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.

翻译：本文探讨了微调Llama 2大语言模型（LLM）以进行虚假信息分析和假新闻检测的可行性。在微调过程中，采用了基于PEFT/LoRA的方法。研究中，模型针对以下任务进行了微调：揭露虚假信息和宣传叙事的文本分析、事实核查、假新闻检测、操纵分析以及命名实体情感提取。实验结果表明，微调后的Llama 2模型能够对文本进行深入分析，并揭示复杂的风格和叙事模式。提取的命名实体情感可作为监督式机器学习模型中的预测特征。