# Harnessing Large Language Models: Fine-tuned BERT for Detecting Charismatic Leadership Tactics in Natural Language

arXiv: https://arxiv.org/abs/2409.18984 · HTML: https://mlsanity.com/paper/2409.18984/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 24 references that are in the map.
- First posted on arXiv 16 Sep 2024.

## Builds on (most cited first, 4 of 4)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)

## Abstract (arXiv)

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.