Retrieval-Augmented Multilingual Citation Generation
Xun Liang, Simin Niu, Sensen Zhang, Zhiyu Li, Xuan Zhang, Bo Wu, Feiyu Xiong, Bo Tang, Hanyu Wang, Shichao Song, Mengwei Wang, Jiawei Yang · 2025
Retrieval-augmented citation generation (RACG) helps users trust the large language model output by retrieving evidence from reliable sources. However, most current RACG research focuses on single-language tasks, particularly in English, and overlooks the need for cross-lingual evidence retrieval and utilization in real-world applications. To address this issue, we introduce a plug-and-play Retrieval-Augmented Multilingual Citation Generation method (RAMCG) which uses a multilingual retriever to identify relevant evidence from a multilingual knowledge base. The evidence is then combined with the query and processed by a multilingual citation generator. The result is citations that are both accurate and comprehensive. Experiments show that RAMCG outperforms baseline methods in multilingual citation generation and is well-suited for practical use.