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In the era of digitization and the growing flood of information, the automatic, role-specific identification of information is crucial. This research paper aims to investigate whether the adaptation of LLM is suitable for classifying information obtained from standards for corresponding role profiles. This research reveals that with systematic fine-tuning, prediction accuracy can be increased by almost 100%. The validation was carried out using a two-digit number of standards for three predefined roles and demonstrates the significant potential of LM for labelling content with regard to roles.

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artificial intelligence (AI), large language model (LLM), fine-tuning, role-specific information, knowledge management