84 citations · 128 across the 9 of their papers we have counts for
12 papers
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…
ForecastTKGQuestions: A Benchmark for Temporal Question Answering and Forecasting over Temporal Knowledge Graphs
Zifeng Ding, Zongyue Li, Ruoxia Qi +8
Question answering over temporal knowledge graphs (TKGQA) has recently found increasing interest. TKGQA requires temporal reasoning techniques to extract the relevant information f…
3D-RETR: End-to-End Single and Multi-View 3D Reconstruction with Transformers
Zai Shi, Zhao Meng, Yiran Xing +2
3D reconstruction aims to reconstruct 3D objects from 2D views. Previous works for 3D reconstruction mainly focus on feature matching between views or using CNNs as backbones. Rece…
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…
Self-Supervised Contrastive Learning with Adversarial Perturbations for Defending Word Substitution-based Attacks
Zhao Meng, Yihan Dong, Mrinmaya Sachan +1
In this paper, we present an approach to improve the robustness of BERT language models against word substitution-based adversarial attacks by leveraging adversarial perturbations…
Towards Robust Graph Contrastive Learning
Nikola Jovanović, Zhao Meng, Lukas Faber +1
We study the problem of adversarially robust self-supervised learning on graphs. In the contrastive learning framework, we introduce a new method that increases the adversarial rob…
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…
Towards Neural Speaker Modeling in Multi-Party Conversation: The Task, Dataset, and Models
Zhao Meng, Lili Mou, Zhi Jin
Neural network-based dialog systems are attracting increasing attention in both academia and industry. Recently, researchers have begun to realize the importance of speaker modelin…
Hierarchical RNN with Static Sentence-Level Attention for Text-Based Speaker Change Detection
Zhao Meng, Lili Mou, Zhi Jin
Speaker change detection (SCD) is an important task in dialog modeling. Our paper addresses the problem of text-based SCD, which differs from existing audio-based studies and is us…
How Transferable are Neural Networks in NLP Applications?
Lili Mou, Zhao Meng, Rui Yan +4
Transfer learning is aimed to make use of valuable knowledge in a source domain to help model performance in a target domain. It is particularly important to neural networks, which…