LLM – how are large language models changing the future?

‍The development of large language models (LLM) has revolutionised the field of natural language processing (NLP) on an unprecedented scale, enabling machines to understand and generate human-like text. In an AI-driven era, it is crucial to understand the impact of LLM on future business strategies and solutions, redefining industries and creating new value for companies and their customers.

What is LLM?

A Large Language Model (LLM) is a type of advanced artificial intelligence (AI) model that has been fed massive amounts of complex data collected from different sources, including the Internet. The millions of gigabytes of datasets provide enough examples for LLMs to learn natural language, its semantics and grammar, process it (NLP), and then produce text and other content, such as programming code, translations, and various tasks.

Large language models (LLMs) form the foundation of generative AI (GenAI) tools for text and natural language processing. When creating GenAI tools, large language models are trained and fine-tuned for specific tasks using appropriate questions and commands. Tools developed this way allow, among other things, to easily generate contextually consistent answers, summarise documents, or create entirely new content.

How does LLM work?

For LLM to understand how characters, words, and sentences work together, we use deep learning techniques. Deep learning is the use of artificial neural networks composed of a large number of so-called hidden layers.

Neural networks (both deep and shallow) work similarly to the human brain, which comprises neurons that connect and send signals to each other. LLMs are based on a special type of neural network – transformers. They use a mechanism called self-attention, which allows them to understand context of specific words – essential in the case of natural language, which is highly contextual. It enables LLMs to interpret language even when unclear, poorly defined, or previously unexposed.

Beside the ability to analyse and recognise the text structure (syntax), large language models “understand”, at some level, the meaning of processed content (semantics). LLMs can associate words with their meaning, “seeing” them grouped in the same way millions of times in the training set. Understanding and interpreting natural language is the key advantage of large language models, making them so widely useful.

The capabilities of LLM models are as vast and diverse as the datasets they are trained on

Examples of LLM tasks

Depending on the scope of training, a given LLM model can be used for the following tasks:

• text generation,
• translation,
• content summarisation,
• article editing,
• spell and grammar checking,
• creating dialogue systems,
• user sentiment analysis,
• customer experience personalisation,
• code writing and proofreading,
• customer service automation.

LLM in chatbots

One of the most common uses of LLM is a chatbot. The most famous conversational AI chatbots include ChatGPT with the latest GPT-4o and GPT-o1 models. AI chatbots based on LLM also include Gemini (Google), Meta AI (Meta), and Claude (Anthropic).

Examples of using LLM-based chatbots:

• Banking and finance – Bank of America is one of the leaders in using LLM and AI technology in banking and financial services. Its virtual financial assistant, “Erica”, is an advanced chatbot model and the first so widely available in financial management and customer service, reaching 2 million daily interactions.
• E-commerce – Amazon has developed a generative business chatbot, “Q”, directly competing with giants like Microsoft and Google. By integrating with its own data and company systems, it offers personalised shopping experiences.
• Media and publishing – The Washington Post uses the chatbot “Heliograf” to automatically generate news, short reports and articles based on sports, election or financial data, as well as update articles based on new data in real-time.

LLM Applications in Practice

Top LLM use cases and applications in 2024 to streamline business operations and automate daily tasks:

1. Content Creation

LLMs are a game-changer for content-driven industries. Businesses and content creators use their models to streamline content production, saving time and effort in the writing process.

LLMs allow marketers, journalists, and writers to generate rough drafts, suggest edits, or create complete articles, reports, and even more significant pieces of writing.

👉🏻 Example: Claude, Anthropic's AI assistant. He's effective in sophisticated dialogues, creative content creation, complex reasoning, and step-by-step instructions; Grammarly is a spell-checker and plagiarism detection tool. It uses LLM to understand context and make suggestions to improve the style and clarity of your text. Its tone detector analyses text for sentiment and emotion, such as off-putting, interesting, formal, or optimistic.

2. Language translation and localisation

LLMs enable real-time localisation translations, making websites, apps, and digital content universally accessible and enabling businesses to effectively communicate with international customers.

Trained on large collections of bilingual or multilingual texts, LLM applications provide contextual translations across multiple languages, allowing them to understand different languages' nuances, idioms, and grammatical structures. They can preserve the intent and style of the original text, which is important for literary translation, business communication, and legal documents.

Localisation helps tailor content culturally and contextually for different audiences. They consider local customs, measurements, date formats, and cultural references. This capability is especially important in the marketing and entertainment industries, where engagement is highly dependent on cultural nuances.

👉🏻 Example: NLLB-200 is an LLM model from Meta AI. It translates into 200 languages, including some not previously supported by existing translation tools, and includes support for 55 African languages. Other: Falcon LLM

3. Customer Service and Virtual Assistants

LLMs are transforming customer service by providing automated and personalised services because they can understand context and analyse sentiment. This technology allows companies to offer 24/7 support, improving the user experience and bringing benefits to the company without a huge investment.

Virtual assistants are the foundation of customer service. Working on LLM models, they understand and process natural language. When a user asks a question or issues a command, the LLM interprets the intent and context of the request and then generates a response.

LLMs allow users to quickly retrieve information from various sources. In addition to customer service, assistants can set alarms, remind about appointments, send messages, order items from stores, and provide weather forecasts and traffic updates. They are a valuable tool and source of information for people with disabilities or those who need hands-free support.

👉🏻 Example: Alexa – Amazon's voice-controlled virtual assistant, based on a cloud service. It can operate through voice interaction, e.g., playing music, setting alarms, or providing real-time information. It can also control smart home devices. Other: Siri

4. Sentiment analysis and market research

LLM applications can classify  text into categories, e.g. positive, negative or neutral. In customer feedback analysis, LLMs help identify sentiments, patterns and attitudes towards products or services, providing insight into their behaviour and preferences. Such tools can analyse customer opinions and reviews, thus predicting market trends and their evolution and generating summary reports.

Obtaining insights into customer satisfaction allows companies to adapt and develop products and marketing strategies. LLMs are also used to analyse reviews and mentions on social media to gain insight into public opinion and trends.

LLMs can also conduct extensive market research around specific products/services, track competitors, provide strategic data on positioning and innovation and other useful business information.

👉🏻 Example: Brandwatch is a digital consumer research platform. It uses LLM to analyse online conversations and provide information for market research. It provides access to a vast collection of online consumer discussions, including SM, blogs, forums and news sites, which allows for detailed sentiment analysis, trend spotting and brand perception. Other: Talkwalker

5. Code Development

LLMs can help developers write, analyse, review, and debug code. These models can understand and generate code snippets, suggest completions, and even write entire functions based on short descriptions.

In addition, LLMs can translate code between different programming languages, making it easier for developers to work with unfamiliar syntax or port projects to a new language.

👉🏻 Example: StarCoder is an open-source LLM trained on a large dataset from GitHub covering multiple programming languages. It is used to automatically complete code, modify code, and provide natural language explanations. Others: DeepCode, GitHub Copilot

6. Education and Training

LLMs can be used for personalised education and training. They can be adapted to the individual student's style and learning pace, offering practice questions, customised explanations, and feedback.

The model can generate reading materials and provide real-time language translation. Using LLMs helps create textbooks and interactive online courses, democratising education worldwide.

👉🏻 Example: Duolingo is a personalised language learning tool. Using the power of GPT-4, it offers features like "Explain My Answer" (which helps you understand why an answer was correct or incorrect) and "Role Play" (practice conversations with virtual characters). Others: Course Hero, MNIST-1D

Security and Ethical Challenges for LLM

The development of LLM has impacted lives and industries, showing the potential to drive growth; it also poses challenges in the areas of data privacy and security. Concerns include:

• vulnerability to manipulation – a properly constructed prompt can provide certain types of answers that are dangerous or unethical;
• being “hallucinated” – models create false information when they are unable to provide an accurate answer;
• duplicating errors and stereotypes – models replicate errors present in the training data, which can lead to results that are discriminatory and offensive;
• exploiting confidential data – once disclosed, it can be used to further train models.

As can be seen, LLMs are not designed to be safe vaults. Disinformation and misuse of generated content, therefore, raise ethical questions. Finding a balance between technological progress and responsible use of LLMs is important.

The most important challenges include:

1. Ethics of companies that create and implement LLMs and ensure that they are trained on unbiased, representative data sets;
2. Data protection from misuse requires rigorous data management and ethical AI frameworks.

Information provided by LLMs is only as reliable as the data on which they are trained

LLM implementation vs. cost and energy efficiency

The implementation of LLMs, although revolutionising industries and everyday life, is associated with significant expenses. This is due to the fact that training LLMs requires high computing power, including expensive hardware (GPU, TPU), significant cloud infrastructure, and energy, which generates high operational costs.

The high energy consumption of data centres also raises concerns about the environmental impact, as the carbon footprint of these energy-intensive systems is large. In addition, training professionals specialised in this field is expensive.

There are several solutions for companies that want to implement LLM while avoiding the costs and being environmentally friendly, e.g.:

• implementing on existing infrastructure, which reduces the use of specialised and expensive tools;
• adopting open-source versions (GPT-Neo or smaller BERT versions) – this avoids licensing costs and training models from scratch;
• training smaller models, i.e. imitating the behaviour of larger ones, resulting in lower operating costs while maintaining a similar level of performance;
• tuning – training and adapting existing models for specific applications rather than training them from scratch.

Summary

LLMs create a future full of prospects for companies, acting as a driver of innovation, efficiency, and competitive advantage. As GenAI technology develops, its role will expand beyond text generation and sentiment analysis. LLMs will be used in more and more business applications. Companies should integrate LLMs into their business solutions, if they want to adapt to this revolution.

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