most citedCan LLMs be Fooled? Investigating Vulnerabilities in LLMs

2 citations · 2 across the 1 of their papers we have counts for

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3 papers

cs.LG2024★ 2 cited

Can LLMs be Fooled? Investigating Vulnerabilities in LLMs

Sara Abdali, Jia He, CJ Barberan +1

The advent of Large Language Models (LLMs) has garnered significant popularity and wielded immense power across various domains within Natural Language Processing (NLP). While thei…

cs.CR2024

Securing Large Language Models: Threats, Vulnerabilities and Responsible Practices

Sara Abdali, Richard Anarfi, CJ Barberan +2

Large language models (LLMs) have significantly transformed the landscape of Natural Language Processing (NLP). Their impact extends across a diverse spectrum of tasks, revolutioni…

cs.CL2024

Decoding the AI Pen: Techniques and Challenges in Detecting AI-Generated Text

Sara Abdali, Richard Anarfi, CJ Barberan +1

Large Language Models (LLMs) have revolutionized the field of Natural Language Generation (NLG) by demonstrating an impressive ability to generate human-like text. However, their w…