1 citations · 3 across the 6 of their papers we have counts for
5 papers
Co-Evolving Agents: Learning from Failures as Hard Negatives
Yeonsung Jung, Trilok Padhi, Sina Shaham +4
The rapid progress of large foundation models has accelerated the development of task-specialized agents across diverse domains. However, the effectiveness of agents remains tightl…
From Reddit to Generative AI: Evaluating Large Language Models for Anxiety Support Fine-tuned on Social Media Data
Ugur Kursuncu, Trilok Padhi, Gaurav Sinha +3
The growing demand for accessible mental health support, compounded by workforce shortages and logistical barriers, has led to increased interest in utilizing Large Language Models…
Calibrating Uncertainty Quantification of Multi-Modal LLMs using Grounding
Trilok Padhi, Ramneet Kaur, Adam D. Cobb +7
We introduce a novel approach for calibrating uncertainty quantification (UQ) tailored for multi-modal large language models (LLMs). Existing state-of-the-art UQ methods rely on co…
Playing Devil's Advocate: Unmasking Toxicity and Vulnerabilities in Large Vision-Language Models
Abdulkadir Erol, Trilok Padhi, Agnik Saha +2
The rapid advancement of Large Vision-Language Models (LVLMs) has enhanced capabilities offering potential applications from content creation to productivity enhancement. Despite t…
Just KIDDIN: Knowledge Infusion and Distillation for Detection of INdecent Memes
Rahul Garg, Trilok Padhi, Hemang Jain +2
Toxicity identification in online multimodal environments remains a challenging task due to the complexity of contextual connections across modalities (e.g., textual and visual). I…