WHat is a Large Language Model?

What is a large language model?

A large language model (LLM) is an AI-based language model that is trained on huge amounts of text in order to recognize linguistic patterns and generate new, meaningful texts. It analyzes and understands natural language and can independently write, summarize, translate or answer questions on this basis. The underlying transformer architecture enables the model to capture even complex relationships in the text context.

LLMs "learn" language by predicting the missing words from incomplete sentences. In this way, they develop a statistical understanding of language without any manually prepared or specially labeled training data. Newer models now go beyond pure text processing: they are able to argue logically, make strategic considerations or support the development of complex concepts - for example in planning, decision-making or idea generation in a business context.

Companies use LLMs to make knowledge from unstructured sources such as emails, PDF and Word files, contracts or invoices accessible. In this way, information can be analyzed in a targeted manner and made available across departments - a valuable building block in modern knowledge management.

Background and context

The idea of processing language by machine has been around for decades. However, the rapid rise of large language models only began with the Transformer architecture (2017). Models such as BERT, GPT-2, GPT-3 and most recently GPT-4 made it clear just how powerful AI can be when it comes to understanding text. The freely usable ChatGPT (end of 2022) made this technology accessible to a wide audience for the first time.

Today, companies such as OpenAI, Google and Meta are working on ever better models, supported by particularly powerful computing systems developed for AI applications. It is becoming increasingly easy for companies to get started: thanks to open interfaces and pre-trained models, they can now use LLMs effectively even without their own AI department, for example via partner solutions or embedded in existing software.

How do LLMs work?

Large Language Models use state-of-the-art machine learning methods to process and generate language. At their core are artificial neural networks, some of which have hundreds of billions of adjustable parameters. The way they work can be described in simple terms as follows:

• Transformer architecture: it makes it possible to analyze entire sentences simultaneously and thus recognize even complex relationships.
• Training on mass texts: LLMs learn language patterns by sifting through large volumes of text - e.g. books, websites or chats. From this, they develop a statistical understanding of language.
• Word-by-word prediction: During operation, they predict the next probable word based on a prompt and thus generate fluent texts.
• Adaptation through fine-tuning or prompts: Existing models can be further trained for specific tasks or specifically controlled, e.g. through clear instructions in the input text.
• Knowledge integration: Modern LLMs can be linked to in-house knowledge by incorporating structured or unstructured data sources. In this way, they provide more precise and context-related answers.

Application examples

INFORM integrates LLMs into its software solutions to enable users to interact easily and naturally, for example through voice-controlled digital assistants and co-pilots. In general, large language models are increasingly finding their way into many industries and are already being used in practice in numerous fields of application.

Einige Beispiele sind: 

1. Customer Service in Logistics

INFORM uses LLMs in vehicle logistics to automatically analyze and respond to incoming transport requests via email. The system understands even incomplete information such as identification numbers, fills in missing data, and suggests a response directly - efficiently, quickly, and reducing the workload for the team.

2. Software Development and Programming

LLM-based tools such as GitHub Copilot help developers to automate many routine manual tasks. For example, they automatically generate test cases to test functions in different ways (e.g. inputs, error scenarios or special edge cases), write simple code from short descriptions and make suggestions for solutions to typical problems such as data formats or interfaces. This saves time - and makes development processes much more efficient. As a result, the job profile is changing: developers are increasingly taking on the role of controllers who review, adapt and integrate AI-generated code.

3 Knowledge management and internal search systems
LLMs facilitate access to internal knowledge by providing employees with answers to questions such as "How do I request a travel expense report?" directly from guidelines or manuals, without the need for lengthy searches. The same principle can also be used externally: On our homepage, a chatbot helps website visitors to quickly find answers about INFORM without having to click through menus.

Advantages and disadvantages of Large Language Models

Advantages

A major advantage of large language models is their ability to make complex systems accessible using completely natural language. Instead of using complicated operating screens, users can simply ask questions - this makes applications more intuitive and reduces training costs. At the same time, recurring tasks can be automated, which saves time and increases efficiency. Their flexibility is also remarkable: LLMs can be used in a wide variety of areas - from customer service and internal knowledge management to text generation. They are also very scalable: once trained, a model can provide knowledge from thousands of documents on demand.

Disadvantages

At the same time, there are limitations that need to be taken into account. For example, LLMs do not always deliver reliable results - they can generate plausible but factually incorrect content ("hallucinate"). Their decision-making processes are also not always comprehensible, which can be problematic, particularly in sensitive areas. In addition, there are high demands on computing power and energy consumption, especially when training large models. Last but not least, data protection and the handling of sensitive information pose a challenge - especially when external cloud services are involved.

FAQ On Large Language Models

What distinguishes an LLM from conventional software?

In contrast to rule-based programs, LLMs learn from extensive text data and generate answers based on statistical probabilities. They are not hard-coded, but adaptive and context-sensitive.

Why can LLMs provide incorrect information?

LLMs can generate so-called "hallucinations" - plausible but factually incorrect statements. This is because their answers are based on probability calculations and the models do not have a true understanding of the world. 

How up-to-date is an LLM's knowledge?

The knowledge of an LLM corresponds to the status of its training data. Without a connection to current data sources, it can provide outdated information.

Can LLMs use confidential company data?

Not by default. In order to provide LLMs with company-specific knowledge, they must be specifically trained with the relevant data or connected via secure interfaces.

How can the quality of LLM output be improved?

The relevance and accuracy of the outputs can be increased through precisely formulated inputs (prompts) and, if necessary, fine-tuning with specific data.

What are the risks of using LLMs in the company?

In addition to the possibility of misinformation, there are risks relating to data protection, copyright and ethical issues. Careful implementation and monitoring are therefore essential.

How can LLMs be integrated into existing systems?

Many LLMs offer APIs for integration. There are also specialized platforms and tools that facilitate integration into company processes.

Conclusion

Large Language Models mark a milestone in AI - they enable intuitive interaction with machines in everyday language. They open up new opportunities for companies: from more efficient communication to automated analyses and assistance systems. However, their use requires a sense of responsibility in order to avoid risks such as misinformation. When used correctly, LLMs increase productivity and reduce the workload of employees.