Dead or Murdered? Predicting Responsibility Perception in Femicide News Reports (2022.aacl-main)
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| Challenge: | linguistic expressions of gender-based violence can conceptualize the same event from different perspectives by emphasizing certain participants over others. |
| Approach: | They conduct a large-scale perception survey of GBV descriptions from italian newspapers and train regression models that predict the salience of GV participants with respect to different dimensions of perceived responsibility. |
| Outcome: | The proposed model shows that salient focus is more predictable than salient blame, and perpetrators’ salience is more predictable than victims’ salient. |
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Responsibility Perspective Transfer for Italian Femicide News (2023.findings-acl)
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| Challenge: | Existing work has shown that different descriptions of gender-based violence influence the reader’s perception of who is to blame for the violence. |
| Approach: | They propose to automatically rewrite GBV descriptions to alter the perceived level of blame on the perpetrator. |
| Outcome: | The proposed task alters perceived responsibility levels for perpetrators by using unsupervised, zero-shot and few-shot methods. |
What social attitudes about gender does BERT encode? Leveraging insights from psycholinguistics (2023.acl-long)
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| Challenge: | Much research has focused on evaluating whether large language models encode stereotypical/harmful associations. |
| Approach: | They propose to use two datasets from human experiments to examine how word preferences in a large language model reflect social attitudes about gender. |
| Outcome: | The language model BERT takes into account factors that shape human lexical choice of such language, but may not weigh those factors in the same way people do. |
Your Mileage May Vary: How Empathy and Demographics Shape Human Preferences in LLM Responses (2025.findings-emnlp)
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| Challenge: | large language models (LLMs) increasingly assist subjective decision-making . prior work uses aggregate human judgments, but demographic variation and its linguistic drivers remain underexplored. |
| Approach: | They analyze how demographic background and empathy level correlate with LLM-generated dilemma responses . they also identify markers that predict group-level differences . |
| Outcome: | The authors show that demographic background and empathy level correlate with LLM preferences . their findings highlight the need for demographically informed LLM evaluations. |
To Protect and To Serve? Analyzing Entity-Centric Framing of Police Violence (2021.findings-emnlp)
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| Challenge: | a new study examines the media coverage of police violence in the United States by examining the framing of 82k news articles spanning 7k police killings. |
| Approach: | They propose an NLP framework to measure entity-centric framing to understand media coverage on police violence in the United States in a new police violence frames corpus of 82k news articles spanning 7k police killings. |
| Outcome: | The proposed framework reveals significant differences in the way liberal and conservative news sources frame both the issue of police violence and the entities involved. |
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 . |
| Approach: | They propose to use unsupervised, supervised, and finally supervised methods to extract emotional associations from pretrained vectors and models. |
| Outcome: | The proposed method shows higher correlation with ground truth ratings than state-of-the-art lexicons based on labeled data. |
Rethinking Research on Stereotypes: An Analysis through Social Psychological and Computational Perspectives (2026.findings-acl)
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| Challenge: | Existing research on stereotypical biases ignores literature on them and results in resource wastage. |
| Approach: | They argue that stereotypes are social constructs shaping human perception and behavior that can produce harmful outcomes under specific conditions. |
| Outcome: | The proposed models can inherit and amplify stereotypes under certain conditions. |
RtGender: A Corpus for Studying Differential Responses to Gender (L18-1)
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| Challenge: | Prior work on linguistic gender difference and communications about gender has focused on language about or portraying persons of a particular gender. |
| Approach: | They present a multi-genre corpus of 25M comments from five socially and topically diverse sources tagged for the gender of the addressee and 30k annotations for sentiment and relevance of these responses. |
| Outcome: | The proposed dataset shows that responses to women are more emotive and about the speaker as an individual (rather than about the content being responded to). |
Re-examining Sexism and Misogyny Classification with Annotator Attitudes (2024.findings-emnlp)
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| Challenge: | Existing datasets for content moderation fail to capture plurality of possible annotator perspectives or ensure representation of affected groups. |
| Approach: | They examine the relationship between annotator identities and attitudes and the responses they give to two GBV labelling tasks. |
| Outcome: | The results show that higher Right Wing Authoritarianism scores are associated with a higher propensity to label text as sexist . higher scores are also associated with negative attitudes towards sexism and neosexist attitudes . |
Gender Bias in Decision-Making with Large Language Models: A Study of Relationship Conflicts (2024.findings-emnlp)
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| Challenge: | Large language models acquire beliefs about gender from training data and can therefore generate text with stereotypical gender attitudes. |
| Approach: | They use a decision-making lens to examine gender equity within large language models . they explore relationships through typical and gender-neutral names . |
| Outcome: | The proposed model generation and classification models exhibit stereotypical gender biases . the proposed model generates gender-neutral names, with and without safety enhancements, and egalitarian versus traditional scenarios across topics. |
A Psycholinguistic Evaluation of Language Models’ Sensitivity to Argument Roles (2024.findings-emnlp)
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| Challenge: | a systematic evaluation of large language models' sensitivity to argument roles is presented . a recent study shows that argument roles have a delayed impact on verb prediction in human sentence processing. |
| Approach: | They propose to replicate psycholinguistic studies on human argument role processing . they find that language models are able to distinguish verbs that appear in plausible and implausible contexts . |
| Outcome: | The proposed models are able to distinguish verbs that appear in plausible and implausible contexts, but none captures the same selective patterns that human comprehenders exhibit during real-time verb prediction. |