StereoTales shows that all tested LLMs emit harmful stereotypes in open-ended stories, with associations adapting to prompt language and targeting locally salient groups rather than transferring uniformly across languages.
Zhao Liu, Tian Xie, and Xueru Zhang
6 Pith papers cite this work. Polarity classification is still indexing.
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SCOPE is a new large-scale dataset of counterfactual prompt pairs for evaluating fairness and stereotype sensitivity in LLMs across 1,438 topics, nine bias dimensions, 1,536 groups, and four communicative intents.
FairNVT injects calibrated noise into sensitive embeddings of transformer encoders to jointly improve representation-level and prediction-level fairness metrics without degrading task performance.
Recruiters perceive themselves as retaining agency over GenAI in hiring pipelines, yet GenAI invisibly architects core evaluation inputs, producing only marginal efficiency gains at the cost of deskilling.
LLMs are more accurate when answers match stereotypes in clear contexts, especially for race-gender combinations, and no tested model shows consistent fairness or reliability across intersectional groups.
A systematic review of memory designs, evaluation methods, applications, limitations, and future directions for LLM-based agents.
citing papers explorer
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StereoTales: A Multilingual Framework for Open-Ended Stereotype Discovery in LLMs
StereoTales shows that all tested LLMs emit harmful stereotypes in open-ended stories, with associations adapting to prompt language and targeting locally salient groups rather than transferring uniformly across languages.
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SCOPE: A Dataset of Stereotyped Prompts for Counterfactual Fairness Assessment of LLMs
SCOPE is a new large-scale dataset of counterfactual prompt pairs for evaluating fairness and stereotype sensitivity in LLMs across 1,438 topics, nine bias dimensions, 1,536 groups, and four communicative intents.
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FairNVT: Improving Fairness via Noise Injection in Vision Transformers
FairNVT injects calibrated noise into sensitive embeddings of transformer encoders to jointly improve representation-level and prediction-level fairness metrics without degrading task performance.
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Resume-ing Control: (Mis)Perceptions of Agency Around GenAI Use in Recruiting Workflows
Recruiters perceive themselves as retaining agency over GenAI in hiring pipelines, yet GenAI invisibly architects core evaluation inputs, producing only marginal efficiency gains at the cost of deskilling.
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Intersectional Fairness in Large Language Models
LLMs are more accurate when answers match stereotypes in clear contexts, especially for race-gender combinations, and no tested model shows consistent fairness or reliability across intersectional groups.
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A Survey on the Memory Mechanism of Large Language Model based Agents
A systematic review of memory designs, evaluation methods, applications, limitations, and future directions for LLM-based agents.