Research map / Large language models / AI and society
Building Socio-culturally Inclusive Stereotype Resources with Community Engagement
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add SPICE regional stereotypes to existing regional lists | BART · NLI regional stereotype eval | % entailed: 9.1 -> 11.5 | Helped |
| add SPICE regional stereotypes | RoBERTa · NLI regional stereotype eval | % entailed: 6.2 -> 10.7 | Helped |
| add SPICE regional stereotypes | XLNET · NLI regional stereotype eval | % entailed: 7.5 -> 16.0 | Helped |
| add SPICE regional stereotypes | ELECTRA · NLI regional stereotype eval | % entailed: 8.4 -> 14.0 | Helped |
Closest papers by meaning
- StereoTales: A Multilingual Framework for Open-Ended Stereotype Discovery in LLMs (NeurIPS 2026 · similarity 0.88)
- Bias and Volatility: A Statistical Framework for Evaluating Large Language Model's Stereotypes and the Associated Generation Inconsistency (NeurIPS 2024 · similarity 0.86)
- SocialStigmaQA: A Benchmark to Uncover Stigma Amplification in Generative Language Models (AAAI 2024 · similarity 0.85)
- Culture Affordance Atlas: Reconciling Object Diversity Through Functional Mapping (AAAI 2026 · similarity 0.84)
- Large Language Models are Geographically Biased (ICML 2024 · similarity 0.84)
- OASIS Uncovers: High-Quality T2I Models, Same Old Stereotypes (ICLR 2025 · similarity 0.84)
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