4 citations · 13 across the 21 of their papers we have counts for
22 papers
TTMark: Pairwise Distortion-Free Watermarking Beyond Single-Token Entropy
Ruibo Chen, Zhengmian Hu, Donghang Lu +5
Distortion-free watermarking enables reliable attribution of machine-generated text while preserving output distribution. However, existing methods operate independently on each ge…
Hidden in Plain Tokens: Simply Robust, Gradient-Free Watermark for Synthetic Audio
Georgios Milis, Yubin Qin, Yihan Wu +1
As policy catches up with the capabilities of generative AI, watermarking is central to content provenance efforts. Inference-time watermarks for autoregressive models are unfit fo…
MCMark: Distortion-Free Multi-Bit Watermarking for Long Messages
Xuehao Cui, Ruibo Chen, Yihan Wu +1
Large language models now produce text indistinguishable from human writing, which increases the need for reliable provenance tracing. Multi-bit watermarking can embed identifiers…
More Haste, Less Speed: Weaker Single-Layer Watermark Improves Distortion-Free Watermark Ensembles
Ruibo Chen, Yihan Wu, Xuehao Cui +2
Watermarking has emerged as a crucial technique for detecting and attributing content generated by large language models. While recent advancements have utilized watermark ensemble…
Robust Distortion-Free Watermark for Autoregressive Audio Generation Models
Yihan Wu, Georgios Milis, Ruibo Chen +1
The rapid advancement of next-token-prediction models has led to widespread adoption across modalities, enabling the creation of realistic synthetic media. In the audio domain, whi…
Model Correlation Detection via Random Selection Probing
Ruibo Chen, Sheng Zhang, Yihan Wu +3
The growing prevalence of large language models (LLMs) and vision-language models (VLMs) has heightened the need for reliable techniques to determine whether a model has been fine-…
Analyzing and Evaluating Unbiased Language Model Watermark
Yihan Wu, Xuehao Cui, Ruibo Chen +1
Verifying the authenticity of AI-generated text has become increasingly important with the rapid advancement of large language models, and unbiased watermarking has emerged as a pr…
An Ensemble Framework for Unbiased Language Model Watermarking
Yihan Wu, Ruibo Chen, Georgios Milis +1
As large language models become increasingly capable and widely deployed, verifying the provenance of machine-generated content is critical to ensuring trust, safety, and accountab…
A Watermark for Auto-Regressive Image Generation Models
Yihan Wu, Xuehao Cui, Ruibo Chen +2
The rapid evolution of image generation models has revolutionized visual content creation, enabling the synthesis of highly realistic and contextually accurate images for diverse a…
Modality-Balancing Preference Optimization of Large Multimodal Models by Adversarial Negative Mining
Chenxi Liu, Tianyi Xiong, Yanshuo Chen +5
The task adaptation and alignment of Large Multimodal Models (LMMs) have been significantly advanced by instruction tuning and further strengthened by recent preference optimizatio…
Towards Optimal Multi-draft Speculative Decoding
Zhengmian Hu, Tong Zheng, Vignesh Viswanathan +5
Large Language Models (LLMs) have become an indispensable part of natural language processing tasks. However, autoregressive sampling has become an efficiency bottleneck. Multi-Dra…
Improved Unbiased Watermark for Large Language Models
Ruibo Chen, Yihan Wu, Junfeng Guo +1
As artificial intelligence surpasses human capabilities in text generation, the necessity to authenticate the origins of AI-generated content has become paramount. Unbiased waterma…
Towards Copyright Protection for Knowledge Bases of Retrieval-augmented Language Models via Reasoning
Junfeng Guo, Yiming Li, Ruibo Chen +4
Large language models (LLMs) are increasingly integrated into real-world personalized applications through retrieval-augmented generation (RAG) mechanisms to supplement their respo…
A Watermark for Order-Agnostic Language Models
Ruibo Chen, Yihan Wu, Yanshuo Chen +3
Statistical watermarking techniques are well-established for sequentially decoded language models (LMs). However, these techniques cannot be directly applied to order-agnostic LMs,…
De-mark: Watermark Removal in Large Language Models
Ruibo Chen, Yihan Wu, Junfeng Guo +1
Watermarking techniques offer a promising way to identify machine-generated content via embedding covert information into the contents generated from language models (LMs). However…
Distortion-free Watermarks are not Truly Distortion-free under Watermark Key Collisions
Yihan Wu, Ruibo Chen, Zhengmian Hu +4
Language model (LM) watermarking techniques inject a statistical signal into LM-generated content by substituting the random sampling process with pseudo-random sampling, using wat…
Few-Shot Class Incremental Learning with Attention-Aware Self-Adaptive Prompt
Chenxi Liu, Zhenyi Wang, Tianyi Xiong +4
Few-Shot Class-Incremental Learning (FSCIL) models aim to incrementally learn new classes with scarce samples while preserving knowledge of old ones. Existing FSCIL methods usually…
Your Vision-Language Model Itself Is a Strong Filter: Towards High-Quality Instruction Tuning with Data Selection
Ruibo Chen, Yihan Wu, Lichang Chen +6
Data selection in instruction tuning emerges as a pivotal process for acquiring high-quality data and training instruction-following large language models (LLMs), but it is still a…
GPT-4 Vision on Medical Image Classification -- A Case Study on COVID-19 Dataset
Ruibo Chen, Tianyi Xiong, Yihan Wu +6
This technical report delves into the application of GPT-4 Vision (GPT-4V) in the nuanced realm of COVID-19 image classification, leveraging the transformative potential of in-cont…
Unbiased Watermark for Large Language Models
Zhengmian Hu, Lichang Chen, Xidong Wu +3
The recent advancements in large language models (LLMs) have sparked a growing apprehension regarding the potential misuse. One approach to mitigating this risk is to incorporate w…
Shielding the Unseen: Privacy Protection through Poisoning NeRF with Spatial Deformation
Yihan Wu, Brandon Y. Feng, Heng Huang
In this paper, we introduce an innovative method of safeguarding user privacy against the generative capabilities of Neural Radiance Fields (NeRF) models. Our novel poisoning attac…
A Resilient and Accessible Distribution-Preserving Watermark for Large Language Models
Yihan Wu, Zhengmian Hu, Junfeng Guo +2
Watermarking techniques offer a promising way to identify machine-generated content via embedding covert information into the contents generated from language models. A challenge i…