Adversarial Training of Retrieval Augmented Generation to Generate Believable Fake News

Recent advancements in Large Language Models (LLMs) have revolutionized Natural Language Processing (NLP) and Natural Language Understanding (NLU), showcasing their ability to produce coherent and contextually relevant responses. However, their widespread use raises serious concerns regarding the potential for generating and spreading false or misleading information. This research examines the effectiveness of customized Retrieval Augmented Generation (RAG) models, alongside fine-tuned versions of GPT-Neo and RoBERTa.The proposed framework leverages multiple generative language models, including GPT-Neo, RoBERTa, and a custom RAG model, to produce diverse fake news content grounded in retrieved contextual information. It employs a combination of large language models and a specialized fake news detection pipeline, which integrates embedding based retrieval with sentence transformers and Facebook AI Similarity Search (FAISS), while also enabling generation through GPT-Neo, RoBERTa, and custom RAG structures. Additionally, we employ a passive aggressive classifier trained on "Fake" and "Real" dataset from a public GitHub repository to assess the likelihood of generated responses being classified as "Fake" or "Real." This pipeline evaluates the authenticity of news articles and incorporates believability scores to enhance interpretability.Results indicate that while all models perform comparably, the custom RAG model consistently excels in providing contextually grounded and highly relevant fake information,in these fake news scenarios. This study highlights the robustness of retrieval augmented frameworks in adversarial tasks, offering superior alignment with factual references. It contributes to the AI driven misinformation detection landscape, providing valuable insights into model selection and training methodologies to combat social engineering and the spread of fake news.