1 citations · 2 across the 10 of their papers we have counts for
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How Reasoning Influences Intersectional Biases in Vision Language Models
Adit Desai, Sudipta Roy, Mohna Chakraborty
Vision Language Models (VLMs) are increasingly deployed across downstream tasks, yet their training data often encode social biases that surface in outputs. Unlike humans, who inte…
MuGa-VTON: Multi-Garment Virtual Try-On via Diffusion Transformers with Prompt Customization
Ankan Deria, Dwarikanath Mahapatra, Behzad Bozorgtabar +3
Virtual try-on seeks to generate photorealistic images of individuals in desired garments, a task that must simultaneously preserve personal identity and garment fidelity for pract…
Is Large Language Model Performance on Reasoning Tasks Impacted by Different Ways Questions Are Asked?
Seok Hwan Song, Mohna Chakraborty, Qi Li +1
Large Language Models (LLMs) have been evaluated using diverse question types, e.g., multiple-choice, true/false, and short/long answers. This study answers an unexplored question…
Modeling Data Diversity for Joint Instance and Verbalizer Selection in Cold-Start Scenarios
Mohna Chakraborty, Adithya Kulkarni, Qi Li
Prompt-based methods leverage the knowledge of pre-trained language models (PLMs) trained with a masked language modeling (MLM) objective; however, these methods are sensitive to t…
Structured Moral Reasoning in Language Models: A Value-Grounded Evaluation Framework
Mohna Chakraborty, Lu Wang, David Jurgens
Large language models (LLMs) are increasingly deployed in domains requiring moral understanding, yet their reasoning often remains shallow, and misaligned with human reasoning. Unl…