Language models are advanced Artificial intelligence–based systems that are trained on large collections of texts to predict subsequent words, sentences, or entire texts based on a given context. Large language models (LLMs) are models with billions of parameters, such as GPT-3 or BERT, that offer extensive language understanding and generation capabilities. Small language models (SLMs) are less complex but more optimised for speed and computational efficiency. The models can be used in applications such as machine translation, chatbots, and voice assistants.
Language Models (Large Language Models – LLM, Small Language Models – SLM)
Type of technology
Description of the technology
Basic elements
- Tokenisation: Transforming text into smaller units, such as words or phrases.
- Embedding: Representation of words as numeric vectors, which enables models to understand the relationship between words.
- Hidden layers: Deep neural layers that process data to understand linguistic context.
- Model parameters: A set of weights and values that the model optimises during training.
- Cost function: A measure of error that is minimised so that the model can improve its predictions.
Industry usage
- Chatbots: Automatic generation of responses in customer service and interactions with users.
- Machine translation: Real-time translation of texts into various languages.
- Content creation: Generatiung product descriptions, articles, and even creative literary texts.
- Voice assistants: Understanding and generating responses from smart speakers, such as Alexa and Google Assistant.
- Sentiment analysis: Analysing customer feedback on social media and reviews.
Importance for the economy
Language models are crucial to the development of language processing–based technologies, such as chatbots, machine translation, and voice assistants. Companies use these models to automate processes, improve customer service, and analyse social media sentiment. As LLM technologies continue to evolve, their role in analysing data, creating content, and supporting customer interactions will grow, contributing to innovation across industries.
Related technologies
Cloud Computing
Tools that integrate language models into applications such as voice assistants and chatbots.
Mechanism of action
- Language models are based on analysing large textual data sets, identifying patterns and relationships between words and sentences. The input text is transformed into numerical vectors, which are processed by deep neural networks. Based on previously trained data, the models predict what word or phrase should appear next. In larger models (LLMs), the algorithms are more complex, which enables them to generate more sophisticated and contextual responses. Smaller models (SLMs) are faster but may offer less precise results.
Advantages
- Ability to understand context: LLMs can generate more accurate answers by understanding the broad context of the text.
- Communication automation: Language models make it possible to automatically respond to customer inquiries in natural language.
- Personalisation: They can tailor responses to individual user preferences.
- Content creation: Models are able to generate marketing texts, product descriptions, and even creative content.
- Broad application: From sentiment analysis to automated translations and recommendations.
Disadvantages
- Misinterpretations: Models can misunderstand the context and generate inadequate or misleading responses.
- Disinformation: LLMs can be used to create false information, including text deepfakes.
- Computational complexity: Training large language models requires huge computing resources, which is expensive.
- Ethics: There is a risk of misuse of models to generate harmful content.
- Dependence on data: Models can be prone to error if trained on inappropriate or distorted data.
Implementation of the technology
Required resources
- Large data sets: Texts in different languages to train LLMs and SLMs.
- Computing infrastructure: Computing power to train language models, including GPU servers and cloud computing.
- Software: Tools, such as TensorFlow, PyTorch, or Hugging Face, to create and train models.
- Team of specialists: Experts in NLP, machine learning, and data analysis.
- Resources for model validation: Textual language data to optimise model performance.
Required competences
- Machine learning: Knowledge of LLMs, such as GPT and BERT, and techniques for training them.
- Natural language processing (NLP): Ability to work with textual data and build models for language processing.
- Programming: Knowledge of tools for training NLP models, such as Python, TensorFlow, and PyTorch.
- Model optimisation: Ability to customise models for specific applications based on user needs.
- Data analysis: Ability to interpret results generated by language models.
Environmental aspects
- Energy consumption: Training large language models (LLMs) requires huge energy resources.
- Raw material consumption: The need for extensive IT infrastructure to support LLMs generates demand for rare earth metals and other raw materials.
- Recycling: Computing equipment upgrades and replacements generate electronic waste.
- Emissions of pollutants: The development of data centres to support model training can lead to CO2 emissions.
- Waste generated: Upgrading servers and computing equipment generates electronic waste.
Legal conditions
- Legislation governing the implementation of solutions, such as AI Act (example: regulations on accountability for generated content).
- Safety standards: Regulations for the protection of data processed by language models (example: ISO/IEC 27001 regarding information security).
- Intellectual property: Rules for protecting the content generated by language models and the intellectual property of the data used (example: copyright on the content generated).
- Data security: Regulations for the protection of personal data used to train language models (example: GDPR in the European Union).
- Export regulations: Regulations for the export of advanced natural language processing technologies (example: restrictions on exports to sanctioned countries).