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This research compares the effectiveness of different Retrieval-Augmented Generation (RAG) methods in processing multilingual AI-related legal documents, focusing on text summarization and content comprehension. Specifically, it contrasts standard RAG-based applications with the GraphRAG and Lazy GraphRAG approaches, conducting a cross-lingual analysis of legal texts in English, Spanish, German, and French. Using SwarmLexAI, an AI-native document analyzer, along with the Swarm API from OpenAI, spaCy, and fastText, legal materials are processed to extract key entities, keywords, and legal clauses. In addition, an AI-driven document converter improves the accuracy of summarization across languages, improving contextual understanding. A visualization system generates interactive knowledge graphs to analyze the relationships between extracted legal concepts that illustrate connections across linguistic and cultural boundaries. The study highlights trade-offs between GraphRAG, which offers in-depth, structured legal analysis, and Lazy GraphRAG, which is optimized for speed and high-level document insights. The results contribute to cross-lingual legal comparisons and enhance AI-native processing of legislation. The study aims to compare AI-related legislation across different countries by identifying these cross-lingual relationships.

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Comparative Analysis of RAG-Based Methods for Multilingual SwarmLexAI

  • 会议论文

  • 发表日期：

    2026年

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  • Yulia Kumar,
  • Jose Marchena,
  • Stephany Guzman,
  • Dov Kruger,
  • J. Jenny Li

摘要

This research compares the effectiveness of different Retrieval-Augmented Generation (RAG) methods in processing multilingual AI-related legal documents, focusing on text summarization and content comprehension. Specifically, it contrasts standard RAG-based applications with the GraphRAG and Lazy GraphRAG approaches, conducting a cross-lingual analysis of legal texts in English, Spanish, German, and French. Using SwarmLexAI, an AI-native document analyzer, along with the Swarm API from OpenAI, spaCy, and fastText, legal materials are processed to extract key entities, keywords, and legal clauses. In addition, an AI-driven document converter improves the accuracy of summarization across languages, improving contextual understanding. A visualization system generates interactive knowledge graphs to analyze the relationships between extracted legal concepts that illustrate connections across linguistic and cultural boundaries. The study highlights trade-offs between GraphRAG, which offers in-depth, structured legal analysis, and Lazy GraphRAG, which is optimized for speed and high-level document insights. The results contribute to cross-lingual legal comparisons and enhance AI-native processing of legislation. The study aims to compare AI-related legislation across different countries by identifying these cross-lingual relationships.