6 papers
ChildGuard: A Specialized Dataset for Combatting Child-Targeted Hate Speech
Gautam Siddharth Kashyap, Mohammad Anas Azeez, Rafiq Ali +3
Mental health industry faces growing concerns regarding hate speech directed at children's on social media, as exposure to such content can contribute to adverse psychological outc…
Truth, Trust, and Trouble: Medical AI on the Edge
Mohammad Anas Azeez, Rafiq Ali, Ebad Shabbir +4
Large Language Models (LLMs) hold significant promise for transforming digital health by enabling automated medical question answering. However, ensuring these models meet critical…
See Fair, Speak Truth: Equitable Attention Improves Grounding and Reduces Hallucination in Vision-Language Alignment
Mohammad Anas Azeez, Ankan Deria, Zohaib Hasan Siddiqui +5
Multimodal large language models (MLLMs) frequently hallucinate objects that are absent from the visual input, often because attention during decoding is disproportionately drawn t…
LLMs on a Budget? Say HOLA
Zohaib Hasan Siddiqui, Jiechao Gao, Ebad Shabbir +4
Running Large Language Models (LLMs) on edge devices is constrained by high compute and memory demands posing a barrier for real-time applications in sectors like healthcare, educa…
Surgeons Are Indian Males and Speech Therapists Are White Females: Auditing Biases in Vision-Language Models for Healthcare Professionals
Zohaib Hasan Siddiqui, Dayam Nadeem, Mohammad Masudur Rahman +3
Vision language models (VLMs), such as CLIP and OpenCLIP, can encode and reflect stereotypical associations between medical professions and demographic attributes learned from web-…
Can We Predict Your Next Move Without Breaking Your Privacy?
Arpita Soni, Sahil Tripathi, Gautam Siddharth Kashyap +5
We propose FLLL3M--Federated Learning with Large Language Models for Mobility Modeling--a privacy-preserving framework for Next-Location Prediction (NxLP). By retaining user data l…