52 citations · 61 across the 15 of their papers we have counts for
16 papers
dLLM: Simple Diffusion Language Modeling
Zhanhui Zhou, Lingjie Chen, Hanghang Tong +1
Although diffusion language models (DLMs) are evolving quickly, many recent models converge on a set of shared components. These components, however, are distributed across ad-hoc…
SafeWork-R1: Coevolving Safety and Intelligence under the AI-45 Law
Shanghai AI Lab, :, Yicheng Bao +115
We introduce SafeWork-R1, a cutting-edge multimodal reasoning model that demonstrates the coevolution of capabilities and safety. It is developed by our proposed SafeLadder framewo…
RePO: Replay-Enhanced Policy Optimization
Siheng Li, Zhanhui Zhou, Wai Lam +2
Reinforcement learning (RL) is vital for optimizing large language models (LLMs). Recent Group Relative Policy Optimization (GRPO) estimates advantages using multiple on-policy out…
SafeCoT: Improving VLM Safety with Minimal Reasoning
Jiachen Ma, Zhanhui Zhou, Chao Yang +1
Ensuring safe and appropriate responses from vision-language models (VLMs) remains a critical challenge, particularly in high-risk or ambiguous scenarios. We introduce SafeCoT, a l…
Mitigating Object Hallucination via Robust Local Perception Search
Zixian Gao, Chao Yang, Zhanhui Zhou +2
Recent advancements in Multimodal Large Language Models (MLLMs) have enabled them to effectively integrate vision and language, addressing a variety of downstream tasks. However, d…
VLMs Can Aggregate Scattered Training Patches
Zhanhui Zhou, Lingjie Chen, Chao Yang +1
One way to mitigate risks in vision-language models (VLMs) is to remove dangerous samples in their training data. However, such data moderation can be easily bypassed when harmful…
Emergent Response Planning in LLMs
Zhichen Dong, Zhanhui Zhou, Zhixuan Liu +2
In this work, we argue that large language models (LLMs), though trained to predict only the next token, exhibit emergent planning behaviors: $\textbf{their hidden representations…
Inference-Time Language Model Alignment via Integrated Value Guidance
Zhixuan Liu, Zhanhui Zhou, Yuanfu Wang +2
Large language models are typically fine-tuned to align with human preferences, but tuning large models is computationally intensive and complex. In this work, we introduce $\texti…
Iterative Length-Regularized Direct Preference Optimization: A Case Study on Improving 7B Language Models to GPT-4 Level
Jie Liu, Zhanhui Zhou, Jiaheng Liu +4
Direct Preference Optimization (DPO), a standard method for aligning language models with human preferences, is traditionally applied to offline preferences. Recent studies show th…
Weak-to-Strong Search: Align Large Language Models via Searching over Small Language Models
Zhanhui Zhou, Zhixuan Liu, Jie Liu +3
Large language models are usually fine-tuned to align with human preferences. However, fine-tuning a large language model can be challenging. In this work, we introduce $\textit{we…
Attacks, Defenses and Evaluations for LLM Conversation Safety: A Survey
Zhichen Dong, Zhanhui Zhou, Chao Yang +2
Large Language Models (LLMs) are now commonplace in conversation applications. However, their risks of misuse for generating harmful responses have raised serious societal concerns…
ConceptMath: A Bilingual Concept-wise Benchmark for Measuring Mathematical Reasoning of Large Language Models
Yanan Wu, Jie Liu, Xingyuan Bu +10
This paper introduces ConceptMath, a bilingual (English and Chinese), fine-grained benchmark that evaluates concept-wise mathematical reasoning of Large Language Models (LLMs). Unl…
MT-Bench-101: A Fine-Grained Benchmark for Evaluating Large Language Models in Multi-Turn Dialogues
Ge Bai, Jie Liu, Xingyuan Bu +8
The advent of Large Language Models (LLMs) has drastically enhanced dialogue systems. However, comprehensively evaluating the dialogue abilities of LLMs remains a challenge. Previo…
Emulated Disalignment: Safety Alignment for Large Language Models May Backfire!
Zhanhui Zhou, Jie Liu, Zhichen Dong +4
Large language models (LLMs) undergo safety alignment to ensure safe conversations with humans. However, this paper introduces a training-free attack method capable of reversing sa…
MaskMA: Towards Zero-Shot Multi-Agent Decision Making with Mask-Based Collaborative Learning
Jie Liu, Yinmin Zhang, Chuming Li +4
Building a single generalist agent with strong zero-shot capability has recently sparked significant advancements. However, extending this capability to multi-agent decision making…
Beyond One-Preference-Fits-All Alignment: Multi-Objective Direct Preference Optimization
Zhanhui Zhou, Jie Liu, Jing Shao +4
A single language model, even when aligned with labelers through reinforcement learning from human feedback (RLHF), may not suit all human preferences. Recent approaches therefore…