7 citations · 15 across the 11 of their papers we have counts for
11 papers
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…
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…
VideoDistill: Language-aware Vision Distillation for Video Question Answering
Bo Zou, Chao Yang, Yu Qiao +2
Significant advancements in video question answering (VideoQA) have been made thanks to thriving large image-language pretraining frameworks. Although these image-language models c…
LLaMA-Excitor: General Instruction Tuning via Indirect Feature Interaction
Bo Zou, Chao Yang, Yu Qiao +2
Existing methods to fine-tune LLMs, like Adapter, Prefix-tuning, and LoRA, which introduce extra modules or additional input sequences to inject new skills or knowledge, may compro…
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…
Rethinking Mutual Information for Language Conditioned Skill Discovery on Imitation Learning
Zhaoxun Ju, Chao Yang, Hongbo Wang +2
Language-conditioned robot behavior plays a vital role in executing complex tasks by associating human commands or instructions with perception and actions. The ability to compose…
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…
Safety of Multimodal Large Language Models on Images and Texts
Xin Liu, Yichen Zhu, Yunshi Lan +2
Attracted by the impressive power of Multimodal Large Language Models (MLLMs), the public is increasingly utilizing them to improve the efficiency of daily work. Nonetheless, the v…
SEER: Facilitating Structured Reasoning and Explanation via Reinforcement Learning
Guoxin Chen, Kexin Tang, Chao Yang +3
Elucidating the reasoning process with structured explanations from question to answer is crucial, as it significantly enhances the interpretability, traceability, and trustworthin…
Critic-Guided Decision Transformer for Offline Reinforcement Learning
Yuanfu Wang, Chao Yang, Ying Wen +2
Recent advancements in offline reinforcement learning (RL) have underscored the capabilities of Return-Conditioned Supervised Learning (RCSL), a paradigm that learns the action dis…
MM-SafetyBench: A Benchmark for Safety Evaluation of Multimodal Large Language Models
Xin Liu, Yichen Zhu, Jindong Gu +3
The security concerns surrounding Large Language Models (LLMs) have been extensively explored, yet the safety of Multimodal Large Language Models (MLLMs) remains understudied. In t…