Agarwal, Chirag

Evaluating explainability for graph neural networks

Agarwal, C., Queen, O., Lakkaraju, H., & Zitnik, M. (2023) · Scientific Data

Explainable Artificial Intelligence (XAI)Advanced Graph Neural Networks

Cited by 186

DeAR: Debiasing Vision-Language Models with Additive Residuals

Seth, A., Hemani, M., & Agarwal, C. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Ethics and Social Impacts of AIMultimodal Machine Learning Applications

Cited by 34

Certifying LLM Safety against Adversarial Prompting

Kumar, A., Agarwal, C., Srinivas, S., Li, A. J., Feizi, S., & Lakkaraju, H. (2023) · arXiv

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 28

Faithfulness vs. Plausibility: On the (Un)Reliability of Explanations from Large Language Models

Agarwal, C., Tanneru, S. H., & Lakkaraju, H. (2024) · arXiv

Explainable Artificial Intelligence (XAI)Topic Modeling

Cited by 14

Improving Human Verification of LLM Reasoning through Interactive Explanation Interfaces

Zhou, R., Nguyen, G., Kharya, N., Nguyen, A., & Agarwal, C. (2026) · Proceedings of the 31st International Conference on Intelligent User Interfaces

Explainable Artificial Intelligence (XAI)Intelligent Tutoring Systems and Adaptive Learning

Cited by 2

On the Hardness of Faithful Chain-of-Thought Reasoning in Large Language Models

Tanneru, S. H., Ley, D., Agarwal, C., & Lakkaraju, H. (2024) · arXiv

Explainable Artificial Intelligence (XAI)Topic Modeling

Cited by 1