Large Language Models Explained
Large language models (LLMs) are deep learning algorithms that can recognize, summarize, translate, predict, and generate content using very large datasets.
What are Large Language Models?
Large language models largely represent a class of deep learning architectures called transformer networks. A transformer model is a neural network that learns context and meaning by tracking relationships in sequential data, like the words in this sentence.
A transformer is made up of multiple transformer blocks, also known as layers. For example, a transformer has self-attention layers, feed-forward layers, and normalization layers, all working together to decipher input to predict streams of output at inference. The layers can be stacked to make deeper transformers and powerful language models. Transformers were first introduced by Google in the 2017 paper “Attention Is All You Need.”
Figure 1. How transformer models work.
There are two key innovations that make transformers particularly adept for large language models: positional encodings and self-attention.
Positional encoding embeds the order of which the input occurs within a given sequence. Essentially, instead of feeding words within a sentence sequentially into the neural network, thanks to positional encoding, the words can be fed in non-sequentially.
Self-attention assigns a weight to each part of the input data while processing it. This weight signifies the importance of that input in context to the rest of the input. In other words, models no longer have to dedicate the same attention to all inputs and can focus on the parts of the input that actually matter. This representation of what parts of the input the neural network needs to pay attention to is learnt over time as the model sifts and analyzes mountains of data.
These two techniques in conjunction allow for analyzing the subtle ways and contexts in which distinct elements influence and relate to each other over long distances, non-sequentially.
The ability to process data non-sequentially enables the decomposition of the complex problem into multiple, smaller, simultaneous computations. Naturally, GPUs are well suited to solve these types of problems in parallel, allowing for large-scale processing of large-scale unlabelled datasets and enormous transformer networks.
Why are Large Language Models Important?
Historically, AI models had been focused on perception and understanding.
However, large language models, which are trained on internet-scale datasets with hundreds of billions of parameters, have now unlocked an AI model’s ability to generate human-like content.
Models can read, write, code, draw, and create in a credible fashion and augment human creativity and improve productivity across industries to solve the world’s toughest problems.
The applications for these LLMs span across a plethora of use cases. For example, an AI system can learn the language of protein sequences to provide viable compounds that will help scientists develop groundbreaking, life-saving vaccines.
Or computers can help humans do what they do best—be creative, communicate, and create. A writer suffering from writer’s block can use a large language model to help spark their creativity.
Or a software programmer can be more productive, leveraging LLMs to generate code based on natural language descriptions.
What are Large Language Model examples?
Advancements across the entire compute stack have allowed for the development of increasingly sophisticated LLMs. As LLMs have grown in size, so have their capabilities. Broadly, LLM use cases for text-based content can be divided up in the following manner:
Generation (e.g., story writing, marketing content creation)
Summarization (e.g., legal paraphrasing, meeting notes summarization)
Translation (e.g., between languages, text-to-code)
Classification (e.g., toxicity classification, sentiment analysis)
Chatbot (e.g., open-domain Q+A, virtual assistants)
Enterprises across the world are starting to leverage LLMs to unlock new possibilities:
- Medical researchers train large language models in healthcare on a corpus of data from textbooks, research papers and patient electronic health records for tasks like protein structure prediction that can uncover patterns in disease and predict outcomes.
- Retailers can leverage LLMs to provide stellar customer experiences to customers through dynamic chatbots.
- Developers can leverage LLMs to write software and teach robots how to do physical tasks.
- Financial advisors can use LLMs to summarize earnings calls and create transcripts of important meetings.
- Marketers can train an LLM to organize customer feedback and requests into clusters or segment products into categories based on product descriptions.
Large language models are still in their early days, and their promise is enormous; a single model with zero-shot learning capabilities can solve nearly every imaginable problem by understanding and generating human-like thoughts instantaneously. The use cases span across every company, every business transaction, and every industry, allowing for immense value-creation opportunities.
How Do Large Language Models Work?
Large language models are trained using unsupervised learning. With unsupervised learning, models can find previously unknown patterns in data using unlabelled datasets. This also eliminates the need for extensive data labeling, which is one of the biggest challenges in building AI models.
Thanks to the extensive training process that LLMs undergo, the models don’t need to be trained for any specific task and can instead serve multiple use cases. These types of models are known as foundation models.
The ability for the foundation model to generate text for a wide variety of purposes without much instruction or training is called zero-shot learning. Different variations of this capability include one-shot or few-shot learning, wherein the foundation model is fed one or a few examples illustrating how a task can be accomplished to understand and better perform on select use cases.
Despite the tremendous capabilities of zero-shot learning with large language models, developers and enterprises have an innate desire to tame these systems to behave in their desired manner. To deploy these large language models for specific use cases, the models can be customized using several techniques to achieve higher accuracy. Some techniques include prompt tuning, fine-tuning, and adapters.
Figure 2. Image shows the structure of encoder-decoder language models.
There are several classes of large language models that are suited for different types of use cases:
- Encoder only: These models are typically suited for tasks that can understand language, such as classification and sentiment analysis. Examples of encoder-only models include BERT (Bidirectional Encoder Representations from Transformers).
- Decoder only: This class of models is extremely good at generating language and content. Some use cases include story writing and blog generation. Examples of decoder-only architectures include GPT-3 (Generative Pretrained Transformer 3).
- Encoder-decoder: These models combine the encoder and decoder components of the transformer architecture to both understand and generate content. Some use cases where this architecture shines include translation and summarization. Examples of encoder-decoder architectures include T5 (Text-to-Text Transformer).
What are the Challenges of Large Language Models?
The significant capital investment, large datasets, technical expertise, and large-scale compute infrastructure necessary to develop and maintain large language models have been a barrier to entry for most enterprises.
Figure 3. Compute required for training transformer models.
- Compute-, cost-, and time-intensive workload: Significant capital investment, technical expertise, and large-scale compute infrastructure are necessary to maintain and develop LLMs. Training an LLM requires thousands of GPUs and weeks to months of dedicated training time. Some estimates indicate that a single training run for a GPT-3 model with 175 billion parameters, trained on 300 billion tokens, may cost over $12 million dollars in just compute.
- Scale of data required: As mentioned, training a large model requires a significant amount of data. Many companies struggle to get access to large enough datasets to train their large language models. This issue is compounded for use cases that require private—such as financial or health—data. In fact, it’s possible that the data required to train the model doesn’t even exist.
- Technical expertise: Due to their scale, training and deploying large language models are very difficult and require a strong understanding of deep learning workflows, transformers, and distributed software and hardware, as well as the ability to manage thousands of GPUs simultaneously.
Using open models for transparency, control and customization
Open large language models in the NVIDIA Nemotron family are developed through staged pretraining and post-training processes that can include synthetic data generation, supervised fine-tuning, and reinforcement learning to improve performance on reasoning and agentic tasks. NVIDIA releases Nemotron models with open weights, training data or datasets where redistribution rights allow, and training recipes.
This gives enterprises greater visibility into model development and enables them to fine-tune models on proprietary data and deploy supported models on infrastructure they control, subject to the applicable model licenses and hardware requirements.