1 citations · 1 across the 8 of their papers we have counts for
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Amplification Effects in Test-Time Reinforcement Learning: Safety and Reasoning Vulnerabilities
Vanshaj Khattar, Md Rafi ur Rashid, Moumita Choudhury +4
Test-time training (TTT) has recently emerged as a promising method to improve the reasoning abilities of large language models (LLMs), in which the model directly learns from test…
From Insight to Exploit: Leveraging LLM Collaboration for Adaptive Adversarial Text Generation
Najrin Sultana, Md Rafi Ur Rashid, Kang Gu +1
LLMs can provide substantial zero-shot performance on diverse tasks using a simple task prompt, eliminating the need for training or fine-tuning. However, when applying these model…
Smoothed Embeddings for Robust Language Models
Ryo Hase, Md Rafi Ur Rashid, Ashley Lewis +4
Improving the safety and reliability of large language models (LLMs) is a crucial aspect of realizing trustworthy AI systems. Although alignment methods aim to suppress harmful con…
Forget to Flourish: Leveraging Machine-Unlearning on Pretrained Language Models for Privacy Leakage
Md Rafi Ur Rashid, Jing Liu, Toshiaki Koike-Akino +2
Fine-tuning large language models on private data for downstream applications poses significant privacy risks in potentially exposing sensitive information. Several popular communi…
Second-Order Information Matters: Revisiting Machine Unlearning for Large Language Models
Kang Gu, Md Rafi Ur Rashid, Najrin Sultana +1
With the rapid development of Large Language Models (LLMs), we have witnessed intense competition among the major LLM products like ChatGPT, LLaMa, and Gemini. However, various iss…