Große Sprachmodelle – sogenannte Large Language Models (LLM) – arbeiten mit Millionen bis hin zu Milliarden Parametern. Sie wurden meist auf breiten Datenmengen vortrainiert und können dadurch komplexe Zusammenhänge erfassen sowie flexibel auf unterschiedliche Anforderungen reagieren.
LLMs analyze language statistically: by learning from large-scale text corpora which words typically occur together and in what contexts. This enables them not only to complete sentences but also to answer questions, summarize content, generate code, and more. The more data and computational power used in training, the more powerful the model becomes.
LLMs are used across many industries—in virtual assistants, customer service, automated text analysis, healthcare, software development, and education. Their versatility and quality are transforming workflows and enabling new forms of human–machine interaction.
Despite all the benefits, such as high accuracy in language processing and test generation, the ability to learn, flexible use in different industries and use cases and the potential for automation of recurring tasks, LLMs also bring challenges. They require enormous computing resources, which is associated with high costs and environmental impact. There is also a risk of misinformation, bias and potential misuse, for example to create deepfakes or disinformation. They should therefore be used in a carefully controlled manner – especially in sensitive areas.
Large Language Models are a milestone in AI research and have fundamentally changed the way we interact with technology. However, their continued development demands responsible usage, clear ethical guidelines, and a mindful approach to their societal impact.
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