Papers by Katharina Haemmerl

2 papers
Speaking Multiple Languages Affects the Moral Bias of Language Models (2023.findings-acl)

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Challenge: Pre-trained multilingual language models are often better on English than other languages . however, they are trained on varying amounts of data for each language .
Approach: They apply the MORALDIRECTION framework to multilingual models and analyse their results . they find that PMLMs encode differing moral biases, but these do not correspond to cultural differences or commonalities in human opinions.
Outcome: The proposed model captures moral norms from English and imposes them on other languages.
Exploring Anisotropy and Outliers in Multilingual Language Models for Cross-Lingual Semantic Sentence Similarity (2023.findings-acl)

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Challenge: Recent studies have shown that contextual language models display outlier dimensions . this is true for monolingual and multilingual models, but little work has been done on multilingual contexts .
Approach: They investigate outlier dimensions and their relationship to anisotropy in multilingual contexts . they focus on cross-lingual semantic similarity tasks .
Outcome: The proposed model improves on cross-lingual semantic similarity tasks.

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