as the AI community searches for more sustainable approaches to LLMs. More specialized models are also likely to emerge, tailored to specific industries or tasks, including greater language –specificity. There will also be more integration for multimodal capabilities, improvements in reasoning capabilities, and a reduction in hallucinations (when a model generates text that is factually incorrect, misleading, or entirely fabricated). 

MBZUAI also aims to democratize the field with open-source LLMs that foster transparency, trust and collaborative research. This open-source approach will give researchers and developers greater access to models, training data, code, and training checkpoints, enabling greater innovation and make advanced technologies available to those who may not have the resources to develop proprietary models.  

Glossary of terms 

LLM (Large Language Model): A type of AI model that is trained on vast amounts of data to understand and generate human-like language. 

Open-source LLM: An LLM whose code, weights and sometimes data are publicly available for anyone to use and modify. 

Embeddings: Numeric representations of words or tokens that help models capture meaning. 

Fine-tuning: The process of adjusting a pre-trained LLM on a specific dataset to improve its performance on a particular task. 

GPTs: Short for ‘Generative Pre-trained Transformers’. A family of LLMs trained to generate coherent and contextually relevant text. 

Hallucinations: Instances where an LLM generates inaccurate or fabricated information, despite sounding plausible.

Inference: The process of using a trained model to make predictions or generate text based on input data. 

Prompt engineering: The practice of crafting specific input queries (known as prompts) to guide LLMs in generating desired outputs. 

Tokenization: The process of breaking text into smaller units (tokens), which can be more easily processed by the model. 

Transformers: A type of neural network architecture that uses self-attention to process and generate sequences of data, particularly text. 

Self-attention: A mechanism in transformers that helps the model decide the importance of different words in a sentence, regardless of their position.