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20232025
most citedResolving Knowledge Conflicts in Large Language Models

6 citations · 23 across the 11 of their papers we have counts for

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Showing 2024Show all

5 papers · 1 filter

cs.CV2024★ 1 cited

Jailbreak Large Vision-Language Models Through Multi-Modal Linkage

Yu Wang, Xiaofei Zhou, Yichen Wang +2

With the significant advancement of Large Vision-Language Models (VLMs), concerns about their potential misuse and abuse have grown rapidly. Previous studies have highlighted VLMs'…

cs.CL2024★ 2 cited

Can LLM Graph Reasoning Generalize beyond Pattern Memorization?

Yizhuo Zhang, Heng Wang, Shangbin Feng +4

Large language models (LLMs) demonstrate great potential for problems with implicit graphical structures, while recent works seek to enhance the graph reasoning capabilities of LLM…

cs.CL2024★ 3 cited

Learning Syntax Without Planting Trees: Understanding Hierarchical Generalization in Transformers

Kabir Ahuja, Vidhisha Balachandran, Madhur Panwar +4

Transformers trained on natural language data have been shown to learn its hierarchical structure and generalize to sentences with unseen syntactic structures without explicitly en…

cs.CL2024★ 2 cited

Stumbling Blocks: Stress Testing the Robustness of Machine-Generated Text Detectors Under Attacks

Yichen Wang, Shangbin Feng, Abe Bohan Hou +5

The widespread use of large language models (LLMs) is increasing the demand for methods that detect machine-generated text to prevent misuse. The goal of our study is to stress tes…

cs.CL2024★ 2 cited

k-SemStamp: A Clustering-Based Semantic Watermark for Detection of Machine-Generated Text

Abe Bohan Hou, Jingyu Zhang, Yichen Wang +2

Recent watermarked generation algorithms inject detectable signatures during language generation to facilitate post-hoc detection. While token-level watermarks are vulnerable to pa…