@inproceedings{haemmerl-etal-2023-speaking,
title = "Speaking Multiple Languages Affects the Moral Bias of Language Models",
author = "Haemmerl, Katharina and
Deiseroth, Bjoern and
Schramowski, Patrick and
Libovick{\'y}, Jind{\v{r}}ich and
Rothkopf, Constantin and
Fraser, Alexander and
Kersting, Kristian",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-acl.134",
doi = "10.18653/v1/2023.findings-acl.134",
pages = "2137--2156",
abstract = "Pre-trained multilingual language models (PMLMs) are commonly used when dealing with data from multiple languages and cross-lingual transfer. However, PMLMs are trained on varying amounts of data for each language. In practice this means their performance is often much better on English than many other languages. We explore to what extent this also applies to moral norms. Do the models capture moral norms from English and impose them on other languages? Do the models exhibit random and thus potentially harmful beliefs in certain languages? Both these issues could negatively impact cross-lingual transfer and potentially lead to harmful outcomes. In this paper, we (1) apply the MORALDIRECTION framework to multilingual models, comparing results in German, Czech, Arabic, Chinese, and English, (2) analyse model behaviour on filtered parallel subtitles corpora, and (3) apply the models to a Moral Foundations Questionnaire, comparing with human responses from different countries. Our experiments demonstrate that, indeed, PMLMs encode differing moral biases, but these do not necessarily correspond to cultural differences or commonalities in human opinions. We release our code and models.",
}
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<abstract>Pre-trained multilingual language models (PMLMs) are commonly used when dealing with data from multiple languages and cross-lingual transfer. However, PMLMs are trained on varying amounts of data for each language. In practice this means their performance is often much better on English than many other languages. We explore to what extent this also applies to moral norms. Do the models capture moral norms from English and impose them on other languages? Do the models exhibit random and thus potentially harmful beliefs in certain languages? Both these issues could negatively impact cross-lingual transfer and potentially lead to harmful outcomes. In this paper, we (1) apply the MORALDIRECTION framework to multilingual models, comparing results in German, Czech, Arabic, Chinese, and English, (2) analyse model behaviour on filtered parallel subtitles corpora, and (3) apply the models to a Moral Foundations Questionnaire, comparing with human responses from different countries. Our experiments demonstrate that, indeed, PMLMs encode differing moral biases, but these do not necessarily correspond to cultural differences or commonalities in human opinions. We release our code and models.</abstract>
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%0 Conference Proceedings
%T Speaking Multiple Languages Affects the Moral Bias of Language Models
%A Haemmerl, Katharina
%A Deiseroth, Bjoern
%A Schramowski, Patrick
%A Libovický, Jindřich
%A Rothkopf, Constantin
%A Fraser, Alexander
%A Kersting, Kristian
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F haemmerl-etal-2023-speaking
%X Pre-trained multilingual language models (PMLMs) are commonly used when dealing with data from multiple languages and cross-lingual transfer. However, PMLMs are trained on varying amounts of data for each language. In practice this means their performance is often much better on English than many other languages. We explore to what extent this also applies to moral norms. Do the models capture moral norms from English and impose them on other languages? Do the models exhibit random and thus potentially harmful beliefs in certain languages? Both these issues could negatively impact cross-lingual transfer and potentially lead to harmful outcomes. In this paper, we (1) apply the MORALDIRECTION framework to multilingual models, comparing results in German, Czech, Arabic, Chinese, and English, (2) analyse model behaviour on filtered parallel subtitles corpora, and (3) apply the models to a Moral Foundations Questionnaire, comparing with human responses from different countries. Our experiments demonstrate that, indeed, PMLMs encode differing moral biases, but these do not necessarily correspond to cultural differences or commonalities in human opinions. We release our code and models.
%R 10.18653/v1/2023.findings-acl.134
%U https://aclanthology.org/2023.findings-acl.134
%U https://doi.org/10.18653/v1/2023.findings-acl.134
%P 2137-2156
Markdown (Informal)
[Speaking Multiple Languages Affects the Moral Bias of Language Models](https://aclanthology.org/2023.findings-acl.134) (Haemmerl et al., Findings 2023)
ACL
- Katharina Haemmerl, Bjoern Deiseroth, Patrick Schramowski, Jindřich Libovický, Constantin Rothkopf, Alexander Fraser, and Kristian Kersting. 2023. Speaking Multiple Languages Affects the Moral Bias of Language Models. In Findings of the Association for Computational Linguistics: ACL 2023, pages 2137–2156, Toronto, Canada. Association for Computational Linguistics.