22 citations · 38 across the 6 of their papers we have counts for
10 papers
Unpacking Large Language Models with Conceptual Consistency
Pritish Sahu, Michael Cogswell, Yunye Gong +1
If a Large Language Model (LLM) answers "yes" to the question "Are mountains tall?" then does it know what a mountain is? Can you rely on it responding correctly or incorrectly to…
Challenges in Procedural Multimodal Machine Comprehension:A Novel Way To Benchmark
Pritish Sahu, Karan Sikka, Ajay Divakaran
We focus on Multimodal Machine Reading Comprehension (M3C) where a model is expected to answer questions based on given passage (or context), and the context and the questions can…
Comprehension Based Question Answering using Bloom's Taxonomy
Pritish Sahu, Michael Cogswell, Sara Rutherford-Quach +1
Current pre-trained language models have lots of knowledge, but a more limited ability to use that knowledge. Bloom's Taxonomy helps educators teach children how to use knowledge b…
Towards Solving Multimodal Comprehension
Pritish Sahu, Karan Sikka, Ajay Divakaran
This paper targets the problem of procedural multimodal machine comprehension (M3C). This task requires an AI to comprehend given steps of multimodal instructions and then answer q…
Zero-Shot Learning with Knowledge Enhanced Visual Semantic Embeddings
Karan Sikka, Jihua Huang, Andrew Silberfarb +6
We improve zero-shot learning (ZSL) by incorporating common-sense knowledge in DNNs. We propose Common-Sense based Neuro-Symbolic Loss (CSNL) that formulates prior knowledge as nov…
Task-Discriminative Domain Alignment for Unsupervised Domain Adaptation
Behnam Gholami, Pritish Sahu, Minyoung Kim +1
Domain Adaptation (DA), the process of effectively adapting task models learned on one domain, the source, to other related but distinct domains, the targets, with no or minimal re…