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Analysis of Disinformation and Fake News Detection Using Fine-Tuned Large Language Model
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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.
Forward citations
Cited by 2 Pith papers
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Sword and Shield: Uses and Strategies of LLMs in Navigating Disinformation
In a 25-participant Werewolf-style game, all roles used an LLM chatbot strategically, as a sword for disinformation and a shield against it.
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Multilevel Analysis of Cryptocurrency News using RAG Approach with Fine-Tuned Mistral Large Language Model
A fine-tuned Mistral 7B model produces graph and text summaries, sentiment scores, and stacked meta-summaries of crypto news, but the paper reports no quantitative evaluation.
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