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.

利用 PEFT/LoRA 的方法对 Llama 2 大型语言模型进行细调，以用于辨析虚假信息和检测假新闻。该研究中，模型被细调以完成以下任务：解析揭示虚假信息和宣传叙事的文本、事实核查、假新闻检测、操纵分析、提取带情感的命名实体。实验结果表明，细调后的 Llama 2 模型能够对文本进行深度分析，揭示复杂的风格和叙事。命名实体的情感提取可以作为监督机器学习模型中的预测特征。