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Chenchen Li

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7 papers

2 author rows

AAAI Conference 2025 Conference Paper

DMT-RoleBench: A Dynamic Multi-Turn Dialogue Based Benchmark for Role-Playing Evaluation of Large Language Model and Agent

Dingbo Yuan
Yipeng Chen
Guodong Liu
Chenchen Li
Chengfu Tang
Dongxu Zhang
Zhenkui Wang
Xudong Wang

Recent years have witnessed a profound evolution in the abilities of Large Language Model, which has significantly boosted the proliferation of role-playing agents and platforms. Nonetheless, there is a conspicuous absence of systematic and comprehensive evaluations of role-playing abilities which are truly aligned with users' interaction scenarios in real-world. To address this gap, we have devised DMT-RoleBench, a benchmark designed to evaluate the role-playing abilities of large language models and agents based on dynamic multi-turn dialogues. Compared with existed role-playing benchmarks, DMT-RoleBench boasts several principal advantages: (1) It contains a more diverse role types and system prompts of different formats. (2) We propose an innovative evaluation paradigm to assess role-playing abilities based on dynamically generating multi-turn dialogues constrained by specific evaluation intents and topics, which is well aligned with users' interaction scenarios in real-world. (3) We define a three-tiered metric system and provide DMT-RM, which is a reward model aligned with human annotations, to annotate the dialogues. And we propose DMT-Score to calculate the final scores based on the annotated dialogues. Our experiments and analysis of leading models equipped with role-playing abilities have demonstrated the effectiveness of DMT-RoleBench.

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IROS Conference 2024 Conference Paper

A Perceptive Pneumatic Artificial Muscle Empowered by Double Helix Fiber Reinforcement

Yufeng Wang
Houping Wu
Chenchen Li
Yulian Peng
Hongbo Wang 0002

In the last decades, soft robotics has been growing rapidly as an emerging research topic, bringing new paradigms for robotic manipulation, locomotion, and human‒machine interactions. Pneumatic artificial muscle is a powerful, lightweight, rapid response with great design flexibility, making it promising for developing biological muscle-like robotic systems. The PPAM is made of a silicone tube body with double helix coil fiber reinforcement. The double helix coil fiber restricts the radial expansion of the cylinder tube to achieve extension in actuation, and monitors the muscle length change in real time by measuring its inductance. A finite element model was built to simulate the actuation characteristics of the PPAM. A theoretical formula was derived to analyze the inductive length sensing response of the double-helix coil on the PPAM. It is verified that the PPAM can sense its length change regardless of whether it is caused by active driving or external manipulation. Rigorous testing reveals that PPAM has an ultrahigh length sensing resolution of 5. 9 μm in relaxed state, with a short response time of 50 ms. The self-length sensing of PPAM is hysteresis free, and highly repeatable, showing no degradation in 1000 operation cycles. In summary, the PPAM shows promising features for developing the next-generation perceptive and responsive soft robots, intelligent hybrid robots, or safer biomedical instruments.

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EAAI Journal 2024 Journal Article

Graph Confident Learning for Software Vulnerability Detection

Qian Wang
Zhengdao Li
Hetong Liang
Xiaowei Pan
Hui Li
Tingting Li
Xiaochen Li
Chenchen Li

Code vulnerability exposes millions of software to the possibility of being attacked, as evidence every year on increasing reports of security issues, such as information leaks, system compromise, and denial of service. Despite with many vulnerability detection models proposed so far, their effectiveness is still limited due to the ignorance of syntactic structural information analysis in source code and the improper handling of labeling errors. To address these issues, we propose the Graph Confident Learning for Software Vulnerability Detection (GCL4SVD) model, a machine learning model to detect software vulnerability in the development phase. It comprises two components: code graph embedding and graph confident learning denoising. To address the syntactic structural information analysis limitation, the code graph embedding component extracts the structure and semantic information of source code with a sliding window mechanism, and then encodes source code into a graph structure to capture the patterns and characteristics of code vulnerabilities. Additionally, the graph confident learning denoising component identifies labeling errors to improve the quality of training set. Experimental results show that GCL4SVD outperforms the state-of-the-art vulnerability detection models on four open source datasets by 3. 7%, 3. 3%, 2. 5%, 0. 8% in terms of Accuracy, respectively, and by 10. 2%, 21. 8%, 8. 2%, 11. 2% in terms of F1-score.

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IJCAI Conference 2022 Conference Paper

On the Convergence of Fictitious Play: A Decomposition Approach

Yurong Chen
Xiaotie Deng
Chenchen Li
David Mguni
Jun Wang
Xiang Yan
Yaodong Yang

Fictitious play (FP) is one of the most fundamental game-theoretical learning frameworks for computing Nash equilibrium in n-player games, which builds the foundation for modern multi-agent learning algorithms. Although FP has provable convergence guarantees on zero-sum games and potential games, many real-world problems are often a mixture of both and the convergence property of FP has not been fully studied yet. In this paper, we extend the convergence results of FP to the combinations of such games and beyond. Specifically, we derive new conditions for FP to converge by leveraging game decomposition techniques. We further develop a linear relationship unifying cooperation and competition in the sense that these two classes of games are mutually transferable. Finally, we analyse a non-convergent example of FP, the Shapley game, and develop sufficient conditions for FP to converge.

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AAAI Conference 2020 Conference Paper

Cost-Effective Incentive Allocation via Structured Counterfactual Inference

Romain Lopez
Chenchen Li
Xiang Yan
Junwu Xiong
Michael Jordan
Yuan Qi
Le Song

We address a practical problem ubiquitous in modern marketing campaigns, in which a central agent tries to learn a policy for allocating strategic ﬁnancial incentives to customers and observes only bandit feedback. In contrast to traditional policy optimization frameworks, we take into account the additional reward structure and budget constraints common in this setting, and develop a new two-step method for solving this constrained counterfactual policy optimization problem. Our method ﬁrst casts the reward estimation problem as a domain adaptation problem with supplementary structure, and then subsequently uses the estimators for optimizing the policy with constraints. We also establish theoretical error bounds for our estimation procedure and we empirically show that the approach leads to signiﬁcant improvement on both synthetic and real datasets.

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AAAI Conference 2019 Conference Paper

Latent Dirichlet Allocation for Internet Price War

Chenchen Li
Xiang Yan
Xiaotie Deng
Yuan Qi
Wei Chu
Le Song
Junlong Qiao
Jianshan He

Current Internet market makers are facing an intense competitive environment, where personalized price reductions or discounted coupons are provided by their peers to attract more customers. Much investment is spent to catch up with each other’s competitors but participants in such a price cut war are often incapable of winning due to their lack of information about others’ strategies or customers’ preference. We formalize the problem as a stochastic game with imperfect and incomplete information and develop a variant of Latent Dirichlet Allocation (LDA) to infer latent variables under the current market environment, which represents preferences of customers and strategies of competitors. Tests on simulated experiments and an open dataset for real data show that, by subsuming all available market information of the market maker’s competitors, our model exhibits a significant improvement for understanding the market environment and finding the best response strategies in the Internet price war. Our work marks the first successful learning method to infer latent information in the environment of price war by the LDA modeling, and sets an example for related competitive applications to follow.

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IROS Conference 2006 Conference Paper

Walking Pattern Generation for Humanoid Robot Considering Upper Body Motion

Jie Yang 0018
Qiang Huang 0002
Jianxi Li
Chenchen Li
Kejie Li

Walking pattern generation is a main issue for humanoid robot. We have already proposed a method for planning stable walking pattern. Based on this method, this paper mainly discusses generating stable and harmonious walking pattern by considering upper body motion, and planning hip trajectories in both sagittal plane and lateral plane. To reduce the iterative computation cost, some constraints of the relationship between sagittal hip motion and lateral hip motion are formulated, and only the trajectories satisfy these constraints are worked out. Finally, we determine the trajectory with a large stability margin from these generated trajectories. The effectiveness of the proposed method is confirmed by simulations and experiments with our developed humanoid robot BHR-02 with 32 DOF

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