Morality is Non-Binary: Building a Pluralist Moral Sentence Embedding Space using Contrastive Learning (2024.findings-eacl)
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| Challenge: | Existing NLP methods treat morality as binary, ranging from right to wrong. |
| Approach: | They propose to build a pluralist moral sentence embedding space using contrastive learning methods to examine relationships among moral elements. |
| Outcome: | The proposed method shows that pluralism can be captured in an embedding space. |
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| Challenge: | Existing methods to debias word embeddings in binary settings such as gender and religion are limited to binary labels, whereas word2vec embedders can be used to propagate biases. |
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A Survey on Modelling Morality for Text Analysis (2024.findings-acl)
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| Challenge: | Recent work on modelling morality in text has garnered increasing attention due to its complexity and complexity. |
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| Challenge: | a recent study examines the morality of NLP models that can take in arbitrary text and output a moral judgment . a Delphi project is a popular system for moral prediction, but it has received criticism . |
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| Challenge: | Recent studies have focused on the ethical aspects of NLP, but little to no discussion of the terminology and theories underpinning those efforts and their implications. |
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| Challenge: | Existing methods to remove gender bias from word embeddings are insufficient, we argue . existing methods for gender-neutral modeling are ineffective, we conclude . |
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What does a Text Classifier Learn about Morality? An Explainable Method for Cross-Domain Comparison of Moral Rhetoric (2023.acl-long)
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Enrico Liscio, Oscar Araque, Lorenzo Gatti, Ionut Constantinescu, Catholijn Jonker, Kyriaki Kalimeri, Pradeep Kumar Murukannaiah
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| Challenge: | Using large language models, large language model learning has become more integrated into our daily lives, making it increasingly important to ensure they reflect ethical and equitable values. |
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From Prejudice to Parity: A New Approach to Debiasing Large Language Model Word Embeddings (2025.coling-main)
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| Challenge: | Existing work in this field has looked most commonly into gender bias, racial bias, and religious bias. |
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