Papers with social psychology
A Comparative Study of Explicit and Implicit Gender Biases in Large Language Models via Self-evaluation (2024.lrec-main)
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| Challenge: | Existing studies on the explicit and implicit biases in large language models (LLMs) focus on either explicit or implicit bias. |
| Approach: | They propose a self-evaluation-based two-stage measurement of explicit and implicit biases within large language models grounded in social psychology. |
| Outcome: | The proposed model is based on two stages of self-evaluation on state-of-the-art LLMs to measure explicit bias toward social targets, where bias is less likely to be self-recognized by the LLM. |
A Multilingual Dataset of Racial Stereotypes in Social Media Conversational Threads (2023.findings-eacl)
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Tom Bourgeade, Alessandra Teresa Cignarella, Simona Frenda, Mario Laurent, Wolfgang Schmeisser-Nieto, Farah Benamara, Cristina Bosco, Véronique Moriceau, Viviana Patti, Mariona Taulé
| Challenge: | a new corpus-based study addresses racial stereotypes in social media conversations . a multilingual corpus of rhs is used to investigate how they are spread . |
| Approach: | They propose a corpus-based method for multilingual racial stereotype identification in social media conversational threads. |
| Outcome: | The proposed method sheds light on how racial hoaxes are spread and allows identification of negative stereotypes that reinforce them. |
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. |
Exploring the Impact of Personality Traits on LLM Toxicity and Bias (2025.emnlp-main)
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| Challenge: | anthropomorphic LLMs are being developed to serve diversified roles, but content safety concerns remain regarding their toxicity and toxicity. |
| Approach: | They propose to assign personality traits to large language models (LLMs) to reduce toxic language and social biases in their outputs by using the widely accepted HEXACO personality framework developed in social psychology. |
| Outcome: | The proposed model is able to perform on three toxic and bias benchmarks and shows that assigning personality traits reduces bias and toxicity similar to humans’ correlations between personality traits and toxic behaviors. |
StereoDetect: Detecting Stereotypes and Anti-stereotypes the Correct Way Using Social Psychological Underpinnings (2025.findings-emnlp)
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| Challenge: | Stereotypes are known to have harmful effects, making their detection critical . current research focuses on detecting and evaluating stereotypical biases . |
| Approach: | They propose a five-tuple definition and provide precise terminologies disentangling stereotypes, antistereotypes, stereotypical bias, and general bias. |
| Outcome: | The proposed framework disentangles stereotypes, antistereotypes, stereotypical bias, and general bias. |
Social-Group-Agnostic Bias Mitigation via the Stereotype Content Model (2023.acl-long)
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Ali Omrani, Alireza Salkhordeh Ziabari, Charles Yu, Preni Golazizian, Brendan Kennedy, Mohammad Atari, Heng Ji, Morteza Dehghani
| Challenge: | Existing methods for mitigating bias require social-group-specific word pairs for each social attribute (e.g., gender) Existing approaches require only one social attribute, rendering them impractical and costly . |
| Approach: | They propose that stereotype content models capture the underlying connection between bias and stereotypes by embedding only two psychological dimensions of warmth and competence. |
| Outcome: | The proposed method performs comparably to group-specific debiasing on multiple bias benchmarks, but has theoretical and practical advantages over existing methods. |
Quantifying Intimacy in Language (2020.emnlp-main)
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| Challenge: | Intimacy is a fundamental aspect of how we relate to others in social settings. |
| Approach: | They propose a computational framework for studying the intimacy in language with a dataset and a deep learning model for accurately predicting the intimacy level of questions. |
| Outcome: | The proposed framework enables the analysis of 80.5M questions across social media, books, and films to quantify the intimacy expressed in language and to predict the intimacy level. |