no code implementations • 29 Apr 2024 • Utkarsh Agarwal, Kumar Tanmay, Aditi Khandelwal, Monojit Choudhury
Ethical reasoning is a crucial skill for Large Language Models (LLMs).
no code implementations • 3 Feb 2024 • Aditi Khandelwal, Utkarsh Agarwal, Kumar Tanmay, Monojit Choudhury
This paper explores the moral judgment and moral reasoning abilities exhibited by Large Language Models (LLMs) across languages through the Defining Issues Test.
no code implementations • 11 Oct 2023 • Abhinav Rao, Aditi Khandelwal, Kumar Tanmay, Utkarsh Agarwal, Monojit Choudhury
In this position paper, we argue that instead of morally aligning LLMs to specific set of ethical principles, we should infuse generic ethical reasoning capabilities into them so that they can handle value pluralism at a global scale.
no code implementations • 23 Sep 2023 • Kumar Tanmay, Aditi Khandelwal, Utkarsh Agarwal, Monojit Choudhury
In this study, we measure the moral reasoning ability of LLMs using the Defining Issues Test - a psychometric instrument developed for measuring the moral development stage of a person according to the Kohlberg's Cognitive Moral Development Model.
no code implementations • 23 May 2023 • Kriti Aggarwal, Aditi Khandelwal, Kumar Tanmay, Owais Mohammed Khan, Qiang Liu, Monojit Choudhury, Hardik Hansrajbhai Chauhan, Subhojit Som, Vishrav Chaudhary, Saurabh Tiwary
Visual document understanding is a complex task that involves analyzing both the text and the visual elements in document images.
Ranked #1 on Visual Question Answering (VQA) on DeepForm
no code implementations • 20 Nov 2020 • Shuvam Chakraborty, Burak Uzkent, Kumar Ayush, Kumar Tanmay, Evan Sheehan, Stefano Ermon
Finally, we improve standard ImageNet pre-training by 1-3% by tuning available models on our subsets and pre-training on a dataset filtered from a larger scale dataset.
1 code implementation • ICCV 2021 • Kumar Ayush, Burak Uzkent, Chenlin Meng, Kumar Tanmay, Marshall Burke, David Lobell, Stefano Ermon
Contrastive learning methods have significantly narrowed the gap between supervised and unsupervised learning on computer vision tasks.
Ranked #5 on Semantic Segmentation on SpaceNet 1 (using extra training data)
no code implementations • 7 Jun 2020 • Kumar Ayush, Burak Uzkent, Kumar Tanmay, Marshall Burke, David Lobell, Stefano Ermon
The combination of high-resolution satellite imagery and machine learning have proven useful in many sustainability-related tasks, including poverty prediction, infrastructure measurement, and forest monitoring.