ColBERT-XM: A Modular Multi-Vector Representation Model for Zero-Shot Multilingual Information Retrieval

TL;DR

ColBERT-XM is a modular multi-vector retrieval model that excels in zero-shot multilingual information retrieval, reducing reliance on labeled data and energy consumption while maintaining competitive performance across diverse languages.

Contribution

It introduces a novel modular dense retrieval architecture that transfers knowledge from high-resource languages to low-resource ones without additional fine-tuning.

Findings

Achieves competitive results in zero-shot multilingual retrieval tasks.

Highly data-efficient and adaptable to out-of-distribution data.

Reduces energy consumption and carbon emissions compared to existing models.

Abstract

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.