Plaza-del-Arco, F.M., Cercas Curry, A., Paoli, S. et al. (2 more authors) (2024) Divine LLaMAs: Bias, Stereotypes, Stigmatization, and Emotion Representation of Religion in Large Language Models. In: Findings of the Association for Computational Linguistics: EMNLP 2024. The 2024 Conference on Empirical Methods in Natural Language Processing, 12-16 Nov 2024, Miami, FL, USA. Association of Computer Linguistics , pp. 4346-4366. ISBN 979-8-89176-168-1
Abstract
Emotions play important epistemological and cognitive roles in our lives, revealing our values and guiding our actions. Previous work has shown that LLMs display biases in emotion attribution along gender lines. However, unlike gender, which says little about our values, religion, as a socio-cultural system, prescribes a set of beliefs and values for its followers. Religions, therefore, cultivate certain emotions. Moreover, these rules are explicitly laid out and interpreted by religious leaders. Using emotion attribution, we explore how different religions are represented in LLMs. We find that:Major religions in the US and European countries are represented with more nuance, displaying a more shaded model of their beliefs.Eastern religions like Hinduism and Buddhism are strongly stereotyped.Judaism and Islam are stigmatized – the models’ refusal skyrocket. We ascribe these to cultural bias in LLMs and the scarcity of NLP literature on religion. In the rare instances where religion is discussed, it is often in the context of toxic language, perpetuating the perception of these religions as inherently toxic. This finding underscores the urgent need to address and rectify these biases. Our research emphasizes the crucial role emotions play in shaping our lives and how our values influence them.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024 Association for Computational Linguistics. This is an open access conference paper under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Arts, Humanities and Cultures (Leeds) > School of Philosophy, Religion and History of Science (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 27 Sep 2024 09:33 |
Last Modified: | 28 Nov 2024 15:03 |
Status: | Published |
Publisher: | Association of Computer Linguistics |
Identification Number: | 10.18653/v1/2024.findings-emnlp.251 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:217683 |