no code implementations • 9 Jan 2024 • Negar Maleki, Balaji Padmanabhan, Kaushik Dutta
As large language models continue to advance in Artificial Intelligence (AI), text generation systems have been shown to suffer from a problematic phenomenon termed often as "hallucination."
no code implementations • 14 Apr 2023 • Arindam Ray, Balaji Padmanabhan, Lina Bouayad
This paper develops formalisms for firm versus systemic fairness, and calls for a greater focus in the algorithmic fairness literature on ecosystem-wide fairness - or more simply systemic fairness - in real-world contexts.
no code implementations • 26 Oct 2022 • Rouzbeh Behnia, Mohamamdreza Ebrahimi, Jason Pacheco, Balaji Padmanabhan
Differential privacy (DP) provides a rigorous framework that allows adding noise in the process of training or fine-tuning LLMs such that extracting the training data becomes infeasible (i. e., with a cryptographically small success probability).
no code implementations • 25 Dec 2020 • Yingfei Wang, Inbal Yahav, Balaji Padmanabhan
This paper presents methods to choose individuals to test for infection during a pandemic such as COVID-19, characterized by high contagion and presence of asymptomatic carriers.
no code implementations • 7 Oct 2020 • Sagar Samtani, Hongyi Zhu, Balaji Padmanabhan, Yidong Chai, Hsinchun Chen
Related to this broader goal, this paper makes five timely contributions.