11 citations · 15 across the 7 of their papers we have counts for
7 papers
Agentic AI Security: Threats, Defenses, Evaluation, and Open Challenges
Anshuman Chhabra, Shrestha Datta, Shahriar Kabir Nahin +1
Agentic AI systems powered by large language models (LLMs) and endowed with planning, tool use, memory, and autonomy, are emerging as powerful, flexible platforms for automation. T…
RF Sensing Security and Malicious Exploitation: A Comprehensive Survey
Mingda Han, Huanqi Yang, Wenhao Li +4
Radio Frequency (RF) sensing technologies have experienced significant growth due to the widespread adoption of RF devices and the Internet of Things (IoT). These technologies enab…
Assessing LLMs for Zero-shot Abstractive Summarization Through the Lens of Relevance Paraphrasing
Hadi Askari, Anshuman Chhabra, Muhao Chen +1
Large Language Models (LLMs) have achieved state-of-the-art performance at zero-shot generation of abstractive summaries for given articles. However, little is known about the robu…
Stability of Explainable Recommendation
Sairamvinay Vijayaraghavan, Prasant Mohapatra
Explainable Recommendation has been gaining attention over the last few years in industry and academia. Explanations provided along with recommendations in a recommender system fra…
Outlier Gradient Analysis: Efficiently Identifying Detrimental Training Samples for Deep Learning Models
Anshuman Chhabra, Bo Li, Jian Chen +2
A core data-centric learning challenge is the identification of training samples that are detrimental to model performance. Influence functions serve as a prominent tool for this t…
Robust Explainable Recommendation
Sairamvinay Vijayaraghavan, Prasant Mohapatra
Explainable Recommender Systems is an important field of study which provides reasons behind the suggested recommendations. Explanations with recommender systems are useful for dev…
Revisiting Zero-Shot Abstractive Summarization in the Era of Large Language Models from the Perspective of Position Bias
Anshuman Chhabra, Hadi Askari, Prasant Mohapatra
We characterize and study zero-shot abstractive summarization in Large Language Models (LLMs) by measuring position bias, which we propose as a general formulation of the more rest…