mlsanityData to 14 Oct 2026

Do Multilingual Language Models Capture Differing Moral Norms?

arXiv 2203.09904 ↗First posted 18 Mar 2022cs.CLcited by 15

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Abstract (arXiv)

Massively multilingual sentence representations are trained on large corpora of uncurated data, with a very imbalanced proportion of languages included in the training. This may cause the models to grasp cultural values including moral judgments from the high-resource languages and impose them on the low-resource languages. The lack of data in certain languages can also lead to developing random and thus potentially harmful beliefs. Both these issues can negatively influence zero-shot cross-lingual model transfer and potentially lead to harmful outcomes. Therefore, we aim to (1) detect and quantify these issues by comparing different models in different languages, (2) develop methods for improving undesirable properties of the models. Our initial experiments using the multilingual model XLM-R show that indeed multilingual LMs capture moral norms, even with potentially higher human-agreement than monolingual ones. However, it is not yet clear to what extent these moral norms differ between languages.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Builds on 14 of its 17 references that are in the map. First posted on arXiv 18 Mar 2022.

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14 of 17 references.

References · 14

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2 more
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