1 citations · 2 across the 9 of their papers we have counts for
10 papers
P-GenRM: Personalized Generative Reward Model with Test-time User-based Scaling
Pinyi Zhang, Ting-En Lin, Yuchuan Wu +7
Personalized alignment of large language models seeks to adapt responses to individual user preferences, typically via reinforcement learning. A key challenge is obtaining accurate…
From Sparse Decisions to Dense Reasoning: A Multi-attribute Trajectory Paradigm for Multimodal Moderation
Tianle Gu, Kexin Huang, Lingyu Li +6
Safety moderation is pivotal for identifying harmful content. Despite the success of textual safety moderation, its multimodal counterparts remain hindered by a dual sparsity of da…
Reward Modeling from Natural Language Human Feedback
Zongqi Wang, Rui Wang, Yuchuan Wu +5
Reinforcement Learning with Verifiable reward (RLVR) on preference data has become the mainstream approach for training Generative Reward Models (GRMs). Typically in pairwise rewar…
S2J: Bridging the Gap Between Solving and Judging Ability in Generative Reward Models
Shaoning Sun, Jiachen Yu, Zongqi Wang +3
With the rapid development of large language models (LLMs), generative reward models (GRMs) have been widely adopted for reward modeling and evaluation. Previous studies have prima…
Probing the Robustness of Large Language Models Safety to Latent Perturbations
Tianle Gu, Kexin Huang, Zongqi Wang +7
Safety alignment is a key requirement for building reliable Artificial General Intelligence. Despite significant advances in safety alignment, we observe that minor latent shifts c…
Invisible Entropy: Towards Safe and Efficient Low-Entropy LLM Watermarking
Tianle Gu, Zongqi Wang, Kexin Huang +4
Logit-based LLM watermarking traces and verifies AI-generated content by maintaining green and red token lists and increasing the likelihood of green tokens during generation. Howe…
MorphMark: Flexible Adaptive Watermarking for Large Language Models
Zongqi Wang, Tianle Gu, Baoyuan Wu +1
Watermarking by altering token sampling probabilities based on red-green list is a promising method for tracing the origin of text generated by large language models (LLMs). Howeve…
SCAN: Structured Capability Assessment and Navigation for LLMs
Zongqi Wang, Tianle Gu, Chen Gong +3
Evaluating Large Language Models (LLMs) has become increasingly important, with automatic evaluation benchmarks gaining prominence as alternatives to human evaluation. While existi…
Robust and Minimally Invasive Watermarking for EaaS
Zongqi Wang, Baoyuan Wu, Jingyuan Deng +1
Embeddings as a Service (EaaS) is emerging as a crucial role in AI applications. Unfortunately, EaaS is vulnerable to model extraction attacks, highlighting the urgent need for cop…
Thinking Racial Bias in Fair Forgery Detection: Models, Datasets and Evaluations
Decheng Liu, Zongqi Wang, Chunlei Peng +3
Due to the successful development of deep image generation technology, forgery detection plays a more important role in social and economic security. Racial bias has not been explo…