Papers with Moral Foundations Theory
Knowledge Graphs meet Moral Values (2020.starsem-1)
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| Challenge: | Moral Foundations Theory (MFT) is one of the most adopted theories of morality due to its accompanying lexicon, the Moral Foundation Dictionary (MFD). |
| Approach: | They propose to use the Moral Foundation Dictionary to analyze moral values in three widely used KGs and propose several Personalized PageRank variations to score concepts and entities in the KG with respect to their relevance to the different moral values. |
| Outcome: | The proposed methods help to operationalize morality in both NLP and KG communities. |
Moral Mimicry: Large Language Models Produce Moral Rationalizations Tailored to Political Identity (2023.acl-srw)
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| Challenge: | Large Language Models (LLMs) have demonstrated impressive capabilities in generating fluent text, as well as tendencies to reproduce undesirable social biases. |
| Approach: | They propose that LLMs reproduce moral biases associated with political groups in the United States, an instance of a broader capability termed moral mimicry. |
| Outcome: | The LLMs generated by the models reproduce moral biases associated with political groups in the United States, and this is an instance of a broader capability termed moral mimicry. |
MFTCXplain: A Multilingual Benchmark Dataset for Evaluating the Moral Reasoning of LLMs through Multi-hop Hate Speech Explanation (2025.findings-emnlp)
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Jackson Trager, Francielle Vargas, Diego Alves, Matteo Guida, Mikel K. Ngueajio, Ameeta Agrawal, Yalda Daryani, Farzan Karimi Malekabadi, Flor Miriam Plaza-del-Arco
| Challenge: | Existing evaluation benchmarks for large language models lack annotations that justify moral classifications and focus on English constrain moral reasoning across diverse cultural settings. |
| Approach: | They propose a multilingual benchmark dataset for evaluating moral reasoning of large language models . it includes 3,000 tweets annotated with binary hate speech labels, moral categories and rationales . |
| Outcome: | The proposed dataset shows a misalignment between LLM outputs and human annotations in moral reasoning tasks. |
Probing Narrative Morals: A New Character-Focused MFT Framework for Use with Large Language Models (2025.emnlp-main)
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| Challenge: | Existing methods to categorize moral foundations in storytelling are limited. |
| Approach: | They propose a character-centric method to quantify moral foundations in storytelling using large language models and a novel Moral Foundations Character Action Questionnaire to validate their approach against human annotations. |
| Outcome: | The proposed method validates against human annotations and then applies to 2,697 folktales from 55 countries. |
Self-Explaining Hate Speech Detection with Moral Rationales (2026.findings-acl)
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Francielle Vargas, Jackson Trager, Diego Alves, Matteo Guida, Surendrabikram Thapa, Berk Atıl, Daryna Dementieva, Andrew J Smart, Ameeta Agrawal
| Challenge: | Existing models for hate speech detection are opaque and rely on surface-level cues. Existing approaches often encode biases originating from training data and annotation processes. |
| Approach: | They propose a framework that integrates moral rationale supervision into training . they propose SMRA for self-explaining hate speech detection . |
| Outcome: | The proposed framework improves performance across binary hate speech detection and multi-label moral sentiment classification. |
Moral Framing in Politics (MFiP): A new resource and models for moral framing (2025.emnlp-main)
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| Challenge: | Recent studies have focused on detecting moral values in political communication, trying to identify moral frames used by political actors or parties to convey their messages. |
| Approach: | They propose to code German parliamentary debates to identify moral framing and to detect subtle differences in politicians’ moral framming. |
| Outcome: | The proposed model distinguishes between different types of moral frames and includes narrative roles, together with the moral foundations for each frame. |