AI is happening. The future is updating.mLateOn
> π― **TL;DR**: A 307M-parameter multilingual ColBERT (multi-vector) retrieval model achieving state-of-the-art results across **multilingual retrieval (MIRACL), long-document retrieval (MLDR), English general-domain retrieval (BEIR), and code retrieval (MTEB Code)**. Trained on nine languages only, yet generalizes to unseen languages and scripts, demonstrating that **late interaction enables translate-train viability** for broad multilingual coverage without exhaustive translation. With DenseOn and LateOn, we demonstrated that an open, carefully curated data recipe can match closed-data retrieval models on English. mDenseOn and mLateOn extend this recipe to multilingual, long-context, and code retrieval. Rather than independently collecting multilingual corpora from scratch (which would be expensive, uneven across languages, and hard to curate at the same quality), we applied the **translate-train** approach: machine-translating our validated English data into eight target languages (French, German, Italian, Spanish, Portuguese, Swedish, Norwegian, and Arabic) and adding cross-lingual pairs for cross-lingual alignment.
> π― **TL;DR**: A 307M-parameter multilingual ColBERT (multi-vector) retrieval model achieving state-of-the-art results across **multilingual retrieval (MIRACL), long-document retrieval (MLDR), English general-domain retrieval (BEIR), and code retrieval (MTEB Code)**. Trained on nine languages only, yet generalizes to unseen languages and scripts, demonstrating that **late interaction enables translate-train viability** for broad multilingual coverage without exhaustive translation. With DenseOn and LateOn, we demonstrated that an open, carefully curated data recipe can match closed-data retrieval models on English. mDenseOn and mLateOn extend this recipe to multilingual, long-context, and code retrieval. Rather than independently collecting multilingual corpora from scratch (which would be expensive, uneven across languages, and hard to curate at the same quality), we applied the **translate-train** approach: machine-translating our validated English data into eight target languages (French, German, Italian, Spanish, Portuguese, Swedish, Norwegian, and Arabic) and adding cross-lingual pairs for cross-lingual alignment.