4 papers
How Deep Is Representational Bias in LLMs? The Cases of Caste and Religion
Agrima Seth, Monojit Choudhary, Sunayana Sitaram +3
Representational bias in large language models (LLMs) has predominantly been measured through single-response interactions and has focused on Global North-centric identities like r…
Fluent but Foreign: Even Regional LLMs Lack Cultural Alignment
Dhruv Agarwal, Anya Shukla, Sunayana Sitaram +1
Large language models (LLMs) are used worldwide, yet exhibit Western cultural tendencies. Many countries are now building ``regional'' or ``sovereign'' LLMs, but it remains unclear…
Uncovering inequalities in new knowledge learning by large language models across different languages
Chenglong Wang, Haoyu Tang, Xiyuan Yang +8
As large language models (LLMs) gradually become integral tools for problem solving in daily life worldwide, understanding linguistic inequality is becoming increasingly important.…
A Multilingual, Culture-First Approach to Addressing Misgendering in LLM Applications
Sunayana Sitaram, Adrian de Wynter, Isobel McCrum +2
Misgendering is the act of referring to someone by a gender that does not match their chosen identity. It marginalizes and undermines a person's sense of self, causing significant…