The paper considers the possibility of fine-tuning Llama 2 large language model (LLM) for the disinformation analysis and fake news detection. For fine-tuning, the PEFT/LoRA based approach was used. In the study, the model was fine-tuned for the following tasks: analysing a text on revealing disinformation and propaganda narratives, fact checking, fake news detection, manipulation analytics, extracting named entities with their sentiments. The obtained results show that the fine-tuned Llama 2 model can perform a deep analysis of texts and reveal complex styles and narratives. Extracted sentiments for named entities can be considered as predictive features in supervised machine learning models.
@article{arxiv.2309.04704,
title = {Analysis of Disinformation and Fake News Detection Using Fine-Tuned Large Language Model},
author = {Bohdan M. Pavlyshenko},
journal= {arXiv preprint arXiv:2309.04704},
year = {2023}
}