Feeling Rules in Language Models: Mapping Norms of Emotional Appropriateness Across Roles, Institutions, and Intensity (2026.acl-long)
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| Challenge: | Existing benchmarks measure whether Large language models recognize emotions . authors: LLMs can be used to validate, but they can still judge anger inappropriately . |
| Approach: | They propose a benchmark to measure whether Large language models validate anger . they use explicit norm judgments and implicit acceptability tests to measure norms . |
| Outcome: | The study finds that large differences in sanctioning thresholds and institutional norm signatures are not reducible to overall strictness. |
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| Challenge: | Language models (LMs) are used in decision-making systems and as interactive assistants. |
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Guilt by Association: Emotion Intensities in Lexical Representations (2021.emnlp-main)
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| Challenge: | linguistic models have a higher correlation with human ground truth ratings than labeled data . word vectors have often been evaluated on standard word relatedness benchmarks . |
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NormAd: A Framework for Measuring the Cultural Adaptability of Large Language Models (2025.naacl-long)
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| Challenge: | Large language models (LLMs) are widely used and engage millions of users from diverse contexts and cultures. |
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Whose Emotions and Moral Sentiments do Language Models Reflect? (2024.findings-acl)
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| Challenge: | Existing research has focused on positional alignment, which measures how closely the models mimic the opinions and stances of different social groups. |
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Do Emotions Influence Moral Judgment in Large Language Models? (2026.findings-acl)
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| Challenge: | Recent systems enforce explicit ethical constraints, but moral judgment rarely involves such clear-cut prohibitions. |
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| Challenge: | Our work provides preliminary guidelines and highlights the nuances of applying Large Language models in culturally sensitive cases. |
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| Challenge: | Several studies have examined whether large language models exhibit bias or discrimination against individuals or groups in terms of protected attributes like race, gender, or religion. |
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A Study of Implicit Ranking Unfairness in Large Language Models (2024.findings-emnlp)
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| Challenge: | Large language models (LLMs) have demonstrated superior ability to serve as ranking models, but they will exhibit discriminatory ranking behaviors based on users’ sensitive attributes (gender). |
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| Challenge: | Autoregressive Language Models (ARLMs) partially mitigate these patterns, while closed-access ARLMs tend to produce more harmful outputs for unmarked subjects. |
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