@inproceedings{hieu-etal-2025-magix,
    title = "{M}a{G}i{X}: A Multi-Granular Adaptive Graph Intelligence Framework for Enhancing Cross-Lingual {RAG}",
    author = "Hieu, Nguyen Manh  and
      Anh, Vu Lam  and
      Van, Hung Pham  and
      Hai, Nam Le  and
      Van, Linh Ngo  and
      Diep, Nguyen Thi Ngoc  and
      Nguyen, Thien Huu",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.279/",
    doi = "10.18653/v1/2025.findings-emnlp.279",
    pages = "5202--5219",
    ISBN = "979-8-89176-335-7",
    abstract = "Retrieval-Augmented Generation (RAG) enhances large language models by grounding their outputs in external knowledge. Recent advances in Graph-based RAG (GRAG) frameworks, such as GraphRAG, LightRAG, and HippoRAG2, integrate knowledge graphs into the retrieval process to improve multi-hop reasoning and semantic coherence. While effective in monolingual settings, these methods remain underexplored in cross-lingual scenarios and face limitations in semantic granularity and entity alignment. In this work, we propose MaGiX, the first GRAG framework tailored for English{--}Vietnamese cross-lingual question answering. MaGiX constructs a multi-granular cross-lingual knowledge graph using fine-grained attribute descriptions and cross-synonym edges, and incorporates a custom multilingual embedding model trained with contrastive learning for semantic alignment. During retrieval, MaGiX leverages graph-based reasoning and a semantic-aware reranking strategy to enhance cross-lingual relevance. Experiments across five benchmarks show that MaGiX substantially outperforms prior GRAG systems in both retrieval accuracy and generation quality, advancing structured retrieval for multilingual QA."
}