LLM Use Cases in Business

As technology advances, businesses are constantly looking for innovative solutions to maintain a competitive edge. In the first half of 2023, the investment into Artificial Intelligence (AI) startups was over $40 billion. Moreover, this spending is expected to double by 2026.

Large Language Models (LLM) are just one example of how companies utilize innovations to create more sophisticated software for understanding and generation of human languages and code. OpenAI's GPT-x, Google's LaMDA, and Amazon’s Titan redefined the success of modern businesses. In this article, we will explore LLM use cases by referring to our expertise.

What Are LLMs?

Large Language Models (LLMs) are cutting-edge Natural Language Processing (NLP) algorithms trained on large datasets. They can understand written content, create human-like texts, and perform diverse language tasks. They take action via special plugins, which make them perfect for optimizing business processes, improving customer experiences, and completing valuable insights from unstructured data.

LLMs offer several benefits over classic natural language processing models. They can create high-quality content that is hard to distinguish from what a human could produce, which makes them suitable for multiple applications. At the same time, LLMs require large amounts of training data and technological power, and they can be difficult to adjust to specific applications.

Examples of LLMs

Examples of Large Language Models can be found across industries. The key models include:

Pathways Language Model (PaLM)

Google’s language generation model features over 500 billion parameters, including arithmetic reasoning, code generation, joke explanation, etc. Google's Pathways AI architecture allows training a single model to perform millions of tasks, unlike the standard AI model.

Bidirectional Encoder Representations from Transformers (BERT)

The transformer-based language system, developed by Google AI, can comprehend natural language and answer questions. It is used on a variety of language tasks, such as writing content about different topics and differentiating words with multiple meanings.

XLNet

The autoregressive transformer leverages autoregressive language modeling and autoencoding while avoiding possible limitations. It checks the pattern of encoded tokens and then predicts tokens in random rather than sequential order.

Generative Pre-Trained Transformer (GPT)

OpenAI’s language model answers questions, translates languages, generates content, and gives human-like responses based on over 1 trillion parameters. It can be fine-tuned to perform simple and complex tasks downstream.

How LLM Applications Can Be Used in Business?

Once the LLM model has been prepared, it can be used to generate new content based on the set parameters. This feature is adjustable to different aspects of work.

Customer service

LLMs are perfect for businesses that want to automate responses and make smart chatbots handle customer queries and concerns with human-like proficiency. This can improve customer interaction and help human agents proceed with more complex tasks.

Marketing and content creation

LLMs can generate creative content and suggest potential solutions to business problems. Companies can leverage these models to develop novel products, services, or strategies. Personalized advertising and SEO optimization can be used for marketing purposes to reach a competitive advantage in the market.

Data analysis and reporting

LLMs can analyze large amounts of unstructured data to extract actionable insights and establish automated reporting. These AI systems can help automate various tasks—such as document overview and content generation—to boost productivity and save time.

Language translation and localization

Making translations across languages or from text to code is a common feature for LLMs trained in multiple languages. The model can be adapted for various text generation tasks to overcome language barriers in global business.

Recruitment and HR

LLMs can make a big difference in recruiters' workflows, whether it comes to resume screening, automated candidate sourcing, and initial communication. Recruiters can make an input, such as a specialist with a certain skill or in a certain location, and have the system automatically transform it into a query.

Advanced Applications in Business

Building advanced LLM applications helps companies transform their operations and drive growth. Modern tech solutions can deliver good results in different spheres.

• Decision-making and strategy. LLMs can analyze data to discover customer needs and competitor offerings. When strategy teams assess opportunities and risks, this market intelligence helps them get a good understanding. With an LLM, strategists can create strategic plans for effective work.

• Predictive analytics. LLMs can be used for building predictive systems that rely on historical data and contextual information. They connect raw data and feature a single interface to predict market trends.

• Personalization in retail and eCommerce. Online shopping platforms implement LLMs to increase their recommendation engines. By analyzing individual user behavior, preferences, and purchase habits, they can predict products that consumers are more likely to buy and adjust recommendations to customer experiences accordingly.

• Financial services. LLMs support financial advisors by delivering real-time information and generating more personalized pieces of advice for individual clients. They can proceed with risk assessment, fraud detection, and personalized financial advice. This boosts the overall customer experience and satisfaction.

Large Language Model Use Cases

Companies can use LLM software for various business use cases to enhance efficiency and streamline processes. Let's explore some of these below.

Microsoft uses Turing-NLG

Microsoft optimizes its systems' ability to detect and extract relevant entities from different text data sources through Turing-NLG. This allows Microsoft to enhance language understanding capabilities, produce more reliable and context-aware information, and optimize applications across multiple domains, such as natural language processing, search engines, and data retrieval systems.

Facebook uses M2M-100

Facebook implements the M2M-100 (Many-to-Many) model for machine translation and language localization to facilitate smooth communication without linguistic barriers. This way, Facebook and Messenger expand their reach, support cross-cultural interactions, and help people from different linguistic backgrounds interact effectively.

IBM uses Watson NLU

IBM does sentiment analysis and opinion mining through the Watson NLU (Natural Language Understanding) model. The service on the IBM Cloud is used to collect significant information from customer feedback, social media posts, and other sources to understand the sentiment and make better strategic decisions. IBM managed to gain more profound insights from textual data and take appropriate actions based on the collected information.

The Future of LLMs in Business

Businesses that embrace the LLMs carefully and ethically will influence the landscape of the future. They will reduce costs by automating the average processes, enhance innovation by generating new ideas, treat customers with responsive interfaces, and expand their operations based on data. The key trends include:

Domain-specific versions

Most current models were adjusted to large volumes of public internet data. When used for specific purposes, such as in the commercial fields, they can reach their limits and sometimes make mistakes. Many companies are working to create models based on the industry-specific data. The domain-specific LLMs will be better suited to tailored tasks in the business segment.

Self-critiquing versions

What if LLMs could check each other’s performance? In the next few iterations, LLMs will gain a fundamental capability to do statistical reflection. This means that companies will be able to use an LLM to examine whether the output of another generative model is accurate or not. This should help manage LLMs more effectively and reduce imperfections and safety/ethical concerns.

Modular models

Existing LLMs can’t accomplish complex tasks that humans find ease. Modular models are the next step in the technology development. They connect sub-models to perform a single task. How many bridges are in the “Are you a human” authentications? Only humans can complete this task by following the steps: identify, count, and sum. Modular models are going to stick to the same logic.

How to Get Started with LLMs

Now that you are familiar with Large Language Model use cases, you might consider integrating one of such AI systems into your business. Their potential covers different domains, making them invaluable tools for productivity and innovation growth. Feel free to follow our step-by-step instructions on how to seamlessly incorporate an LLM into your workflow.

Determine your use case

Your company must first specify the use case to understand the requirements and adjust parameters for optimal outcomes. Some standard applications of LLMs include machine translation, chatbot implementation, natural language inference, computational linguistics, etc.

Select the right model

The variety of LLMs is constantly growing. Whether it comes to GPT or BERT, they all stand out with their unique strengths and weaknesses. Once you pick the one that meets your business needs, make sure to obtain a license if it’s not available as open-source.

Pre-process your data

To effectively utilize the LLM, you must first make necessary data preparations. This means eliminating unnecessary information, fixing errors, and converting the data into a proper format. Such meticulous steps need to be taken to shape the LLM’s input quality and future performance.

Fine-tune the model

The fine­-tuning process optimizes the model's parameters specifically for your use case. While it might take a lot of time, it is essential in achieving optimal results. It may require experimentation with different settings and on various datasets to find the optimal configuration.

Implement the model

Now it’s time to integrate the LLM into your business software or set it up as an independent service that your systems can query. Make sure the model is compatible with your infrastructure and can deal with the current workload.

Monitor and update the model

After successful implementation, the LLM’s performance needs to be monitored and updated for optimal performance. Furthermore, adjusting the model's parameters may be required as your requirements evolve.

Final Thoughts

Now it’s time to reveal the power of LLM software and take your business data to new heights. We will be glad to consult you on your project and lay the groundwork. Requestum specialists, having extensive AI competence, will help you successfully navigate the development and deployment of LLMs. Contact us and let’s transform your business with generative AI models!