Speaking Multiple Languages Affects the Moral Bias of Language Models

Katharina Hämmerl, Björn Deiseroth, Patrick Schramowski, Jindřich Libovický, Constantin A. Rothkopf, Alexander Fraser, Kristian Kersting

Findings of ACL 2023

Pre-trained multilingual language models (PMLMs) are commonly used
when dealing with data from multiple languages and crosslingual
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
MORAL DIRECTION 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
PMLMs do encode differing moral biases, but these do not necessarily
correspond to cultural differences or commonalities in human
opinions. We release our code and models.