1 citations · 2 across the 9 of their papers we have counts for
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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…
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…
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…
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…