Model Card for XProvence-reranker

• Model: XProvence
• Backbone model: bge-reranker-v2-m3
• Model size: 568 million parameters
• Context length: 8192 tokens

Usage

Model interface

• question: Union[List[str], str]: an input question (str) or a list of input questions (for batched processing)
• context: Union[List[List[str]], str]: context(s) to be pruned. This can be either a single string (in case of a singe str question), or a list of lists contexts (a list of contexts per question), with len(contexts) equal to len(questions)
• title: Optional[Union[List[List[str]], str]], default: “first_sentence”: an optional argument for defining titles. If title=first_sentence, then the first sentence of each context is assumed to be the title. If title=None, then it is assumed that no titles are provided. Titles can be also passed as a list of lists of str, i.e. titles shaped the same way as contexts. Titles are only used if always_select_title=True.
• threshold (float, \(\in [0, 1]\), default 0.3): which threshold to use for context pruning. We recommend 0.3 for more conservative pruning (no performance drop or lowest performance drops) and 0.7 for higher compression, but this value can be further tuned to meet the specific use case requirements.
• always_select_title (bool, default: True): if True, the first sentence (title) will be included into the selection each time the model select a non-empty selection of sentences. This is important, e.g., for Wikipedia passages, to provide proper contextualization for the next sentences.
• batch_size (int, default: 32)
• reorder (bool, default: False): if True, the provided contexts for each question will be reordered according to the computed question-passage relevance scores. If False, the original user-provided order of contexts will be preserved.
• top_k (int, default: 5): if reorder=True, specifies the number of top-ranked passages to keep for each question.
• enable_warnings (bool, default: True): whether the user prefers the warning about model usage to be printed, e.g. too long contexts or questions.

Model features

• XProvence natively supports 16 languages.
• XProvence supports 100+ languages via cross lingual transfer.
• XProvence encodes all sentences in the passage together: this enables capturing of coreferences between sentences and provides more accurate context pruning.
• XProvence automatically detects the number of sentences to keep, based on a threshold. We found that the default value of a threshold works well across various domains, but the threshold can be adjusted further to better meet the particular use case needs.
• XProvence works out-of-the-box with any LLM.

Model Details

• Input: user question (e.g., a sentence) + retrieved context passage (e.g., a paragraph). Training data consisted of monolingual examples (query and context in the same language), but we expect the model to perform well on cross-lingual pairs too, due to cross-lingual transfer.
• Output: pruned context passage, i.e., irrelevant sentences are removed + relevance score (can be used for reranking)
• Model Architecture: The model was initialized from bge-reranker-v2-m3 and finetuned with two objectives: (1) output a binary mask which can be used to prune irrelevant sentences; and (2) preserve initial reranking capabilities.
• Training data: MS Marco + MIRACL, with synthetic silver labelling of which sentences to keep, produced using aya-expanse-8b.
• Languages in the training data: Arabic, Bengali, English, Spanish, Persian, Finnish, France, Hindi, Indonesian, Japanese, Korean, Russian, Swahili, Telugu, Thai, Chinese
• Context length: 8192 tokens (similar to the pretrained BGE-m3 model). However, training data only included paragraph-sized examples.
• Evaluation: we evaluate XProvence on 26 languages from 6 different datasets. We find that XProvence is able to prune irrelevant sentences with little-to-no drop in performance, on all languages, and outperforms existing baselines on the Pareto front.

License

Cite

@misc{mohamed2026xprovence,
      title={XProvence: Zero-Cost Multilingual Context Pruning for Retrieval-Augmented Generation}, 
      author={Mohamed, Youssef and Elhoseiny, Mohamed and Formal, Thibault and Chirkova, Nadezhda},
      year={2026},
      eprint={2601.18886},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2601.18886}, 
}