Search Results for author:
Found 2 papers, 2 papers with code
IITK at SemEval-2024 Task 2: Exploring the Capabilities of LLMs for Safe Biomedical Natural Language Inference for Clinical Trials
Large Language models (LLMs) have demonstrated state-of-the-art performance in various natural language processing (NLP) tasks across multiple domains, yet they are prone to shortcut learning and factual inconsistencies.
Defense Against Adversarial Attacks using Convolutional Auto-Encoders
Deep learning models, while achieving state-of-the-art performance on many tasks, are susceptible to adversarial attacks that exploit inherent vulnerabilities in their architectures.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
