Artificial intelligence (AI) and machine learning (ML)-enhanced Large Language Models (LLMs) performing Natural Language Processing (NLP) have the potential to revolutionize the way organizations work. These models are being used to answer questions, summarize text, perform business record management, structure data, generate text, and act as chatbots using text sentiment analysis to help deliver better customer experiences.

From Text to Speech: The Evolution of Large Language Models

LLMs learn from massive amounts of text data and create accurate models of language. Many models were trained on more than 1 TB of text data from sources including books (e.g., BooksCorpus), articles (e.g., RealNews, PubMed), and websites (e.g., Wikipedia, Common Crawl, OpenWebText). Some LLMs trained on publicly available datasets such as The Pile and ROOTS. Recent advancements in natural language processing have resulted in remarkable LLM outcomes, exemplified by GPT-4 (OpenAI), Megatron-Turing (NVIDIA and Microsoft), OPT-175B (Meta), and ChatGPT (OpenAI).

During LLM training, the goal of AI-ML enhanced models is to predict a word in a sequence of words. The LLM uses transformer Deep Learning (DL) functions to process all input data at once and assign different weights to different parts of the input data. Using the assigned weights, the model creates text based on word position in the training language sequence. LLMs use information and relationships embedded within their deep neural network (DNN) to provide responses to tasks and prompts.

While LLMs show great potential, there are concerns about possible drawbacks of large language models when using them to create factual statements. One major concern is the potential for models to perpetuate bias and discrimination present in the data used to train them.

Introducing ChatGPT

ChatGPT (OpenAI) is a popular LLM with users for its ability to write fluid human-like prose. ChatGPT is a refinement of the InstructGPT LLM model outputs while incorporating reinforcement learning from human feedback (RLHF). Microsoft Corporation has invested in OpenAI and is working to integrate ChatGPT capabilities into search engine functionality.”

How Businesses Can Benefit from ChatGPT and other LLMs

• Business Records Management: Identifying named entities (NER) within documents and aligning business records to a single entity, supplier, or customer to enhance business activities and minimize duplication of effort.
• Chatbot Virtual Assistants: Using Q&A capabilities of large language models, combined with their ability to create human-like text, helps chatbots augment customer service operations.
• Enhance AI / ML with Faster Labeling of Data: Structuring and labeling large volumes of previously unstructured text data to gain value from data removes the process of hand-labeling and organizing unstructured data. Newly structured data can be used in existing downstream ML models or deployed in new ML methodologies that benefit the business.
• Summarize data: Summarizing data such as generating a summary of meeting notes, emails, or a judicial review summary of massive legal data files saves time and provides business value.
• Improve Customer Satisfaction and NPS Scores: Extracting customer sentiment from text captured during customer service interactions and posts on social media platforms aids businesses in improving customer satisfaction (CSAT), net promoter score (NPS), and customer dissatisfaction to reduce churn and improve customer retention.

Dell HPC Solutions Provide Maximize AI Efficiency

Powerful LLMs can contain 100 billion model parameters which requires more than 800 GB of memory to activate. These models require access to high performance computing (HPC) infrastructure including low-latency networking, high-performance storage, and servers equipped with GPUs in a specially designed architecture to maximize efficiency.

Dell has a focus on HPC and AI with products that support AI, training models and inferencing. Tony Rea, National Strategist for HPC, Dell Technologies, states, “The Dell Technologies HPC & AI Innovation Lab is devoted to HPC and AI testing and validation. Dell’s experts take a workload and develop an optimized architecture. We pick certain vertical markets such as life sciences, genomics, AI or manufacturing and our team designs a scalable system that gives the customer the highest efficiency. So when they purchase a system from Dell to train AI models, these systems are optimal for the kind of workloads that are going to run on the system, lessening risk and providing the best return on investment.”

What’s Next for Large Language Models

According to Ben Fauber, Ph.D., Senior ML Research Scientist, Distinguished Member Technical Staff, Dell Technologies, “Large language models have the potential to revolutionize the way we interact with and process language. They have numerous capabilities and applications in language-based tasks across industries and verticals. As the technology continues to advance, it is crucial to develop responsible and ethical approaches to developing and deploying large language models to ensure their beneficial impact on society. Their continued development will undoubtedly lead to exciting new possibilities as their capabilities and applications expand in the future.”

Learn More

Download the latest whitepaper from Dell Technologies, “Unleashing the Power of Large Language Models like ChatGPT for Your Business.” Join the Dell HPC Community to engage in discussions about generative AI and other technologies impacting HPC.