Tracing the Roots of Facts in Multilingual Language Models: Independent, Shared, and Transferred Knowledge
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866909132449120256 |
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| author | Zhao, Xin Yoshinaga, Naoki Oba, Daisuke |
| author_facet | Zhao, Xin Yoshinaga, Naoki Oba, Daisuke |
| contents | Acquiring factual knowledge for language models (LMs) in low-resource languages poses a serious challenge, thus resorting to cross-lingual transfer in multilingual LMs (ML-LMs). In this study, we ask how ML-LMs acquire and represent factual knowledge. Using the multilingual factual knowledge probing dataset, mLAMA, we first conducted a neuron investigation of ML-LMs (specifically, multilingual BERT). We then traced the roots of facts back to the knowledge source (Wikipedia) to identify the ways in which ML-LMs acquire specific facts. We finally identified three patterns of acquiring and representing facts in ML-LMs: language-independent, cross-lingual shared and transferred, and devised methods for differentiating them. Our findings highlight the challenge of maintaining consistent factual knowledge across languages, underscoring the need for better fact representation learning in ML-LMs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_05189 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Tracing the Roots of Facts in Multilingual Language Models: Independent, Shared, and Transferred Knowledge Zhao, Xin Yoshinaga, Naoki Oba, Daisuke Computation and Language Artificial Intelligence Acquiring factual knowledge for language models (LMs) in low-resource languages poses a serious challenge, thus resorting to cross-lingual transfer in multilingual LMs (ML-LMs). In this study, we ask how ML-LMs acquire and represent factual knowledge. Using the multilingual factual knowledge probing dataset, mLAMA, we first conducted a neuron investigation of ML-LMs (specifically, multilingual BERT). We then traced the roots of facts back to the knowledge source (Wikipedia) to identify the ways in which ML-LMs acquire specific facts. We finally identified three patterns of acquiring and representing facts in ML-LMs: language-independent, cross-lingual shared and transferred, and devised methods for differentiating them. Our findings highlight the challenge of maintaining consistent factual knowledge across languages, underscoring the need for better fact representation learning in ML-LMs. |
| title | Tracing the Roots of Facts in Multilingual Language Models: Independent, Shared, and Transferred Knowledge |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2403.05189 |