TY - GEN
T1 - Comparative Analysis of RAG-Based Methods for Multilingual SwarmLexAI
AU - Kumar, Yulia
AU - Marchena, Jose
AU - Guzman, Stephany
AU - Kruger, Dov
AU - Li, J. Jenny
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
PY - 2026
Y1 - 2026
N2 - 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.
AB - 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.
KW - AI-native document analyzer
KW - GraphRAG
KW - Lazy GraphRAG
KW - SwarmLexAI
UR - https://www.scopus.com/pages/publications/105022979193
U2 - 10.1007/978-3-032-07109-5_16
DO - 10.1007/978-3-032-07109-5_16
M3 - Conference contribution
AN - SCOPUS:105022979193
SN - 9783032071088
T3 - Lecture Notes in Networks and Systems
SP - 229
EP - 245
BT - Intelligent Systems and Applications - Proceedings of the 2025 Intelligent Systems Conference IntelliSys
A2 - Arai, Kohei
PB - Springer Science and Business Media Deutschland GmbH
T2 - 11th Intelligent Systems Conference, IntelliSys 2025
Y2 - 28 August 2025 through 29 August 2025
ER -