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Xinpeng Wang

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3 papers

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NeurIPS Conference 2025 Conference Paper

Refusal Direction is Universal Across Safety-Aligned Languages

Xinpeng Wang
Mingyang Wang
Yihong Liu
Hinrich Schuetze
Barbara Plank

Refusal mechanisms in large language models (LLMs) are essential for ensuring safety. Recent research has revealed that refusal behavior can be mediated by a single direction in activation space, enabling targeted interventions to bypass refusals. While this is primarily demonstrated in an English-centric context, appropriate refusal behavior is important for any language, but poorly understood. In this paper, we investigate the refusal behavior in LLMs across 14 languages using \textit{PolyRefuse}, a multilingual safety dataset created by translating malicious and benign English prompts into these languages. We uncover the surprising cross-lingual universality of the refusal direction: a vector extracted from English can bypass refusals in other languages with near-perfect effectiveness, without any additional fine-tuning. Even more remarkably, refusal directions derived from any safety-aligned language transfer seamlessly to others. We attribute this transferability to the parallelism of refusal vectors across languages in the embedding space and identify the underlying mechanism behind cross-lingual jailbreaks. These findings provide actionable insights for building more robust multilingual safety defenses and pave the way for a deeper mechanistic understanding of cross-lingual vulnerabilities in LLMs.

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IJCAI Conference 2024 Conference Paper

On the Essence and Prospect: An Investigation of Alignment Approaches for Big Models

Xinpeng Wang
Shitong Duan
Xiaoyuan Yi
Jing Yao
Shanlin Zhou
Zhihua Wei
Peng Zhang
Dongkuan Xu

Big models have achieved revolutionary breakthroughs in the field of AI, but they also pose potential ethical and societal risks to humans. Addressing such problems, alignment technologies were introduced to make these models conform to human preferences and values. Despite the considerable advancements in the past year, various challenges lie in establishing the optimal alignment strategy, such as data cost and scalable oversight, and how to align remains an open question. In this survey paper, we comprehensively investigate value alignment approaches. We first unpack the historical context of alignment tracing back to the 1920s (where it comes from), then delve into the mathematical essence of alignment (what it is), shedding light on the inherent challenges. Following this foundation, we provide a detailed examination of existing alignment methods, which fall into three categories: RL-based Alignment, SFT-based Alignment, and Inference-Time Alignment, and demonstrate their intrinsic connections, strengths, and limitations, helping readers better understand this research area. In addition, two emerging topics, alignment goal and multimodal alignment, are also discussed as novel frontiers in the field. Looking forward, we discuss potential alignment paradigms and how they could handle remaining challenges, prospecting where future alignment will go.

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IJCAI Conference 2021 Conference Paper

Deep Reinforcement Learning for Multi-contact Motion Planning of Hexapod Robots

Huiqiao Fu
Kaiqiang Tang
Peng Li
Wenqi Zhang
Xinpeng Wang
Guizhou Deng
Tao Wang
Chunlin Chen

Legged locomotion in a complex environment requires careful planning of the footholds of legged robots. In this paper, a novel Deep Reinforcement Learning (DRL) method is proposed to implement multi-contact motion planning for hexapod robots moving on uneven plum-blossom piles. First, the motion of hexapod robots is formulated as a Markov Decision Process (MDP) with a speciﬁed reward function. Second, a transition feasibility model is proposed for hexapod robots, which describes the feasibility of the state transition under the condition of satisfying kinematics and dynamics, and in turn determines the rewards. Third, the footholds and Center-of-Mass (CoM) sequences are sampled from a diagonal Gaussian distribution and the sequences are optimized through learning the optimal policies using the designed DRL algorithm. Both of the simulation and experimental results on physical systems demonstrate the feasibility and efficiency of the proposed method. Videos are shown at https: //videoviewpage. wixsite. com/mcrl.

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