A large language model (LLM) is a type of artificial intelligence model that utilizes machine learning techniques to understand and generate human language. LLMs can be incredibly valuable for companies and organizations looking to automate and enhance various aspects of communication and data processing.
LLMs use neural network-based models and often employ natural language processing (NLP) techniques to process and calculate their output. NLP is a field of artificial intelligence (AI) that focuses on enabling computers to understand, interpret, and generate text, which in turn allows LLMs to perform tasks such as text analysis, sentiment analysis, language translation, and speech recognition.
LLMs form an understanding of language using a method referred to as unsupervised learning. This process involves providing a machine learning model with data sets–hundreds of billions of words and phrases–to study and learn by example. This unsupervised learning phase of pretraining is a fundamental step in the development of LLMs like GPT-3 (Generative Pre-Trained Transformer) and BERT (Bidirectional Encoder Representations from Transformers).
In other words, even without explicit human instructions, the computer is able to draw information from the data, create connections, and “learn” about language. As the model learns about the patterns from which the words are strung together, it can make predictions about how sentences should be structured, based on probability. The end result is a model that is able to capture intricate relationships between words and sentences.
LMMs require lots of resources
Because they are constantly calculating probabilities to find connections, LLMs require significant computational resources. One of the resources they draw computing power from are graphics processing units (GPUs). A GPU is a specialized piece of hardware designed to handle complex parallel processing tasks, making it perfect for ML and deep learning models that require lots of calculations, like an LLM.
LLMs and transformers
GPUs are also instrumental in accelerating the training and operation of transformers–a type of software architecture specifically designed for NLP tasks that most LLMs implement. Transformers are fundamental building blocks for popular LLM foundation models such as ChatGPT and BERT.
A transformer architecture enhances the capability of a machine learning model by efficiently capturing contextual relationships and dependencies between elements in a sequence of data, such as words in a sentence. It achieves this by employing self-attention mechanisms–also known as parameters–that enable the model to weigh the importance of different elements in the sequence, improving its understanding and performance. Parameters define boundaries, and boundaries are critical for making sense of the enormous amount of data that deep learning algorithms must process.
Transformer architecture involves millions or billions of parameters, which enable it to capture intricate language patterns and nuances. In fact, the term “large” in “large language model” refers to the extensive number of parameters necessary to operate an LLM.
LLMs and deep learning
The transformers and parameters that help guide the process of unsupervised learning with an LLM are part of a more broad structure referred to as deep learning. Deep learning is an artificial intelligence technique that teaches computers to process data using an algorithm inspired by the human brain. Also known as deep neural learning or deep neural networking, deep learning techniques allow computers to learn through observation, imitating the way humans gain knowledge.
The human brain contains many interconnected neurons, which act as information messengers when the brain is processing information (or data). These neurons use electrical impulses and chemical signals to communicate with one another and transmit information between different areas of the brain.
Artificial neural networks (ANNs)–the underlying architecture behind deep learning–are based on this biological phenomenon but formed by artificial neurons that are made from software modules called nodes. These nodes use mathematical calculations (instead of chemical signals as in the brain) to communicate and transmit information within the model.
Modern LLMs can understand and utilize language in a way that has been historically unfathomable to expect from a personal computer. These machine learning models can generate text, summarize content, translate, rewrite, classify, categorize, analyze, and more. All of these abilities provide humans with a powerful toolset to augment our creativity and improve productivity to solve difficult problems.
Some of the most common uses for LLMs in a business setting may include:
Automation and efficiency
LLMs can help supplement or entirely take on the role of language-related tasks such as customer support, data analysis, and content generation. This automation can reduce operational costs while freeing up human resources for more strategic tasks.
LLMs can quickly scan large volumes of text data, enabling businesses to better understand market trends and customer feedback by scraping sources like social media, reviews, and research papers, which can in turn help inform business decisions.
Creating a better customer experience
LLMs help businesses deliver highly personalized content to their customers, driving engagement and improving the user experience. This may look like implementing a chatbot to provide round-the-clock customer support, tailoring marketing messages to specific user personas, or facilitating language translation and cross-cultural communication.
Challenges and limitations for LLMs
While there are many potential advantages to using an LLM in a business setting, there are also potential limitations to consider:
LLMs require significant resources to develop, train, and deploy. This is why many LLMs are built from foundation models, which are pretrained with NLP abilities and provide a baseline understanding of language from which more complex LLMs can be built on top of.
- Privacy and security
LLMs require access to a lot of information, and sometimes that includes customer information or proprietary business data. This is something to be especially cautious about if the model is deployed or accessed by third-party providers.
- Accuracy and bias
If a deep learning model is trained on data that is statistically biased, or doesn’t provide an accurate representation of the population, the output can be flawed. Unfortunately, existing human bias is often transferred to artificial intelligence, thus creating risk for discriminatory algorithms and bias outputs. As organizations continue to leverage AI for improved productivity and performance, it’s critical that strategies are put in place to minimize bias. This begins with inclusive design processes and a more thoughtful consideration of representative diversity within the collected data.
Transformative AI/ML use cases are occurring across healthcare, financial services, telecommunications, automotive, and other industries. Our open source platforms and robust partner ecosystem offer complete solutions for creating, deploying, and managing ML and deep learning models for AI-powered intelligent applications.
A leader among hybrid and multicloud container development platforms, Red Hat® OpenShift® enables collaboration between data scientists and software developers. It accelerates the rollout of intelligent applications across hybrid cloud environments, from the datacenter to the network edge to multiple clouds.
With Red Hat OpenShift AI, organizations can access the resources to rapidly develop, train, test, and deploy containerized machine learning models without having to design and deploy Kubernetes infrastructure. Users can more reliably scale to train foundation models using OpenShift’s native GPU acceleration features on-premises or via a cloud service.
Red Hat Ansible® Lightspeed with IBM watsonx Code Assistant is a generative AI service that helps developers create Ansible content more efficiently. It reads plain English entered by a user, and then it interacts with IBM watsonx foundation models to generate code recommendations for automation tasks that are then used to create Ansible Playbooks. Deploy Ansible Lightspeed on Red Hat Openshift to make the hard tasks in Kubernetes easier through intelligent automation and orchestration.