9 citations · 15 across the 28 of their papers we have counts for
6 papers · 1 filter
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…
VERIRL: Boosting the LLM-based Verilog Code Generation via Reinforcement Learning
Fu Teng, Miao Pan, Xuhong Zhang +6
Recent advancements in code generation have shown remarkable success across software domains, yet hardware description languages (HDLs) such as Verilog remain underexplored due to…
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…
TK-Mamba: Marrying KAN With Mamba for Text-Driven 3D Medical Image Segmentation
Haoyu Yang, Yutong Guan, Meixing Shi +8
3D medical image segmentation is important for clinical diagnosis and treatment but faces challenges from high-dimensional data and complex spatial dependencies. Traditional single…
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…
LLM4EFFI: Leveraging Large Language Models to Enhance Code Efficiency and Correctness
Tong Ye, Weigang Huang, Xuhong Zhang +4
Large Language Models (LLMs), particularly Code LLMs, have demonstrated impressive performance in code generation. Current research primarily focuses on the correctness of generate…