Optimal interpolation of query embeddings from parallel translations outperforms the best monolingual query in 88/105 cases on mMARCO, showing English-driven asymmetry and negative correlation with typological distance.
D ista L s: a Comprehensive Collection of Language Distance Measures
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CL 2years
2026 2representative citing papers
Merging any combination of monolingual pre-trained models leads to performance collapse due to interference, indicating that merging flexibility from fine-tuning does not extend to pre-training.
citing papers explorer
-
When Does Mixing Help? Analyzing Query Embedding Interpolation in Multilingual Dense Retrieval
Optimal interpolation of query embeddings from parallel translations outperforms the best monolingual query in 88/105 cases on mMARCO, showing English-driven asymmetry and negative correlation with typological distance.
-
On the Limits of Model Merging for Multilinguality in Pre-Training
Merging any combination of monolingual pre-trained models leads to performance collapse due to interference, indicating that merging flexibility from fine-tuning does not extend to pre-training.