Unmasking Hallucinations: A Causal Graph-Attention Perspective on Factual Reliability in Large Language Models

Research paper proposing a causal graph-attention framework to understand and mitigate hallucinations in large language models by analyzing factual reliability mechanisms. Combines causal analysis with attention-based visualization to identify factors contributing to factual errors in LLM outputs.