5 papers
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…
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…
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…
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…
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…