State-of-the-art neural retrievers predominantly focus on high-resource languages like English, which impedes their adoption in retrieval scenarios involving other languages. Current approaches circumvent the lack of high-quality labeled data in non-English languages by leveraging multilingual pretrained language models capable of cross-lingual transfer. However, these models require substantial task-specific fine-tuning across multiple languages, often perform poorly in languages with minimal representation in the pretraining corpus, and struggle to incorporate new languages after the pretraining phase. In this work, we present a novel modular dense retrieval model that learns from the rich data of a single high-resource language and effectively zero-shot transfers to a wide array of languages, thereby eliminating the need for language-specific labeled data. Our model, ColBERT-XM, demonstrates competitive performance against existing state-of-the-art multilingual retrievers trained on more extensive datasets in various languages. Further analysis reveals that our modular approach is highly data-efficient, effectively adapts to out-of-distribution data, and significantly reduces energy consumption and carbon emissions. By demonstrating its proficiency in zero-shot scenarios, ColBERT-XM marks a shift towards more sustainable and inclusive retrieval systems, enabling effective information accessibility in numerous languages. We publicly release our code and models for the community.

这项研究提出了一种新颖的模块化密集检索模型ColBERT-XM，它通过学习单个高资源语言的丰富数据，并能够有效地零-shot转换到各种语言，从而消除了对特定语言的标记数据的需求。通过展示在零-shot场景中的娴熟表现，ColBERT-XM标志着向更可持续和包容的检索系统的转变，实现了多种语言的有效信息获取。我们公开发布我们的代码和模型供社区使用。

ColBERT-XM: 零射多语信息检索的模块化多向量表示模型