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Large language models (LLMs) — machine learning models trained on massive amounts of text data that can comprehend and generate human language text.
Over the past few years, AI, and LLMs in particular, have become part of the routine of a considerable number of people. Nowadays, it's common to use virtual assistants like ChatGPT or Gemini 1.5 to answer questions, find useful information, or solve tasks.
The most recent data from March 2024 indicates that ChatGPT boasts approximately 180.5 million users. And the newest flagship model, GPT-4o, will definitely bring human interaction with AI to the next level since it offers advanced capabilities across text, voice, and vision.
The same is true for businesses—they are rapidly adopting LLMs to automate texts, improve customer experience, and gain new insights from data analysis.
In this article
LLM landscape overview
According to the Grand View Research, the global large language model market size was estimated at USD 4.35 billion in 2023 and is projected to grow at a compound annual growth rate (CAGR) of 35.9% from 2024 to 2030.
This market is only in its early stages but definitely is experiencing rapid development. And there are a couple of reasons for that.
From the business perspective, growth is predetermined by increasing demand for advanced NLP capabilities across different industries. Companies are seeking ways to increase the usage of LLMs to enhance customer service, data analysis, process automation, and so on.
As LLMs become more sophisticated and solve complex NLP challenges, their adoption will likely continue to expand, driving the growth of LLM creation.
From the technical perspective, transfer learning and self-supervised learning techniques allowed LLMs to use pre-trained knowledge and adapt to new requests more efficiently. Additionally, hardware improvements, especially in GPUs and TPUs, have sped up the training and processing of larger, more intricate models. Such technological advancements improved the performance of LLMs, making them an appealing tool for companies that could use them to boost the efficiency of their operations and gain an advantage in the market.
For now, the landscape of LLMs is constantly changing, so it's hard to compare LLMs and define any of the most popular models. However, some key players appear at the forefront.
GPT-3 (OpenAI)
Background: Developed by OpenAI, a non-profit research company with backing from Microsoft and others. GPT-3 was first announced in 2020 and quickly gained recognition for its impressive text generation capabilities.
Functionalities: GPT-3 excels at generating different creative text formats, including poems, code, scripts, musical pieces, emails, and letters. It can also translate languages, write different kinds of creative content, and answer questions in an informative way.
Gemini (Google)
Background: Developed by Google AI, Gemini is a powerful LLM powering feature within various Google apps like Search and Assistant. It's also available through an API for LLM developers to integrate into their own applications.
Functionalities: Gemini boasts a wide range of capabilities, including text generation, translation, question answering, and code completion. It's known for its adaptability and ability to handle a variety of tasks.
Claude 3 (Anthropic)
Background: Developed by Anthropic, a research company founded by former OpenAI researchers. Claude 3 prioritizes safety and alignment with human values in its design and training.
Functionalities: Claude 3 focuses on tasks requiring safety and factual accuracy, like question answering, code review, and generating different creative text formats while minimizing biases.
Llama 3 (Meta)
Background: Developed by Meta (formerly Facebook), Llama 3 is a relatively new LLM with a focus on multimodal capabilities, meaning it can handle various data formats like text and code.
Functionalities: Information about specific functionalities is limited, but Llama 3 is claimed to excel at tasks like question answering, text summarization, and code generation. It's also said to have strong performance on multilingual tasks.
Comparison of LLMs
Most LLMs are definitely not a one-size-fits-all solution. To choose the LLM that best suits the company's goals it is necessary to carefully consider specific business needs and priorities. Different LLMs excel in different areas, so it's necessary to conduct LLM models comparison before making up your mind.
For those interested in adopting an LLM in the future — make sure to define what kind of tasks your business needs help with. Once you know what you need, you can research LLMs that have a proven track record in those specific areas and do your own large language model comparison. Make sure to research such factors as accuracy, scalability, and ethical considerations.
This table provides a side-by-side LLM model comparison, as we discussed earlier. We've included key factors that decision-makers should consider to help you evaluate each LLM's strengths and weaknesses and select the best fit for their requirements.
Feature | GPT-3 (OpenAI) | Gemini (Google) | Claude 3 (Anthropic) | Llama 3 (Meta) |
Focus | Text generation | General purpose | Safety and alignment, Multimodal | General purpose |
Strengths | Impressive creative text formats | Powerful & adaptable, API access | Focus on safety, factual accuracy | Integrates with productivity tools |
Weaknesses | Limited API access, potentially high cost, factual accuracy concerns | Might require more development effort for specific tasks | Limited access (paid tiers), may not be strongest for all creative tasks | Multimodal and multilingual versions are still in development |
Performance | Excellent for creative text generation, good for some factual tasks | Strong overall performance across various NLP tasks | Focus on safety and factual accuracy can impact creative freedom | Performance details limited, but likely good across various tasks |
Accuracy | Good for creative text formats, factual accuracy can be a concern | Generally high accuracy across various tasks | Focuses on factual accuracy and minimizing bias | Good across various tasks, potentially strong on factual accuracy |
Scalability | Highly scalable architecture, but limited access can restrict scalability for some users | Scalable architecture with API access | Scalable architecture; access depends on the chosen tier | Have highly scalable architecture due to large context windows |
Ethical considerations | Limited focus on safety and bias, potential for misuse | Focus on responsible development by Google AI | Strong focus on safety, alignment, and minimizing bias | Limited information available |
Known applications | Microsoft products, Duolingo | Google Apps, Developer tools | Slack, Notion | Meta AI features, Chatbot |
Accessibility | Limited API access | Paid API access | Paid access (multiple tiers) | Limited information |
Cost | Potentially high (limited access) | Varies based on usage | Varies based on tier | Limited information |
Safety/alignment | No specific focus | Focus on responsible development | Strong focus on safety & alignment | No specific focus |
How are LLMs used in business?
Advancements in AI research and the expanding availability of data pushed forward the development of large language models in terms of number and capabilities. And the growth rate is staggering. New models are appearing at a rapid pace, constantly pushing the boundaries of what's possible.
Of course, progressive business owners and decision-makers are digging into this innovation to find opportunities that would scale and put their companies above competitors. The business world is embracing LLMs with open arms. But how exactly are they embracing them?
Let's see real-world examples of how well-known companies use it in their operations.
Amazon uses large language models to personalize product recommendations for its vast customer base.
PayPal employs LLMs to analyze transaction data and identify potential fraudulent activities.
This real-time analysis helps prevent fraudulent transactions and protects both PayPal and its customers from financial losses.
Netflix uses an LLM-powered recommendation engine that personalizes content suggestions for each user based on their viewing history and preferences.
Bank of America created an LLM-powered virtual assistant, Erica, that helps customers with basic banking questions, transferring funds, and even budgeting. Erica freed up human bankers to handle more complex customer needs, boosting the bank's overall productivity.
Pfizer utilizes LLMs to analyze vast amounts of scientific research data.
What are some of the benefits of using LLMs?
Of course, businesses don't implement LLMs in their processes straight out of curiosity (well, mostly). As we stated above, LLMs offer many benefits for companies, and that is what drives people to use them.
Here are some main advantages LLMs can provide:
Personalized approach
67% of consumers expect companies to offer personalized experiences, and LLMs can help businesses achieve this. By analyzing customer data, LLMs can offer customized support and content for each customer based on their request. This significantly enhances customer satisfaction and, therefore, loyalty.
Increased efficiency
LLMs can power chatbots that answer FAQs, troubleshoot common issues, and even direct customers to the right resources. According to CX Today, automating tasks with LLMs can reduce business inquiry resolution times by up to 50%.
Content creation at scale
Nowadays it's hard for businesses to constantly maintain a digital presence backed with high-quality content since content creation is a resource- and time-consuming process. But LLMs can come in hand and help businesses with different texts for the blog, social media, or product descriptions.
New insights
However, as AI, particularly LLMs, gains popularity, this issue becomes less significant. LLMs can analyze large datasets of text and code, which can help identify patterns and trends that humans might miss. This can lead to new insights and discoveries in various fields.
Improved teamwork
The benefits extend to employees as well. LLMs can personalize training programs, making corporate learning more efficient and effective. This leads to a more skilled and knowledgeable workforce, giving businesses a pool of highly professional employees.
Our experience in the integration of LLMs
Custom QA bot for customer questions regarding the company
Client challenge and our solution. Our client needed a system to efficiently handle a high volume of customer inquiries about their company, delivering timely and accurate responses since delays and inconsistencies were decreasing customer satisfaction and operational efficiency.
After a careful LLMs comparison, we offered a Conversational AI (CAI) powered by OpenAI's GPT-3.5 model and custom embeddings. This CAI would understand natural language questions and provide accurate, contextual responses. By automating responses to frequently asked questions, we aimed to reduce the workload on human support agents, improve response times, and ultimately boost customer satisfaction and efficiency.
Data preparation. Working closely with the client, we identified the most relevant information from their extensive company data for inclusion in the CAI. Through data processing, we cleaned, structured, and organized this data to ensure its effectiveness for our NLU model. This resulted in a refined dataset optimized for powering the CAI.
Development and testing. During development, we used GitLab to manage our version control securely. We focused on attentive testing, comparing expected responses to test questions against the CAI's actual responses. To ensure a user-friendly experience, we also implemented robust error handling.
Results and business benefits. The implemented CAI solution delivered significant results for the client. By automating responses to common inquiries, the CAI:
Reduced workload on human support agents. This freed up agents to focus on more complex customer issues, improving overall support efficiency.
Improved response times. Customers received answers to their questions faster, leading to a more positive customer experience.
Enhanced customer satisfaction. Faster and more consistent responses led to increased customer satisfaction with the company's support channels.
These improvements in efficiency and customer satisfaction translate to tangible business benefits for the client, including:
Reduced operational costs.
Increased customer retention.
Improved brand reputation.
By implementing our CAI solution, the client achieved their goal of providing efficient and accurate customer support, ultimately improving both customer satisfaction and their bottom line.
Conclusion
To sum up language comprehension, large language models are becoming increasingly powerful tools with a wide range of applications in different industries. They can be used for text generation, virtual assistance, answering questions, process automation, and so on. Further AI research and development will definitely help refine available models and create new ones.
The capabilities of LLMs will keep expanding, bringing many benefits for businesses which will be confident enough to use them. Let's embrace this opportunity to build a future where LLMs empower us to learn, create, and connect in ways never before imagined.
