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arXiv · 2601.18886

XProvence: Zero-Cost Multilingual Context Pruning for Retrieval-Augmented Generation

Youssef Mohamed·Mohamed Elhoseiny·Thibault Formal·Nadezhda Chirkova
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Abstract

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.

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Youssef Mohamed, Mohamed Elhoseiny, Thibault Formal, Nadezhda Chirkova. 2026-01-26. XProvence: Zero-Cost Multilingual Context Pruning for Retrieval-Augmented Generation. https://arxiv.org/abs/2601.18886

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