6 papers
A Counterfactual Explanation Framework for Retrieval Models
Bhavik Chandna, Procheta Sen
Explainability has become a crucial concern in today's world, aiming to enhance transparency in machine learning and deep learning models. Information retrieval is no exception to…
The Coupling Within: Flow Matching via Distilled Normalizing Flows
David Berthelot, Tianrong Chen, Jiatao Gu +6
Flow models have rapidly become the go-to method for training and deploying large-scale generators, owing their success to inference-time flexibility via adjustable integration ste…
3DSPA: A 3D Semantic Point Autoencoder for Evaluating Video Realism
Bhavik Chandna, Kelsey R. Allen
AI video generation is evolving rapidly. For video generators to be useful for applications ranging from robotics to film-making, they must consistently produce realistic videos. H…
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…
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…
ExtremeAIGC: Benchmarking LMM Vulnerability to AI-Generated Extremist Content
Bhavik Chandna, Mariam Aboujenane, Usman Naseem
Large Multimodal Models (LMMs) are increasingly vulnerable to AI-generated extremist content, including photorealistic images and text, which can be used to bypass safety mechanism…