2 citations · 5 across the 5 of their papers we have counts for
5 papers
SweEval: Do LLMs Really Swear? A Safety Benchmark for Testing Limits for Enterprise Use
Hitesh Laxmichand Patel, Amit Agarwal, Arion Das +6
Enterprise customers are increasingly adopting Large Language Models (LLMs) for critical communication tasks, such as drafting emails, crafting sales pitches, and composing casual…
Clinical QA 2.0: Multi-Task Learning for Answer Extraction and Categorization
Priyaranjan Pattnayak, Hitesh Laxmichand Patel, Amit Agarwal +3
Clinical Question Answering (CQA) plays a crucial role in medical decision-making, enabling physicians to extract relevant information from Electronic Medical Records (EMRs). While…
Survey of Large Multimodal Model Datasets, Application Categories and Taxonomy
Priyaranjan Pattnayak, Hitesh Laxmichand Patel, Bhargava Kumar +4
Multimodal learning, a rapidly evolving field in artificial intelligence, seeks to construct more versatile and robust systems by integrating and analyzing diverse types of data, i…
MVTamperBench: Evaluating Robustness of Vision-Language Models
Amit Agarwal, Srikant Panda, Angeline Charles +8
Multimodal Large Language Models (MLLMs), are recent advancement of Vision-Language Models (VLMs) that have driven major advances in video understanding. However, their vulnerabili…
Enhancing Document AI Data Generation Through Graph-Based Synthetic Layouts
Amit Agarwal, Hitesh Patel, Priyaranjan Pattnayak +3
The development of robust Document AI models has been constrained by limited access to high-quality, labeled datasets, primarily due to data privacy concerns, scarcity, and the hig…