Histoires Morales: A French Dataset for Assessing Moral Alignment
Aligning language models with human values is crucial, especially as they
become more integrated into everyday life. While models are often adapted to
user preferences, it is equally important to ensure they align with moral norms
and behaviours in real-world social situations. Despite significant progress in
languages like English and Chinese, French has seen little attention in this
area, leaving a gap in understanding how LLMs handle moral reasoning in this
language. To address this gap, we introduce Histoires Morales, a French dataset
derived from Moral Stories, created through translation and subsequently
refined with the assistance of native speakers to guarantee grammatical
accuracy and adaptation to the French cultural context. We also rely on
annotations of the moral values within the dataset to ensure their alignment
with French norms. Histoires Morales covers a wide range of social situations,
including differences in tipping practices, expressions of honesty in
relationships, and responsibilities toward animals. To foster future research,
we also conduct preliminary experiments on the alignment of multilingual models
on French and English data and the robustness of the alignment. We find that
while LLMs are generally aligned with human moral norms by default, they can be
easily influenced with user-preference optimization for both moral and immoral
data.
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Thibaud Leteno
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2025-01-29T17:29:00.764Z
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Thibaud Leteno
user
thibaudltn
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Irina Proskurina
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2025-01-29T17:16:01.526Z
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Irina Proskurina
user
iproskurina
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Antoine Gourru
admin_assigned
2025-01-29T17:29:06.818Z
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Antoine Gourru
user
AntoineGourru
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Julien Velcin
admin_assigned
2025-01-29T17:29:13.336Z
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Julien Velcin
user
jvelcin
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Charlotte Laclau
6799e5f9121155210e4fa491
Guillaume Metzler
6799e5f9121155210e4fa492
Christophe Gravier
Histoires Morales: A French Dataset for Assessing Moral Alignment
Aligning language models with human values is crucial, especially as they
become more integrated into everyday life. While models are often adapted to
user preferences, it is equally important to ensure they align with moral norms
and behaviours in real-world social situations. Despite significant progress in
languages like English and Chinese, French has seen little attention in this
area, leaving a gap in understanding how LLMs handle moral reasoning in this
language. To address this gap, we introduce Histoires Morales, a French dataset
derived from Moral Stories, created through translation and subsequently
refined with the assistance of native speakers to guarantee grammatical
accuracy and adaptation to the French cultural context. We also rely on
annotations of the moral values within the dataset to ensure their alignment
with French norms. Histoires Morales covers a wide range of social situations,
including differences in tipping practices, expressions of honesty in
relationships, and responsibilities toward animals. To foster future research,
we also conduct preliminary experiments on the alignment of multilingual models
on French and English data and the robustness of the alignment. We find that
while LLMs are generally aligned with human moral norms by default, they can be
easily influenced with user-preference optimization for both moral and immoral
data.
Histoires Morales, a French moral reasoning dataset, addresses gaps in understanding how language models align with human values in French by providing culturally adapted data and annotations.
Moral Stories multilingual models moral reasoning cultural context user-preference optimization