28 citations · 29 across the 2 of their papers we have counts for
5 papers · 1 filter
Assessing Adversarial Robustness of Large Language Models: An Empirical Study
Zeyu Yang, Zhao Meng, Xiaochen Zheng +1
Large Language Models (LLMs) have revolutionized natural language processing, but their robustness against adversarial attacks remains a critical concern. We presents a novel white…
Beyond Prompting: Making Pre-trained Language Models Better Zero-shot Learners by Clustering Representations
Yu Fei, Ping Nie, Zhao Meng +2
Recent work has demonstrated that pre-trained language models (PLMs) are zero-shot learners. However, most existing zero-shot methods involve heavy human engineering or complicated…
BERT is Robust! A Case Against Synonym-Based Adversarial Examples in Text Classification
Jens Hauser, Zhao Meng, Damián Pascual +1
Deep Neural Networks have taken Natural Language Processing by storm. While this led to incredible improvements across many tasks, it also initiated a new research field, questioni…
KM-BART: Knowledge Enhanced Multimodal BART for Visual Commonsense Generation
Yiran Xing, Zai Shi, Zhao Meng +3
We present Knowledge Enhanced Multimodal BART (KM-BART), which is a Transformer-based sequence-to-sequence model capable of reasoning about commonsense knowledge from multimodal in…
A Geometry-Inspired Attack for Generating Natural Language Adversarial Examples
Zhao Meng, Roger Wattenhofer
Generating adversarial examples for natural language is hard, as natural language consists of discrete symbols, and examples are often of variable lengths. In this paper, we propos…