Update README.md

README.md

@@ -110,12 +110,12 @@ Interface of the `process` function:
 
 ## Model Details
 
-* Input: user question (e.g., a sentence) + retrieved context passage (e.g., a paragraph)
+* 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](https://huggingface.co/BAAI/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](https://microsoft.github.io/msmarco/Datasets.html) + [MIRACL](https://huggingface.co/datasets/miracl/miracl), with synthetic silver labelling of which sentences to keep, produced using [aya-expanse-8b](https://huggingface.co/CohereLabs/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)
+* 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.