most citedGenerating Valid and Natural Adversarial Examples with Large Language Models

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL2024

Domain-specific Guided Summarization for Mental Health Posts

Lu Qian, Yuqi Wang, Zimu Wang +4

In domain-specific contexts, particularly mental health, abstractive summarization requires advanced techniques adept at handling specialized content to generate domain-relevant an…

cs.CV2024

Interpret Your Decision: Logical Reasoning Regularization for Generalization in Visual Classification

Zhaorui Tan, Xi Yang, Qiufeng Wang +2

Vision models excel in image classification but struggle to generalize to unseen data, such as classifying images from unseen domains or discovering novel categories. In this paper…

cs.CL2024

DKE-Research at SemEval-2024 Task 2: Incorporating Data Augmentation with Generative Models and Biomedical Knowledge to Enhance Inference Robustness

Yuqi Wang, Zeqiang Wang, Wei Wang +4

Safe and reliable natural language inference is critical for extracting insights from clinical trial reports but poses challenges due to biases in large pre-trained language models…

cs.CL2023★ 1 cited

Generating Valid and Natural Adversarial Examples with Large Language Models

Zimu Wang, Wei Wang, Qi Chen +2

Deep learning-based natural language processing (NLP) models, particularly pre-trained language models (PLMs), have been revealed to be vulnerable to adversarial attacks. However,…

cs.CL2023

Zero-Shot Medical Information Retrieval via Knowledge Graph Embedding

Yuqi Wang, Zeqiang Wang, Wei Wang +4

In the era of the Internet of Things (IoT), the retrieval of relevant medical information has become essential for efficient clinical decision-making. This paper introduces MedFusi…