Tracing the Roots of Facts in Multilingual Language Models: Independent, Shared, and Transferred Knowledge

Unveiling Cross-Lingual Knowledge Transfer in Multilingual Language Models

Introduction

The quest for understanding how multilingual language models (ML-LMs) such as mBERT and XLM-R acquire and represent factual knowledge across languages has been a topic of significant interest in the field of NLP. These models are known for their ability to transfer knowledge across languages, leveraging shared representations to aid low-resource languages. However, the mechanisms behind factual knowledge acquisition and representation remain opaque. This paper addresses the gap by probing ML-LMs to uncover how factual knowledge is acquired, focusing on language-independent knowledge, cross-lingual shared, and cross-lingual transferred representations.

Probing Multilingual Factual Knowledge

Utilizing the mLAMA dataset, an extensive investigation was conducted into the model's ability to recognize factual knowledge in various languages. The results reveal a moderate correlation between probing performance and the amount of training data, highlighting a nuanced relationship between data availability and factual knowledge acquisition in ML-LMs. Interestingly, the study also identified localized factual knowledge clusters among languages, suggesting that geographical proximity and shared culture might play a role in knowledge transfer.

Investigating Factual Representation in ML-LMs

Through neuron-level analysis, three patterns of factual knowledge representation were discerned: language-independent, cross-lingual shared, and cross-lingual transferred. This differentiation sheds light on the complexity of knowledge representation within ML-LMs. It appears that while some facts are stored in a language-specific manner, others are shared or transferred across languages, underscoring the diversity in knowledge encoding strategies.

Tracing the Roots of Factual Knowledge

To further understand the formation of cross-lingual representations, the study traced the roots of facts back to their sources in Wikipedia, the primary training corpus for mBERT. Surprisingly, a significant number of facts correctly predicted by the model were not directly present in the corpus, suggesting the model's ability to infer or transfer knowledge across languages. This finding emphasizes the models' capability beyond mere memorization, pointing toward sophisticated inference mechanisms at play.

Implications and Future Directions

This research provides valuable insights into the mechanics of cross-lingual knowledge transfer in ML-LMs, revealing how models represent and acquire factual knowledge across languages. The discovery of language-independent, cross-lingual shared, and transferred knowledge representations opens new avenues for enhancing the cross-lingual capabilities of ML-LMs. Future work could focus on refining probing techniques to better capture the model's knowledge representation strategies and exploring methods to enhance cross-lingual fact learning, especially for low-resource languages.

The findings underscore the complexity of factual knowledge representation in ML-LMs and highlight the need for further research to unravel the intricate mechanisms of knowledge acquisition and cross-lingual transfer. As the field of NLP continues to advance, understanding these mechanisms will be crucial for developing more robust and versatile multilingual models.

Conclusion

This paper provides a thorough analysis of factual knowledge representation in multilingual language models, uncovering the nuanced mechanisms of knowledge acquisition and representation. The findings highlight the model's capability to leverage cross-lingual knowledge transfer, contributing to the understanding of ML-LMs' inner workings. This research marks a step forward in unraveling the complexities of multilingual language models, laying the groundwork for future advancements in the field.