Mixed-Distill enzh-zh (English–Chinese)
A cross-lingual ColBERT late-interaction retriever (XLM-RoBERTa-large backbone) for English ⇄ Chinese web search. The model is distilled from a strong reranker and trained on code-switched (mixed-language) queries, following the Mixed-Distill recipe from the MiLQ paper.
The repo name encodes the training direction: enzh-zh = code-switched en+zh queries → zh documents.
It is designed to be robust when bilingual users issue mixed English+Chinese queries against
Chinese-language documents.
What "Mixed-Distill" means
- Mixed — queries are code-switched (English tokens randomly mixed into Chinese queries, MUSE-based, ~0.5 mixing ratio), so the model handles native, English, and mixed-language queries.
- Distill — trained with knowledge distillation (KL-divergence) from teacher relevance scores (mT5-XXL / monoT5 over mMARCO), 6-way passage scoring.
Intended use
- Queries: English, Chinese, or code-switched English+Chinese.
- Documents: Chinese-language passages.
- Scoring: ColBERT late interaction (MaxSim over per-token embeddings).
Specs
| Base model | xlm-roberta-large |
| Architecture | ColBERT (late interaction) |
| Projection dim | 128 |
| Similarity | cosine |
| Query max length | 32 |
| Doc max length | 180 |
| Training | KD (KLD), n-way 6, teacher: mT5-XXL/monoT5 on mMARCO |
Usage
Load with the ColBERT library:
from colbert.modeling.checkpoint import Checkpoint
from colbert.infra import ColBERTConfig
ckpt = Checkpoint("<your-username>/ColBERT-XLMR-Mixed-Distill-enzh-zh",
colbert_config=ColBERTConfig())
Q = ckpt.queryFromText(["mixed English+Chinese query ..."])
D = ckpt.docFromText(["Chinese document passage ..."])
Citation
@misc{kim2025milqbenchmarkingirmodels,
title={MiLQ: Benchmarking IR Models for Bilingual Web Search with Mixed Language Queries},
author={Jonghwi Kim and Deokhyung Kang and Seonjeong Hwang and Yunsu Kim and Jungseul Ok and Gary Lee},
year={2025},
eprint={2505.16631},
archivePrefix={arXiv},
primaryClass={cs.IR},
url={https://arxiv.org/abs/2505.16631},
}