7 citations · 12 across the 4 of their papers we have counts for
4 papers · 1 filter
SeeGULL Multilingual: a Dataset of Geo-Culturally Situated Stereotypes
Mukul Bhutani, Kevin Robinson, Vinodkumar Prabhakaran +2
While generative multilingual models are rapidly being deployed, their safety and fairness evaluations are largely limited to resources collected in English. This is especially pro…
Building Socio-culturally Inclusive Stereotype Resources with Community Engagement
Sunipa Dev, Jaya Goyal, Dinesh Tewari +2
With rapid development and deployment of generative language models in global settings, there is an urgent need to also scale our measurements of harm, not just in the number and t…
SeeGULL: A Stereotype Benchmark with Broad Geo-Cultural Coverage Leveraging Generative Models
Akshita Jha, Aida Davani, Chandan K. Reddy +3
Stereotype benchmark datasets are crucial to detect and mitigate social stereotypes about groups of people in NLP models. However, existing datasets are limited in size and coverag…
Re-contextualizing Fairness in NLP: The Case of India
Shaily Bhatt, Sunipa Dev, Partha Talukdar +2
Recent research has revealed undesirable biases in NLP data and models. However, these efforts focus on social disparities in West, and are not directly portable to other geo-cultu…