1 citations · 1 across the 3 of their papers we have counts for
6 papers
Knowing Bias, Doing Better: Mitigating Social Bias in LLMs via Know-Bias Neuron Enhancement
Jinhao Pan, Chahat Raj, Anjishnu Mukherjee +4
Large language models (LLMs) exhibit social biases that reinforce harmful stereotypes, limiting their safe deployment. Most existing debiasing methods adopt a suppressive paradigm…
Purdah and Patriarchy: Evaluating and Mitigating South Asian Biases in Open-Ended Multilingual LLM Generations
Mamnuya Rinki, Chahat Raj, Anjishnu Mukherjee +1
Evaluations of Large Language Models (LLMs) often overlook intersectional and culturally specific biases, particularly in underrepresented multilingual regions like South Asia. Thi…
Lost in the Tower of Babel: The Adverse Effects of Incidental Multilingualism in LLMs
Anjishnu Mukherjee, Chutong Meng, Antonios Anastasopoulos
This paper argues that contemporary multilingual NLP has converged on a fragile and misleading paradigm of incidental multilingualism. Today's LLMs appear multilingual largely beca…
Metadata Conditioned Large Language Models for Localization
Anjishnu Mukherjee, Ziwei Zhu, Antonios Anastasopoulos
Large language models are typically trained by treating text as a single global distribution, often resulting in geographically homogenized behavior. We study metadata conditioning…
Crossroads of Continents: Automated Artifact Extraction for Cultural Adaptation with Large Multimodal Models
Anjishnu Mukherjee, Ziwei Zhu, Antonios Anastasopoulos
We present a comprehensive three-phase study to examine (1) the cultural understanding of Large Multimodal Models (LMMs) by introducing DalleStreet, a large-scale dataset generated…
BiasDora: Exploring Hidden Biased Associations in Vision-Language Models
Chahat Raj, Anjishnu Mukherjee, Aylin Caliskan +2
Existing works examining Vision-Language Models (VLMs) for social biases predominantly focus on a limited set of documented bias associations, such as gender:profession or race:cri…