## 4 Methods That Use Large Language Models
### LLM-Based Fake News Detection
#### 4.3.1 Fake Text Classification Using LLMs  
In recent years, the fine-tuning of pre-trained models on fake news datasets has emerged as a pivotal approach in enhancing the accuracy of fake news detection. This method leverages the robust capabilities of pre-trained models and adapts them specifically for identifying misinformation, leading to significant improvements in performance metrics.  
[PERSON] et al. fine-tuned the ChatGPT and Google Gemini models using the LIAR benchmark dataset [69]. The training set from the LIAR dataset was employed, utilizing high-performance computing resources, including GPUs, to meet the computationaldemands and ensure efficient processing. During the fine-tuning process, hyperparameters such as learning rate, batch size, and the number of training epochs were systematically adjusted to achieve optimal performance. The test set of the LIAR dataset was used for evaluating the fine-tuned models, comparing their predictions to true labels to calculate performance metrics like accuracy, precision, recall, and F1 score. Google Gemini exhibited slightly better results compared to ChatCPT, as evidenced by higher accuracy, F1 score, and superior performance on the AUC-ROC curve.  
[PERSON] et al. explored deep learning techniques for detecting Arabic misinformation by leveraging the contextual features of news article content [70]. This approach employed a combination of BiLSTM and the attention mechanism inherent in the transformer architecture. A pre-trained AraBERT model was used to extract features from Arabic text, generating contextual embeddings. These embeddings were then fed into a BiLSTM layer, and the outputs were processed through an attention layer to create a context vector. This context vector was further refined through dense layers and a dropout layer to prevent overfitting, enabling the model to classify each article as fake or real.  
[PERSON] et al. applied fine-tuning of the BERT model to create a health misinformation detection classifier [71]. This classifier processes input text through a transformer architecture comprising multiple layers of bidirectional self-attention mechanisms. The model was trained, validated, and evaluated using three publicly available datasets: CMU-MiSCOV19, CoAID (COVID-19 Health Misinformation Dataset), and FakeHealth. The training data were labeled, and the pre-trained weights were adjusted to optimize performance for the task of distinguishing between real and fake health news.  
[PERSON] et al. proposed a novel framework named FakeNewsGPT4, which augments Large Vision-Language Models (LVLMs) with forgery-specific knowledge for manipulation reasoning while inheriting extensive world knowledge as a complement [72]. This framework requires two types of forgery-specific knowledge: semantic correlations between visual and textual data and artifact traces indicating manipulations. It employs a multi-level cross-modal reasoning module and a dual-branch fine-grained verification module to extract this knowledge. The generated knowledge is then converted into embeddings compatible with LVLMs using candidate answer heuristics and soft prompts to enhance input informativeness. Experiments have demonstrated that FakeNewsGPT4 outperforms previous methods in cross-domain fake news detection tasks.