Retrieval-Augmented Multilingual Citation Generation
Xun Liang, Simin Niu, Sensen Zhang, Zhiyu Li, Xuan Zhang, Bo Wu, Feiyu Xiong, Bo Tang
Abstract
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.
BibTeX
@inproceedings{icassp2025_retrievalaugment,
title = {Retrieval-Augmented Multilingual Citation Generation},
author = {Xun Liang and Simin Niu and Sensen Zhang and Zhiyu Li and Xuan Zhang and Bo Wu and Feiyu Xiong and Bo Tang and Hanyu Wang and Shichao Song and Mengwei Wang and Jiawei Yang},
booktitle = {ICASSP 2025},
year = {2025}
}