Papers with Multilingual retrieval-augmented generation
Language-Coupled Reinforcement Learning for Multilingual Retrieval-Augmented Generation (2026.findings-acl)
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Rui Qi, Fengran Mo, Yufeng Chen, Xue Zhang, Shuo Wang, Hongliang Li, Xu Jinan, Meng Jiang, Jian-Yun Nie, Kaiyu Huang
| Challenge: | Existing approaches to multilingual retrieval-augmented generation (MRAG) use a single-turn retrieval and subsequent optimization to acquire and integrate beneficial external knowledge from multilingual collections. |
| Approach: | They propose a multilingual search-augmented reinforcement learning framework that integrates a language-coupled Group Relative Policy Optimization into the policy and reward models. |
| Outcome: | The proposed framework achieves competitive performance and is appropriate for various practical scenarios such as constrained training data and retrieval over collections encompassing a large number of languages. |
CORAL: Adaptive Retrieval Loop for Culturally-Aligned Multilingual RAG (2026.findings-acl)
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| Challenge: | Multilingual retrieval-augmented generation is inadequate for culturally grounded queries . Across two cultural QA benchmarks, CORAL achieves a 3.58%p accuracy improvement on low-resource languages . |
| Approach: | They propose a multilingual retrieval-augmented generation approach that enables iterative refinement of both the retrieval space and the retrieving probe based on the quality of the evidence. |
| Outcome: | Using CORAL, researchers find that culturally grounded queries can be improved . if retrieved documents are insufficient, the system reselects them and rewrites the query . |