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20242026
most citedJoPA:Explaining Large Language Model's Generation via Joint Prompt Attribution

4 citations · 7 across the 14 of their papers we have counts for

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cs.LG2024

Adversarially Robust Industrial Anomaly Detection Through Diffusion Model

Yuanpu Cao, Lu Lin, Jinghui Chen

Deep learning-based industrial anomaly detection models have achieved remarkably high accuracy on commonly used benchmark datasets. However, the robustness of those models may not…

cs.CL2024★ 1 cited

Personalized Steering of Large Language Models: Versatile Steering Vectors Through Bi-directional Preference Optimization

Yuanpu Cao, Tianrong Zhang, Bochuan Cao +4

Researchers have been studying approaches to steer the behavior of Large Language Models (LLMs) and build personalized LLMs tailored for various applications. While fine-tuning see…

cs.CL2024★ 2 cited

How Chinese are Chinese Language Models? The Puzzling Lack of Language Policy in China's LLMs

Andrea W Wen-Yi, Unso Eun Seo Jo, Lu Jia Lin +1

Contemporary language models are increasingly multilingual, but Chinese LLM developers must navigate complex political and business considerations of language diversity. Language p…

cs.LG2024

WordGame: Efficient & Effective LLM Jailbreak via Simultaneous Obfuscation in Query and Response

Tianrong Zhang, Bochuan Cao, Yuanpu Cao +3

The recent breakthrough in large language models (LLMs) such as ChatGPT has revolutionized production processes at an unprecedented pace. Alongside this progress also comes mountin…

cs.CL2024★ 4 cited

JoPA:Explaining Large Language Model's Generation via Joint Prompt Attribution

Yurui Chang, Bochuan Cao, Yujia Wang +2

Large Language Models (LLMs) have demonstrated impressive performances in complex text generation tasks. However, the contribution of the input prompt to the generated content stil…