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Dissecting Bias in LLMs: A Mechanistic Interpretability Perspective
Bhavik Chandna, Zubair Bashir, Procheta Sen
Large Language Models (LLMs) are known to exhibit social, demographic, and gender biases, often as a consequence of the data on which they are trained. In this work, we adopt a mec…
cs.CL2025
XGUARD: A Graded Benchmark for Evaluating Safety Failures of Large Language Models on Extremist Content
Vadivel Abishethvarman, Bhavik Chandna, Pratik Jalan +1
Large Language Models (LLMs) can generate content spanning ideological rhetoric to explicit instructions for violence. However, existing safety evaluations often rely on simplistic…