7 citations · 12 across the 3 of their papers we have counts for
6 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…
Improving Users' Mental Model with Attention-directed Counterfactual Edits
Kamran Alipour, Arijit Ray, Xiao Lin +4
In the domain of Visual Question Answering (VQA), studies have shown improvement in users' mental model of the VQA system when they are exposed to examples of how these systems ans…
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…
Generating and Evaluating Explanations of Attended and Error-Inducing Input Regions for VQA Models
Arijit Ray, Michael Cogswell, Xiao Lin +4
Attention maps, a popular heatmap-based explanation method for Visual Question Answering (VQA), are supposed to help users understand the model by highlighting portions of the imag…
Dialog without Dialog Data: Learning Visual Dialog Agents from VQA Data
Michael Cogswell, Jiasen Lu, Rishabh Jain +3
Can we develop visually grounded dialog agents that can efficiently adapt to new tasks without forgetting how to talk to people? Such agents could leverage a larger variety of exis…
Emergence of Compositional Language with Deep Generational Transmission
Michael Cogswell, Jiasen Lu, Stefan Lee +2
Recent work has studied the emergence of language among deep reinforcement learning agents that must collaborate to solve a task. Of particular interest are the factors that cause…