ColBERT-XM: A Modular Multi-Vector Representation Model for Zero-Shot Multilingual Information Retrieval (2402.15059v1)

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 LLMs 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.

• The paper introduces ColBERT-XM, a modular approach that enables zero-shot language transfer for efficient multilingual information retrieval.
• It leverages monolingual fine-tuning and the XMOD architecture to match state-of-the-art retrieval performance without extensive multilingual data.
• The design significantly lowers energy consumption and computational costs, aligning with sustainable objectives in AI research.

ColBERT-XM: Enhancing Zero-Shot Multilingual Information Retrieval with a Modular Approach

Introduction to Multilingual Information Retrieval Challenges

Recent advancements in NLP have substantially improved information retrieval capabilities, particularly for high-resource languages such as English and Chinese. However, the field still faces significant challenges when it comes to efficiently retrieving information across a broad spectrum of languages, especially those considered low-resource. The limited availability of high-quality labeled data for multilingual contexts and the difficulties associated with extending models to new languages post-training are prominent obstacles. These challenges suggest the need for more adaptable and resource-efficient models.

The ColBERT-XM Solution

In response to these challenges, this research introduces ColBERT-XM, a multilingual dense retrieval model that innovatively employs a modular approach to learn from data in a single high-resource language (English) and then effectively transfer this learning to handle information retrieval tasks in a wide range of languages without requiring retraining or language-specific labeled data. This model distinguishes itself by demonstrating competitive performance against established state-of-the-art multilingual retrievers trained on far larger datasets across multiple languages, while significantly reducing energy consumption and carbon emissions.

Key Innovations and Benefits

1. Reduced Dependence on Multilingual Data: ColBERT-XM leverages the XMOD architecture, allowing the model to learn efficiently through monolingual fine-tuning, thus eliminating the need for collecting and training on extensive multilingual datasets.
2. Effective Zero-Shot Language Transfer: Thanks to its modular design, the model demonstrates remarkable adaptability, effectively transferring knowledge to diverse languages not seen during training, including those with minimal representation in pretraining corpora.
3. Sustainability: The model's efficient learning and inference processes contribute to reduced energy and computational resource usage, aligning with environmental sustainability goals within the AI research community.
4. Experimental Validation: Extensive experimental results underscore the model's efficacy across various languages, indicating that ColBERT-XM maintains, and in some cases surpasses, the performance of leading multilingual retrievers, even those trained on more extensive multilingual corpora.
5. Data Efficiency: The model exhibits impressive data efficiency, showing that additional training examples from the same distribution do not significantly enhance performance. This trait underscores its potential in low-resource scenarios where data scarcity is a common issue.

Implications and Future Prospects

The introduction of ColBERT-XM marks a significant step towards developing more inclusive and efficient multilingual retrieval systems. By demonstrating powerful zero-shot capabilities and reducing the dependency on extensive multilingual datasets, this model has the potential to greatly enhance information accessibility across languages worldwide. Its modular nature allows for easier extension to additional languages, suggesting a promising direction for future research in NLP and information retrieval.

Looking forward, while ColBERT-XM addresses several key challenges in multilingual retrieval, ongoing work is necessary to explore its adaptability to cross-lingual retrieval tasks, enhance model interpretability, and further examine the environmental impact of deploying such models. Additionally, expanding evaluations to include more varied datasets and domain-specific retrieval tasks will provide a comprehensive understanding of its versatility and capabilities.

Conclusion

ColBERT-XM represents a notable advancement in tackling the persistent challenges of multilingual information retrieval. Through its modular approach, zero-shot transfer capabilities, and commitment to sustainability, the model not only contributes to the technical progress in the field but also aligns with broader goals of inclusivity, accessibility, and environmental consciousness in artificial intelligence research.