mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval
<|reference_start|>mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval: We present systematic efforts in building long-context multilingual text representation model (TRM) and reranker from scratch for text retrieval. We first introduce a text encoder (base size) enhanced with RoPE and unpadding, pre-trained in a native 8192-token context (longer than 512 of previous multilingual encoders). Then we construct a hybrid TRM and a cross-encoder reranker by contrastive learning. Evaluations show that our text encoder outperforms the same-sized previous state-of-the-art XLM-R. Meanwhile, our TRM and reranker match the performance of large-sized state-of-the-art BGE-M3 models and achieve better results on long-context retrieval benchmarks. Further analysis demonstrate that our proposed models exhibit higher efficiency during both training and inference. We believe their efficiency and effectiveness could benefit various researches and industrial applications.<|reference_end|>
@article{zhang2024mgte:,
    title={mGTE: Generalized Long-Context Text Representation and Reranking Models
  for Multilingual Text Retrieval},
    author={Xin Zhang, Yanzhao Zhang, Dingkun Long, Wen Xie, Ziqi Dai, Jialong
  Tang, Huan Lin, Baosong Yang, Pengjun Xie, Fei Huang, Meishan Zhang, Wenjie
  Li, Min Zhang},
    journal={arXiv preprint arXiv:2407.19669},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.19669},
    primaryClass={cs.CL cs.IR}
}