Multilingual LAMA: Investigating Knowledge in Multilingual Pretrained Language Models
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Abstract (arXiv)
Recently, it has been found that monolingual English language models can be used as knowledge bases. Instead of structural knowledge base queries, masked sentences such as "Paris is the capital of [MASK]" are used as probes. We translate the established benchmarks TREx and GoogleRE into 53 languages. Working with mBERT, we investigate three questions. (i) Can mBERT be used as a multilingual knowledge base? Most prior work only considers English. Extending research to multiple languages is important for diversity and accessibility. (ii) Is mBERT's performance as knowledge base language-independent or does it vary from language to language? (iii) A multilingual model is trained on more text, e.g., mBERT is trained on 104 Wikipedias. Can mBERT leverage this for better performance? We find that using mBERT as a knowledge base yields varying performance across languages and pooling predictions across languages improves performance. Conversely, mBERT exhibits a language bias; e.g., when queried in Italian, it tends to predict Italy as the country of origin.
Key facts. Cited by 132 papers in the map (data through 7 Oct 2026). Builds on 25 of its 34 references that are in the map. First posted on arXiv 1 Feb 2021.
Builds on
25 of 34 references.
References · 25
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- 2019MLQA: Evaluating Cross-lingual Extractive Question Answeringcited by 339
- 2020K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapterscited by 318
- 2019Negated and Misprimed Probes for Pretrained Language Models: Birds Can Talk, But Cannot Flycited by 240
- 2020How Context Affects Language Models' Factual Predictionscited by 181
- 2020MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answeringcited by 151
- 2020X-FACTR: Multilingual Factual Knowledge Retrieval from Pretrained Language Modelscited by 117
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Citing papers per year: 2021: 17; 2022: 18; 2023: 23; 2024: 39; 2025: 25; 2026: 10.
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- 2021Few-shot Learning with Multilingual Language Modelscited by 278
- 2022Challenges and Strategies in Cross-Cultural NLPcited by 216
- 2022mGPT: Few-Shot Learners Go Multilingualcited by 141
- 2021AMMUS : A Survey of Transformer-based Pretrained Models in Natural Language Processingcited by 131
- 2023Cross-Lingual Consistency of Factual Knowledge in Multilingual Language Modelscited by 117
- 2023Can Knowledge Graphs Reduce Hallucinations in LLMs? : A Surveycited by 92
- 2024A Survey on Multilingual Large Language Models: Corpora, Alignment, and Biascited by 82
- 2021Common Sense Beyond English: Evaluating and Improving Multilingual Language Models for Commonsense Reasoningcited by 64
- 2024Factual Confidence of LLMs: on Reliability and Robustness of Current Estimatorscited by 59
- 2022Measuring Causal Effects of Data Statistics on Language Model's `Factual' Predictionscited by 57
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- 2026SWORD: Wikidata-based Distortions Reveal Hidden Cross-Lingual Inconsistencies in LLM Factual Error Rejection8 Sep 2026
- 2026A Systematic Evaluation of Cross-Lingual Consistency Enhancement Methods in Multilingual Language Models3 Sep 2026
- 2026Probing Factual Knowledge Transfer with Training Data Interventions1 Sep 2026
- 2026Latent-Space Intervention for Cross-Lingual Factual Consistency: Consistency Improvements without Accuracy Drops28 Aug 2026
- 2026Probing for Knowledge Attribution in Large Language Models26 Feb 2026
- 2026Culturally-Grounded Governance for Multilingual Language Models: Rights, Data Boundaries, and Accountable AI Design31 Jan 2026
- 2026Evaluating Contextually Mediated Factual Recall in Multilingual Large Language Models18 Jan 2026
- 2026Beyond Variance: Knowledge-Aware LLM Compression via Fisher-Aligned Subspace Diagnostics12 Jan 2026
- 2026When Abundance Conceals Weakness: Knowledge Conflict in Multilingual Models11 Jan 2026
- 2025FIBER: A Multilingual Evaluation Resource for Factual Inference Bias11 Dec 2025
- 2025Do You Know About My Nation? Investigating Multilingual Language Models' Cultural Literacy Through Factual Knowledge1 Nov 2025
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