Large Language Model: Innovation in text processing

How do large language models work? How do LLMs promote technological breakthroughs? How do they improve business processes? Is an LLM really artificial intelligence? Gain valuable insights into implementation strategies for effectively using the right language model in your company.

Large language models (LLMs) have the potential to fundamentally change corporate communication. These highly developed AI systems understand, process and generate natural language, offering innovative solutions to the challenges of modern communication. Companies are faced with the task of designing personalised and efficient interactions with their customers – a requirement that can be met through the use of LLMs.

In this article, you will learn how large language models work, promote technological breakthroughs and improve business processes. You will also gain valuable insights into implementation strategies for effectively deploying the right language model in your organisation.

Large language models are a special class of AI language models that are characterised by their enormous amounts of data and their ability to generate coherent, context-related text. In the context of Industry 4.0, where the digitalisation and networking of production processes are at the forefront, LLMs offer unique opportunities for optimising human-machine interaction and data analysis.

Unlike traditional language models, which are trained for specific tasks, LLMs are capable of mastering a wide range of linguistic challenges. By training on huge text corpora, they develop a deep understanding of the structure and semantics of human language. Another special feature of large language models is their versatility and adaptability. They solve problems such as text classification, question answering, document summarisation and text generation with precision.

Despite their differences, these types of LLM share fundamental similarities that explain how they work. In the next section, we will therefore take a closer look at the technological foundations and mechanisms behind the power of large language models.

Large language models are based on complex neural networks that are specially designed to handle demanding language tasks. An effective data strategy is crucial here, as it forms the basis for the quality and performance of the model. Thanks to deep learning techniques, these models can extract patterns and meanings from large amounts of text. This enables them to develop a deep understanding of the structure and semantics of human language.

At the heart of modern large language models is the transformer architecture. Unlike older approaches such as recurrent neural networks (RNNs) or long short-term memory (LSTM), transformers enable more efficient and powerful processing by processing the entire input sequence in parallel. The architecture consists of encoder and decoder components that are connected by self-attention mechanisms and feedforward networks. This structure allows dependencies between words to be captured independently of their position in the sentence, thus developing a deeper understanding of the context.

The functioning of large language models can be divided into four basic steps that work together to extract meaning and context from text data.

As mentioned earlier, large language models work based on the principle of self-supervised learning. With this approach, the model learns by independently recognising patterns and relationships in the training data without the need for explicit instructions or labelling. However, before self-supervised learning can be used, LLMs often go through a pre-training phase.

In this step, the model is trained on huge text corpora to learn basic language patterns and relationships. A common method for this is masked language modelling, in which the model learns to predict missing words in a sentence. Another technique is next sentence prediction, in which the model predicts whether two sentences follow each other or not. Through this pre-training, the LLM develops a broad understanding of language and can transfer this knowledge to various tasks.

After pre-training, the model can be adapted to specific tasks. The pre-trained model is used as a starting point and fine-tuned with additional data for the target task. This process is called fine-tuning and allows the model's general understanding of language to be transferred to specific use cases. During fine-tuning, the model parameters are adjusted using task-specific data so that the large language model learns to produce the desired results. This approach allows LLMs to be quickly and efficiently adapted to new tasks without requiring complete training from scratch.

The quality and quantity of training data are also crucial to the success of LLMs. Modern approaches such as data mesh help to provide the necessary data quality and quantity by integrating and managing data across domains. The more extensive and high-quality the data, the more powerful and versatile the models will be.

Imagine having a digital assistant that not only understands your language, but also independently writes high-quality texts, answers questions and even analyses images. With large language models, this is no longer a vision of the future, but already a reality.

The development of LLMs has led to a real hype in recent years. And for good reason: these models are capable of recognising complex linguistic relationships, drawing conclusions and even performing creative tasks. As a result, they are unlocking cross-industry potential and driving efficiency in various business areas.

The multimodal capabilities of large language models provide companies with a powerful foundation for developing intelligent applications and services. The following sections explain the various areas of application for LLMs and use concrete examples to illustrate how this technology helps companies optimise their processes and open up new opportunities.

The development of large language models is a highly dynamic field in which new models with improved performance features are regularly introduced. Most of these basic models originate in the United States. Since 2017, 73% of AI foundation models have been developed in the United States, which can be attributed to the intensive research and development activities of tech companies there.

The most powerful LLMs form the basis for a wide range of AI applications in various industries. They enable the efficient solution of complex tasks in areas such as customer service, data analysis, content generation and process automation. Especially in the context of AI in industry, these models offer great potential for optimising processes and increasing competitiveness.

The following table provides an overview of the five largest and most powerful language models currently shaping the market:

The performance of large language models is evaluated using demanding metrics such as perplexity, BLEU score or F1 score in various benchmarks. GPT-4 sets new standards in language processing. In comparison, European LLMs are characterised by other specific features.

While ChatGPT was developed as a product by a US company and trained on a broad, global data set, European LLMs often focus more on the linguistic and cultural diversity of Europe. These models place particular emphasis on compliance with European data protection standards and ethical guidelines, which makes them particularly attractive for companies and organisations within the EU. In the next section, we will therefore take a look at three promising European large language models.

A key feature of European large language models is their focus on the specific needs and requirements of the European market. This includes not only taking into account Europe's linguistic and cultural diversity, but also integrating advanced technologies to increase efficiency and sustainability.

The following three LLMs demonstrate the strength of European AI technology and contribute to the diversification and further development of the global AI landscape.

European large language models demonstrate clear strengths in terms of specialisation and ethical orientation. They are designed to comply with European data protection regulations and enable transparent, trustworthy AI use. Compared to closed systems such as Claude 2, European LLMs often pursue open-source approaches. This transparency promotes innovation and allows for critical review of the models in terms of bias and fairness. By disclosing the architecture and training data, researchers and developers can better understand how the models work and identify potential weaknesses.

Another focus of European LLMs is on adaptation to regional characteristics. While global models are often trained in English and neglect cultural nuances, European models take into account the linguistic and cultural diversity of the continent. By integrating multilingual training data and collaborating with local partners, these models can deliver more accurate and context-sensitive results.

In addition, European LLMs are increasingly relying on technologies such as federated learning and differential privacy to ensure the protection of sensitive data. Through decentralised training on distributed data sets, the models can learn without revealing personal information. These approaches are particularly relevant for applications in healthcare or the financial sector, where handling confidential data is a top priority.

Although GPT-4 is still leading the way in areas such as model size and application breadth, European models are catching up fast. They are increasingly establishing themselves as serious alternatives that address specific regional needs and strengthen European digital sovereignty.

To overcome these complex challenges, a holistic approach that harmoniously combines technical expertise and regulatory compliance is essential. MaibornWolff offers you this comprehensive approach with in-depth understanding and expertise in AI architectures, data management and system integration. We place particular emphasis on implementing powerful concepts that ensure both cybersecurity for your company and the integrity of large language models.

MaibornWolff relies on a systematic and iterative approach to successfully integrate large language models into corporate structures. This approach can be understood as a cyclical process in which each phase transitions into the next, enabling continuous improvement. The following core phases form the foundation of our implementation strategy:

Our service combines three core competencies: cloud architecture, digital design and data science. First, we implement a robust cloud infrastructure that communicates seamlessly with the large language model. Next, our digital designers develop intuitive user interfaces for optimal interaction. Finally, our data scientists train and optimise the language model on your domain-specific data. Through this interdisciplinary approach, we ensure that your company realises the full potential of LLMs while ensuring data protection, scalability and user-friendliness.

Research into large language models is opening up new avenues in the development of powerful multi-agent systems that solve complex problems through the collaboration of specialised LLMs. At the same time, the integration of causal understanding improves the reliability and interpretability of the models, while new architectural approaches increase resource efficiency. These advances significantly expand the application areas of LLMs – from science and healthcare to data analysis – and help address ethical and resource-related challenges.

With a well-thought-out approach and support from experienced experts, companies can efficiently implement large language models and secure competitive advantages. MaibornWolff supports you in both the initial prototyping and the optimisation of existing language models. Our goal is to enable you to integrate LLMs into your IT organisation efficiently and with low risk. Together, we create a solid foundation for continuous innovation and sustainable value creation in your company.