The complexity of legal language creates significant barriers to public access to justice and informed consent. This paper presents a comprehensive assessment of advanced Large Language Models (LLMs) for legal document simplification. We investigate the effectiveness of transformer-based architectures, including encoder-decoder models and instruction-tuned LLMs, in translating complex legal documents into plain language. The study examines real-world implementations and addresses critical challenges such as factual hallucination, context preservation, and ethical considerations. Based on experimental analysis and comparative evaluation, the paper identifies optimal approaches for accuracy optimization in legal text simplification systems.