605 citations · 4k across the 143 of their papers we have counts for
165 papers
Universal Pyramid Adversarial Training for Improved ViT Performance
Ping-yeh Chiang, Yipin Zhou, Omid Poursaeed +4
Recently, Pyramid Adversarial training (Herrmann et al., 2022) has been shown to be very effective for improving clean accuracy and distribution-shift robustness of vision transfor…
Seeing in Words: Learning to Classify through Language Bottlenecks
Khalid Saifullah, Yuxin Wen, Jonas Geiping +2
Neural networks for computer vision extract uninterpretable features despite achieving high accuracy on benchmarks. In contrast, humans can explain their predictions using succinct…
Bring Your Own Data! Self-Supervised Evaluation for Large Language Models
Neel Jain, Khalid Saifullah, Yuxin Wen +6
With the rise of Large Language Models (LLMs) and their ubiquitous deployment in diverse domains, measuring language model behavior on realistic data is imperative. For example, a…
On the Exploitability of Instruction Tuning
Manli Shu, Jiongxiao Wang, Chen Zhu +3
Instruction tuning is an effective technique to align large language models (LLMs) with human intents. In this work, we investigate how an adversary can exploit instruction tuning…
On the Reliability of Watermarks for Large Language Models
John Kirchenbauer, Jonas Geiping, Yuxin Wen +7
As LLMs become commonplace, machine-generated text has the potential to flood the internet with spam, social media bots, and valueless content. Watermarking is a simple and effecti…
InstructZero: Efficient Instruction Optimization for Black-Box Large Language Models
Lichang Chen, Jiuhai Chen, Tom Goldstein +2
Large language models~(LLMs) are instruction followers, but it can be challenging to find the best instruction for different situations, especially for black-box LLMs on which back…