3 papers
cs.CV2026
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…
cs.LG2025
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…
cs.LG2025
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…