10 citations · 23 across the 15 of their papers we have counts for
16 papers
HogVul: Black-box Adversarial Code Generation Framework Against LM-based Vulnerability Detectors
Jingxiao Yang, Ping He, Tianyu Du +2
Recent advances in software vulnerability detection have been driven by Language Model (LM)-based approaches. However, these models remain vulnerable to adversarial attacks that ex…
CLMTracing: Black-box User-level Watermarking for Code Language Model Tracing
Boyu Zhang, Ping He, Tianyu Du +4
With the widespread adoption of open-source code language models (code LMs), intellectual property (IP) protection has become an increasingly critical concern. While current waterm…
Probing the Geometry of Truth: Consistency and Generalization of Truth Directions in LLMs Across Logical Transformations and Question Answering Tasks
Yuntai Bao, Xuhong Zhang, Tianyu Du +4
Large language models (LLMs) are trained on extensive datasets that encapsulate substantial world knowledge. However, their outputs often include confidently stated inaccuracies. E…
Scalable Multi-Stage Influence Function for Large Language Models via Eigenvalue-Corrected Kronecker-Factored Parameterization
Yuntai Bao, Xuhong Zhang, Tianyu Du +4
Pre-trained large language models (LLMs) are commonly fine-tuned to adapt to downstream tasks. Since the majority of knowledge is acquired during pre-training, attributing the pred…
CopyrightMeter: Revisiting Copyright Protection in Text-to-image Models
Naen Xu, Changjiang Li, Tianyu Du +8
Text-to-image diffusion models have emerged as powerful tools for generating high-quality images from textual descriptions. However, their increasing popularity has raised signific…
HijackRAG: Hijacking Attacks against Retrieval-Augmented Large Language Models
Yucheng Zhang, Qinfeng Li, Tianyu Du +4
Retrieval-Augmented Generation (RAG) systems enhance large language models (LLMs) by integrating external knowledge, making them adaptable and cost-effective for various applicatio…
SecCoder: Towards Generalizable and Robust Secure Code Generation
Boyu Zhang, Tianyu Du, Junkai Tong +5
After large models (LMs) have gained widespread acceptance in code-related tasks, their superior generative capacity has greatly promoted the application of the code LM. Neverthele…
CoreGuard: Safeguarding Foundational Capabilities of LLMs Against Model Stealing in Edge Deployment
Qinfeng Li, Tianyue Luo, Xuhong Zhang +8
Proprietary large language models (LLMs) exhibit strong generalization capabilities across diverse tasks and are increasingly deployed on edge devices for efficiency and privacy re…
CollabEdit: Towards Non-destructive Collaborative Knowledge Editing
Jiamu Zheng, Jinghuai Zhang, Tianyu Du +3
Collaborative learning of large language models (LLMs) has emerged as a new paradigm for utilizing private data from different parties to guarantee efficiency and privacy. Meanwhil…
Bridging Context Gaps: Leveraging Coreference Resolution for Long Contextual Understanding
Yanming Liu, Xinyue Peng, Jiannan Cao +6
Large language models (LLMs) have shown remarkable capabilities in natural language processing; however, they still face difficulties when tasked with understanding lengthy context…
Unveiling the Vulnerability of Private Fine-Tuning in Split-Based Frameworks for Large Language Models: A Bidirectionally Enhanced Attack
Guanzhong Chen, Zhenghan Qin, Mingxin Yang +4
Recent advancements in pre-trained large language models (LLMs) have significantly influenced various domains. Adapting these models for specific tasks often involves fine-tuning (…
DP-MemArc: Differential Privacy Transfer Learning for Memory Efficient Language Models
Yanming Liu, Xinyue Peng, Yuwei Zhang +10
Large language models have repeatedly shown outstanding performance across diverse applications. However, deploying these models can inadvertently risk user privacy. The significan…
Tool-Planner: Task Planning with Clusters across Multiple Tools
Yanming Liu, Xinyue Peng, Jiannan Cao +6
Large language models (LLMs) have demonstrated exceptional reasoning capabilities, enabling them to solve various complex problems. Recently, this ability has been applied to the p…
TransLinkGuard: Safeguarding Transformer Models Against Model Stealing in Edge Deployment
Qinfeng Li, Zhiqiang Shen, Zhenghan Qin +4
Proprietary large language models (LLMs) have been widely applied in various scenarios. Additionally, deploying LLMs on edge devices is trending for efficiency and privacy reasons.…
ERA-CoT: Improving Chain-of-Thought through Entity Relationship Analysis
Yanming Liu, Xinyue Peng, Tianyu Du +3
Large language models (LLMs) have achieved commendable accomplishments in various natural language processing tasks. However, LLMs still encounter significant challenges when deali…
RA-ISF: Learning to Answer and Understand from Retrieval Augmentation via Iterative Self-Feedback
Yanming Liu, Xinyue Peng, Xuhong Zhang +4
Large language models (LLMs) demonstrate exceptional performance in numerous tasks but still heavily rely on knowledge stored in their parameters. Moreover, updating this knowledge…