Chirag Agarwal
22 papers · 2020–2026 · 10 conferences · across top CS/AI conferences
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Keywords
large language model
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benchmark evaluation
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vision language model
(2)
multilingual reasoning
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vision-language model
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graph neural network
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theoretical analysis
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hallucination detection
(2)
hallucination benchmark
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curriculum learning
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multimodal learning
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chain-of-thought reasoning
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bias detection
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video understanding
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natural language processing
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catastrophic forgetting
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model robustness
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ai safety
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visual question answering
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Papers
Towards Trustworthy Multimodal AI Systems
AAAI 2026
Polarity-Aware Probing for Quantifying Latent Alignment in Language Models
AAAI 2026
CURE-Med: Curriculum-Informed Reinforcement Learning for Multilingual Medical Reasoning
ACL 2026
Analyzing Memorization in Large Language Models through the Lens of Model Attribution
NAACL 2025
Towards Operationalizing Right to Data Protection
NAACL 2025
EGOILLUSION: Benchmarking Hallucinations in Egocentric Video Understanding
EMNLP 2025
A Survey of Multilingual Reasoning in Language Models
EMNLP 2025
HALLUCINOGEN: Benchmarking Hallucination in Implicit Reasoning within Large Vision Language Models
EMNLP 2025
On the Impact of Fine-Tuning on Chain-of-Thought Reasoning
NAACL 2025
Quantifying Uncertainty in Natural Language Explanations of Large Language Models
AISTATS 2024
Understanding the Effects of Iterative Prompting on Truthfulness
ICML 2024
MedSafetyBench: Evaluating and Improving the Medical Safety of Large Language Models
NIPS 2024
Explaining RL Decisions with Trajectories
ICLR 2023
DeAR: Debiasing Vision-Language Models With Additive Residuals
CVPR 2023
GNNDelete: A General Strategy for Unlearning in Graph Neural Networks
ICLR 2023
Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis
AISTATS 2022
Probing GNN Explainers: A Rigorous Theoretical and Empirical Analysis of GNN Explanation Methods
AISTATS 2022
Estimating Example Difficulty Using Variance of Gradients
CVPR 2022
OpenXAI: Towards a Transparent Evaluation of Model Explanations
NIPS 2022
Towards a unified framework for fair and stable graph representation learning
UAI 2021
Towards the Unification and Robustness of Perturbation and Gradient Based Explanations
ICML 2021
SAM: The Sensitivity of Attribution Methods to Hyperparameters
CVPR 2020