A Better Large Language Model Using Lora for False News Recognition System

Abstract: Fake news detection is an important field of study in Natural Language Processing (NLP); yet, problems still exist since there are few well-labeled datasets for AI models to be trained on, and models' efficacy is impacted by privacy and ethical issues. Developing solid algorithms is further complicated by the disparity between false and real news. Previous techniques need to take into account the capacity to take into account both the unique conditions in which the material is provided and the larger narrative in order to handle the difficulty of contextual knowledge in spotting false news. The study investigates the possibilities of using big language models—LLaMA2-7B in particular—for the identification of false news. The study outlines three goals: 1) creating a benchmark dataset that is customized for use in a fake news dataset; 2) using the Language Model (LLM) to develop a fake news detection system through painstakingly fine-tuned training; and 3) assessing the performance of the final model. Using the LLaMA2-7B mod-el, gathering and analyzing datasets of actual and fake news, training the LLM with a variety of real news, and assessing the model's predictive power of news authenticity are all part of the technique. In spite of limited computing resources, the LLaMA2-7B model studies yielded an impressive 96.66% accuracy rate in detecting false news, outperforming earlier technologies and demonstrating the effectiveness of large language models in distinguishing different kinds of fake news.The results imply that using such models has potential for raising the detection of fake news and offering a powerful defense against false information.