XProvence: Zero-Cost Multilingual Context Pruning for Retrieval-Augmented Generation
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866910001715478528 |
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| author | Mohamed, Youssef Elhoseiny, Mohamed Formal, Thibault Chirkova, Nadezhda |
| author_facet | Mohamed, Youssef Elhoseiny, Mohamed Formal, Thibault Chirkova, Nadezhda |
| contents | This paper introduces XProvence, a multilingual zero-cost context pruning model for retrieval-augmented generation (RAG), trained on 16 languages and supporting 100+ languages through effective cross-lingual transfer. Motivated by the growing use of RAG systems across diverse languages, we explore several strategies to generalize the Provence framework-which first integrated efficient zero-cost context pruning directly into the re-ranking model-beyond English. Across four multilingual question answering benchmarks, we show how XProvence can prune RAG contexts with minimal-to-no performance degradation and outperforms strong baselines. Our model is available at https://huggingface.co/naver/xprovence-reranker-bgem3-v2. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_18886 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | XProvence: Zero-Cost Multilingual Context Pruning for Retrieval-Augmented Generation Mohamed, Youssef Elhoseiny, Mohamed Formal, Thibault Chirkova, Nadezhda Information Retrieval Computation and Language This paper introduces XProvence, a multilingual zero-cost context pruning model for retrieval-augmented generation (RAG), trained on 16 languages and supporting 100+ languages through effective cross-lingual transfer. Motivated by the growing use of RAG systems across diverse languages, we explore several strategies to generalize the Provence framework-which first integrated efficient zero-cost context pruning directly into the re-ranking model-beyond English. Across four multilingual question answering benchmarks, we show how XProvence can prune RAG contexts with minimal-to-no performance degradation and outperforms strong baselines. Our model is available at https://huggingface.co/naver/xprovence-reranker-bgem3-v2. |
| title | XProvence: Zero-Cost Multilingual Context Pruning for Retrieval-Augmented Generation |
| topic | Information Retrieval Computation and Language |
| url | https://arxiv.org/abs/2601.18886 |