Harnessing Large Language Models: Fine-tuned BERT for Detecting Charismatic Leadership Tactics in Natural Language
<|reference_start|>Harnessing Large Language Models: Fine-tuned BERT for Detecting Charismatic Leadership Tactics in Natural Language: This work investigates the identification of Charismatic Leadership Tactics (CLTs) in natural language using a fine-tuned Bidirectional Encoder Representations from Transformers (BERT) model. Based on an own extensive corpus of CLTs generated and curated for this task, our methodology entails training a machine learning model that is capable of accurately identifying the presence of these tactics in natural language. A performance evaluation is conducted to assess the effectiveness of our model in detecting CLTs. We find that the total accuracy over the detection of all CLTs is 98.96\% The results of this study have significant implications for research in psychology and management, offering potential methods to simplify the currently elaborate assessment of charisma in texts.<|reference_end|>
@article{saeid2024harnessing,
    title={Harnessing Large Language Models: Fine-tuned BERT for Detecting
  Charismatic Leadership Tactics in Natural Language},
    author={Yasser Saeid, Felix Neub"urger, Stefanie Kr"ugl, Helena H"uster,
  Thomas Kopinski, Ralf Lanwehr},
    journal={arXiv preprint arXiv:2409.18984},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.18984},
    primaryClass={cs.CL cs.AI cs.LG}
}