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.

本研究针对现有多语言文本检索模型在长文本处理中的不足，提出了一种新型的长上下文多语言文本表示模型及重排序模型。通过对文本编码器的增强和对比学习，研究展示了该模型在长上下文检索基准上的优越性能，潜在推动了该领域的研究和实际应用。

mGTE：用于多语言文本检索的广义长文本表示与重排序模型