Large language models (LLMs) such as GPT-4 have become indispensable tools for many industries. Their ability to understand and generate text in a human way opens up endless possibilities for optimizing and transforming business processes. Discover our practical guide to the top LLM use cases. But first, Let's define what the big language models are.
A large language model (LLM) is a deep learning algorithm that can perform a variety of natural language processing (NLP) tasks. Using transformer models and trained on large data sets, they can recognize, translate, predict, or generate text and other content.
These models are also called neural networks (NN), inspired by how the human brain works with their networks of nodes organized in layers. In addition to teaching human languages to artificial intelligence (AI) applications, LLMs can be trained for a variety of tasks, such as understanding protein structures or writing software code.
Like the human brain, these models require prior training and refinement in order to be able to classify or generate text, answer questions, or summarize documents. Their abilities to solve a variety of problems find applications in areas such as healthcare, finance, and entertainment, supporting a variety of NLP applications, including translation, chatbots, and AI assistants.
LLMs have a large number of parameters, comparable to the memories of human memory during learning, constituting their knowledge bank.
What is the difference between big language models and generative AI?
Generative AI is a generic term for artificial intelligence models that can create content. It can produce text, code, images, videos, and music. Examples of generative AI include Midjourney, DALL-E, and ChatGPT.
Large Language Models (LLMs) are a sub-category of generative AI. They are specifically trained on textual data to produce textual content. ChatGPT is a well-known example of a great language model. In summary, all major language models are part of generative AI.
Let's discover the different use cases:
1. Information retrieval
Large language models play a crucial role in retrieving information. Search engines like Bing or Google use LLMs to analyze billions of web pages, extract relevant information, and provide accurate, conversational answers to user queries. These models are able to understand the context of the question, summarize the information found, and present a concise and relevant answer. Some examples of practical applications would be web search to help find information quickly and efficiently, or the knowledge base, to help businesses organize and easily access internal information.
2. Sentiment analysis
Sentiment analysis is an essential application of LLMs in natural language processing. Businesses use these models to analyze thousands of textual data from social networks, customer reviews, and online forums. LLMs can identify emotions expressed in text, detect trends, and help businesses understand the public perception of their products and services. This allows businesses to respond quickly to feedback and improve their offerings.
3. Text generation
LLMs are at the heart of generative AI, capable of producing text from simple instructions. For example, models like ChatGPT can write articles, poems, scripts, or even entire books based on the (good) instructions given (also called “prompt”). They can adopt different writing styles, imitate famous authors, or create original content, making it easy to create content. Other examples can be given in the case of AutoLex, generative AI allows professionals to generate or modify clauses safely.
4. Code generation
In addition to text generation, LLMs are also capable of generating code. Developers can use these models to write scripts, fix bugs, and generate code snippets based on given specifications. LLMs understand programming patterns and can suggest optimal solutions, which speeds up software development and improves the productivity of technical teams.
5. Chatbots and conversational AI
Chatbots and conversational AI systems benefit greatly from the capabilities of LLMs. These models allow chatbots to understand and interpret user questions, provide appropriate answers, and conduct natural conversations. They are widely used in customer service to manage customer requests, resolve common issues, and offer 24/7 support, improving the customer experience and reducing operational costs.
6. Sentence completion and text summaries
LLMs are also used to complete incomplete sentences, answer specific questions, and summarize long texts. This capability is especially useful in document processing applications, where users can quickly get summaries of reports, research, or other large documents. LLMs can extract key information and present it concisely, making it easy to understand complex content quickly.
Applications in various sectors...
- Technology
In the tech sector, LLMs are used to improve search engines, help developers write code, and offer personalized recommendations. They also make it possible to develop advanced virtual assistants and facilitate the automation of technical processes.
- Health and science
LLMs are able to understand complex data about proteins, molecules, DNA, and RNA, making them valuable in medical research. They can help develop vaccines, identify treatments for diseases, and improve preventive medicine. Medical chatbots based on LLMs can also conduct patient admissions and provide basic diagnoses, improving healthcare efficiency.
- Customer service
Across industries, LLMs are used to improve customer service through chatbots and conversational AI systems. They help manage customer requests, provide instant responses, and resolve common issues, providing a smooth and satisfying customer experience.
- Marketing
Marketing teams use LLMs to analyze consumer feelings, generate campaign ideas, and create content quickly. Models can write synopses, taglines, and product descriptions, helping marketers develop effective strategies and engage their audience.
- The legal field
In the legal field, LLMs assist lawyers and jurists by analyzing large sets of textual data, researching legal precedents, and generating accurate legal jargon. They can help with contract writing and summarizing, text generation and analysis, case preparation, and legal research, thus improving the efficiency and accuracy of legal work.
- Banking services
Banking institutions use LLMs to detect fraud by analyzing suspicious transactions and identifying anomalous patterns. They can also assist customers through chatbots, provide personalized financial advice, and automate complex banking processes, making financial services more secure and efficient.
Large language models offer a wide and varied range of applications, transforming numerous industries by improving efficiency, accuracy, and user experience. Their ability to understand and generate text in a contextual manner makes them indispensable tools for businesses seeking to innovate and remain competitive in a world increasingly focused on data and artificial intelligence.
