activity
20232025
most citedSocialStigmaQA: A Benchmark to Uncover Stigma Amplification in Generative Language Models

2 citations · 2 across the 2 of their papers we have counts for

collaborators

5 papers

cs.CL2025

DECASTE: Unveiling Caste Stereotypes in Large Language Models through Multi-Dimensional Bias Analysis

Prashanth Vijayaraghavan, Soroush Vosoughi, Lamogha Chiazor +4

Recent advancements in large language models (LLMs) have revolutionized natural language processing (NLP) and expanded their applications across diverse domains. However, despite t…

cs.CL2024

Epistemological Bias As a Means for the Automated Detection of Injustices in Text

Kenya Andrews, Lamogha Chiazor

Injustices in text are often subtle since implicit biases or stereotypes frequently operate unconsciously due to the pervasive nature of prejudice in society. This makes automated…

cs.LG2024

Detectors for Safe and Reliable LLMs: Implementations, Uses, and Limitations

Swapnaja Achintalwar, Adriana Alvarado Garcia, Ateret Anaby-Tavor +35

Large language models (LLMs) are susceptible to a variety of risks, from non-faithful output to biased and toxic generations. Due to several limiting factors surrounding LLMs (trai…

cs.CL20232 cited

SocialStigmaQA: A Benchmark to Uncover Stigma Amplification in Generative Language Models

Manish Nagireddy, Lamogha Chiazor, Moninder Singh +1

Current datasets for unwanted social bias auditing are limited to studying protected demographic features such as race and gender. In this work, we introduce a comprehensive benchm…

cs.CR2023

Cybersecurity in Motion: A Survey of Challenges and Requirements for Future Test Facilities of CAVs

Ioannis Mavromatis, Theodoros Spyridopoulos, Pietro Carnelli +12

The way we travel is changing rapidly, and Cooperative Intelligent Transportation Systems (C-ITSs) are at the forefront of this evolution. However, the adoption of C-ITSs introduce…