Legal contract understanding often requires connecting information distributed across multiple clauses and cross-referenced provisions. While Retrieval-Augmented Generation (RAG) grounds Large Language Models (LLMs) in relevant evidence, conventional retrieval primarily prioritizes direct query relevance and may overlook supporting contractual context. We propose the Multi-View Contract Graph (MCG), a contract-specific knowledge graph that models semantic, entity, keyword, and structural relationships among clauses. By traversing these relationships, MCG retrieves supporting evidence that may not directly match the query but is necessary for accurate interpretation. We evaluate MCG on the Contract Understanding Atticus Dataset (CUAD), covering 15 information categories, including audit rights, governing laws, and liability-related provisions. Across five open-source LLMs, MCG consistently outperforms traditional retrieval, achieving a best F₁ score of 0.460 with Mistral-7B-Instruct-v0.3. Compared with BM25-RAG and ColBERT-RAG, the best MCG configuration improves F₁ by approximately 10.0% and 12.2%, respectively. These results demonstrate that modeling clause-level relationships improves retrieval and information extraction for legal contracts.

Policy documents often employ intricate and technical language, posing comprehension challenges for policyholders and increasing the risk of misinterpretation, financial losses, and legal disputes. To address these issues, we propose an automated framework leveraging Retrieval-Augmented Generation to identify and clarify potentially mis-interpretable paragraphs within policy documents. The framework consists of two key modules: the Annotation module and the Rectification module. The Annotation module employs both paragraph-level and document-level contextual reasoning to classify paragraphs into categories indicative of potential misinterpretation. The Rectification module resolves these ambiguities by generating targeted interpretation queries, retrieving relevant document-level context, and incorporating external knowledge sources. Applied to a corpus of 240 real-world policy documents, the Annotation module produced a benchmark dataset comprising 11,000 annotated paragraphs, enabling systematic evaluation of interpretability issues. We assessed the dataset’s quality through expert-driven manual reviews and large-scale automated evaluations using fine-tuned Pretrained Language Model. For the Rectification module, we evaluated five open-source Large Language Models: Mistral-2-7B, Mistral-3-7B, LLaMA-2-7B, LLaMA-3-8B, andSaul-7B. Among these, Mistral-2-7B achieved the highest human evaluation scores: 0.912 for Clarity, 0.914 for Fidelity, and 0.934 for Usefulness. This work demonstrates the practical feasibility of utilizing automated frameworks to enhance the clarity and comprehensibility of complex policy documents, thereby mitigating risks associated with misinterpretation and its adverse consequences.