Although artificial intelligence (AI) is a hot topic across numerous industries, its diverse applications are hardly new. Forward-thinking organizations have been leveraging AI tools for years, whether for financial modeling and forecasting or supply chain planning and optimization.
Now, with global AI spending expected to double by 2026, enterprises are readier than ever to embrace the power of sophisticated algorithms. Large language models (LLMs), in particular, are poised to usher in a wave of exciting and far-reaching capabilities.
What is a large language model?
A large language model is a type of artificial intelligence algorithm that’s trained to learn the patterns and structures of a given language. This not only allows it to understand and summarize information, but also generate and predict new content.
Like all language models, LLMs are first “trained” on a data set, allowing them to infer relationships and generate content based on that information. In simple terms, training is merely the process of teaching AI to perceive, interpret, and learn from data.
The term “large” is a reference to the number of parameters LLMs are trained on—the numerical values and variables they use to map relationships between words and phrases. Generally speaking, AI models that consume large data sets can significantly increase their functionality relative to others. In short, they have a much wider range of capabilities.
Compared to standard language models, LLMs process massive data sets and typically contain at least 1 billion parameters. However, several models are known to hold hundreds of billions of parameters, whereas OpenAI’s ChatGPT-4 is rumored to have over 1 trillion.
How do large language models work?
LLMs use deep learning—a subset of machine learning (ML)—to generate outputs based on patterns learned from the training data.
They work primarily on a specialized transformer-based architecture, which is a type of deep learning model that “transforms” text data into a numerical format. This is meant to make it easier for the algorithm to understand and work with the input data effectively.
Google first introduced the concept of transformers in its 2017 paper “Attention Is All You Need.” In essence, transformer models process text data through a neural network—an AI engine made up of multiple nodes and layers. This enables it to read vast amounts of text simultaneously, comprehend how words and phrases relate to each other, and then predict what should come next in the pattern.
In summary, the basic LLM process looks like this:
- Training. The algorithm processes a large volume of training data, which is normally unstructured and unlabeled.
- Fine-tuning. The model begins consuming more structured data, allowing it to work more accurately and effectively.
- Deep learning. Now, the LLM processes text data through a neural network, assigning a score to each item. This enables it to recognize the connection between words, thus determining their relationship.
- Application. Once training is complete, LLMs are ready for practical use, such as summarizing or translating text.
Large language models and natural language processing
Natural language processing (NLP) is a field of data science and artificial intelligence that focuses on creating and improving systems that understand the relationship between computers and human language. The ultimate goal for many NLP tasks is for the machine to interpret and generate human language in a way that’s meaningful and useful.
In general, language models are AI tools that enable exactly that. LLMs are a particularly foundational component of NLP, as they allow people to input queries in natural language to generate a human-like response.
3 large language model examples
As AI adoption continues to rise, a combination of open-source LLMs have gained popularity. Some of the most well-known models include:
OpenAI originally released its Generative Pre-Trained (GPT) model in 2020. Ever since, it’s garnered a reputation as perhaps the largest LLM available on the market. The latest iteration, ChatGPT-4, not only has the power to understand and generate text, but also processes images and video. That said, it still only produces answers in text format.
Among its many applications, its most relevant use cases include:
- Content creation
- Video marketing
- Web development
Google’s Pathways Language Model (PaLM) is another widely used LLM with a range of capabilities. With over 540 billion parameters, it’s among the most highly trained models on the market.
Google unveiled PaLM 2 in May 2023. This second generation is more heavily trained on multilingual text data, allowing it to understand, generate, and translate nuanced information—poems, idioms, riddles, and more—in over 100 languages. With enhanced logic and reasoning, it can use applications such as:
- Code generation
- Cybersecurity threat analysis
- Medical diagnostics and research
LLaMa—Large Language Model Meta AI—is Meta’s own foray into artificial intelligence. As of July 2023, LLaMa 2 is built on over 70 billion parameters and in partnership with Microsoft. It’s designed to be a highly versatile model suited for any number of data language processing tasks and everyday applications.
For example, some of its most relevant use cases include:
- Content summarization
- Marketing and advertising
- Customized learning experiences
Challenges and limitations of LLMs
As is the case with all AI models, LLMs aren’t without their fair share of obstacles. Although they're developing at a rapid pace, most systems are in their infancy—and there’s a good reason for that.
The process of creating and training any subset of AI—let alone a large language model—is extremely complex. It requires an enormous amount of high-quality data, not to mention the time and resources to train, fine-tune, and manage it after completion. Of course, development and operational costs may be prohibitive to some organizations.
Models themselves are also difficult to understand and manage. It’s not always clear why the LLM generates a certain result, and in some cases, data scientists have a hard time removing bias from the equation—especially if they’re using unlabeled data.
Large language model FAQs
AI is complicated, and LLMs are no different. Let’s review some frequently asked questions to better understand the basics:
What’s the difference between a large language model and generative AI?
LLMs are a subset of generative AI focused specifically on natural language understanding and generation. Generative AI encompasses a broader range of AI models and techniques used for generating various types of data, not limited to text.
What are the benefits of large language models?
LLMs have numerous benefits. For instance, users can tap into massive data sets and instantly receive answers to most queries, thus streamlining information retrieval.
Also, LLMs don’t need to be continuously refined or optimized like standard models. They require only a prompt to perform a given task. LLMs are also highly versatile and can be leveraged for any number of personal and professional functions.
What are some top use cases for LLMs?
Organizations are using LLMs to their advantage in various ways, including:
- Content ideation, creation, summarization, and analysis
- Sentiment analysis
- Conversational AI and chatbots
- Language translation
- Web development and code generation
- Information retrieval
- Fraud detection
Unlock the potential of LLMs
Large language models rely on efficient data management throughout the training and development process. With Teradata VantageCloud, organizations can maximize the effectiveness of their LLMs and reap the benefits of enterprise AI.
Combined with the powerful ClearScape Analytics™ engine, businesses can accelerate model training and deployment, making it easier for them to operationalize AI and stimulate long-term growth.
Connect with us to learn more about Teradata VantageCloud and how we can help your organization tap into the power of artificial intelligence.