61 citations · 110 across the 57 of their papers we have counts for
22 papers · 1 filter
d-TreeRPO: Towards More Reliable Policy Optimization for Diffusion Language Models
Leyi Pan, Shuchang Tao, Yunpeng Zhai +8
Reinforcement learning (RL) is pivotal for enhancing the reasoning capabilities of diffusion large language models (dLLMs). However, existing dLLM policy optimization methods suffe…
MarkDiffusion: An Open-Source Toolkit for Generative Watermarking of Latent Diffusion Models
Leyi Pan, Sheng Guan, Zheyu Fu +9
We introduce MarkDiffusion, an open-source Python toolkit for generative watermarking of latent diffusion models. It comprises three key components: a unified implementation framew…
GenCNER: A Generative Framework for Continual Named Entity Recognition
Yawen Yang, Fukun Ma, Shiao Meng +2
Traditional named entity recognition (NER) aims to identify text mentions into pre-defined entity types. Continual Named Entity Recognition (CNER) is introduced since entity catego…
GapDNER: A Gap-Aware Grid Tagging Model for Discontinuous Named Entity Recognition
Yawen Yang, Fukun Ma, Shiao Meng +2
In biomedical fields, one named entity may consist of a series of non-adjacent tokens and overlap with other entities. Previous methods recognize discontinuous entities by connecti…
You only need 4 extra tokens: Synergistic Test-time Adaptation for LLMs
Yijie Xu, Huizai Yao, Zhiyu Guo +5
Large language models (LLMs) are increasingly deployed in specialized domains such as finance, medicine, and agriculture, where they face significant distribution shifts from their…
PMark: Towards Robust and Distortion-free Semantic-level Watermarking with Channel Constraints
Jiahao Huo, Shuliang Liu, Bin Wang +5
Semantic-level watermarking (SWM) for large language models (LLMs) enhances watermarking robustness against text modifications and paraphrasing attacks by treating the sentence as…
A Survey on Parallel Text Generation: From Parallel Decoding to Diffusion Language Models
Lingzhe Zhang, Liancheng Fang, Chiming Duan +8
As text generation has become a core capability of modern Large Language Models (LLMs), it underpins a wide range of downstream applications. However, most existing LLMs rely on au…
Omni-SafetyBench: A Benchmark for Safety Evaluation of Audio-Visual Large Language Models
Leyi Pan, Zheyu Fu, Yunpeng Zhai +9
Omni-modal Large Language Models (OLLMs) that integrate visual, auditory, and textual processing face severe safety risks. They exhibit fragile defenses against audio-visual joint…
A Survey on Proactive Defense Strategies Against Misinformation in Large Language Models
Shuliang Liu, Hongyi Liu, Aiwei Liu +7
The widespread deployment of large language models (LLMs) across critical domains has amplified the societal risks posed by algorithmically generated misinformation. Unlike traditi…
VLA-Mark: A cross modal watermark for large vision-language alignment model
Shuliang Liu, Qi Zheng, Jesse Jiaxi Xu +8
Vision-language models demand watermarking solutions that protect intellectual property without compromising multimodal coherence. Existing text watermarking methods disrupt visual…
A Survey of AIOps in the Era of Large Language Models
Lingzhe Zhang, Tong Jia, Mengxi Jia +7
As large language models (LLMs) grow increasingly sophisticated and pervasive, their application to various Artificial Intelligence for IT Operations (AIOps) tasks has garnered sig…
A Call for Collaborative Intelligence: Why Human-Agent Systems Should Precede AI Autonomy
Henry Peng Zou, Wei-Chieh Huang, Yaozu Wu +10
Recent improvements in large language models (LLMs) have led many researchers to focus on building fully autonomous AI agents. This position paper questions whether this approach i…
Video Signature: Implicit Watermarking for Video Diffusion Models
Yu Huang, Junhao Chen, Shuliang Liu +6
The rapid development of Artificial Intelligence Generated Content (AIGC) has led to significant progress in video generation, but also raises serious concerns about intellectual p…
MUSE: Model-Agnostic Tabular Watermarking via Multi-Sample Selection
Liancheng Fang, Aiwei Liu, Henry Peng Zou +4
We introduce MUSE, a watermarking algorithm for tabular generative models. Previous approaches typically leverage DDIM invertibility to watermark tabular diffusion models, but tabu…
SSR: Speculative Parallel Scaling Reasoning in Test-time
Yuanlin Chu, Bo Wang, Xiang Liu +3
Large language models (LLMs) have achieved impressive results on multi-step mathematical reasoning, yet at the cost of high computational overhead. This challenge is particularly a…
CoheMark: A Novel Sentence-Level Watermark for Enhanced Text Quality
Junyan Zhang, Shuliang Liu, Aiwei Liu +4
Watermarking technology is a method used to trace the usage of content generated by large language models. Sentence-level watermarking aids in preserving the semantic integrity wit…
VideoMark: A Distortion-Free Robust Watermarking Framework for Video Diffusion Models
Xuming Hu, Hanqian Li, Jungang Li +5
This work introduces \textbf{VideoMark}, a distortion-free robust watermarking framework for video diffusion models. As diffusion models excel in generating realistic videos, relia…
Mark Your LLM: Detecting the Misuse of Open-Source Large Language Models via Watermarking
Yijie Xu, Aiwei Liu, Xuming Hu +2
As open-source large language models (LLMs) like Llama3 become more capable, it is crucial to develop watermarking techniques to detect their potential misuse. Existing watermarkin…
Scaling Laws for Many-Shot In-Context Learning with Self-Generated Annotations
Zhengyao Gu, Henry Peng Zou, Yankai Chen +3
The high cost of obtaining high-quality annotated data for in-context learning (ICL) has motivated the development of methods that use self-generated annotations in place of ground…
TabGen-ICL: Residual-Aware In-Context Example Selection for Tabular Data Generation
Liancheng Fang, Aiwei Liu, Hengrui Zhang +3
Large Language models (LLMs) have achieved encouraging results in tabular data generation. However, existing approaches require fine-tuning, which is computationally expensive. Thi…
Can LLM Watermarks Robustly Prevent Unauthorized Knowledge Distillation?
Leyi Pan, Aiwei Liu, Shiyu Huang +5
The radioactive nature of Large Language Model (LLM) watermarking enables the detection of watermarks inherited by student models when trained on the outputs of watermarked teacher…
Cold-Start Recommendation towards the Era of Large Language Models (LLMs): A Comprehensive Survey and Roadmap
Weizhi Zhang, Yuanchen Bei, Liangwei Yang +15
Cold-start problem is one of the long-standing challenges in recommender systems, focusing on accurately modeling new or interaction-limited users or items to provide better recomm…