Chen, Tianlong

TrustLLM: Trustworthiness in Large Language Models

Huang, Y., Sun, L., Wang, H., Wu, S., Zhang, Q., Li, Y., Gao, C., Huang, Y., Lyu, W., Zhang, Y., et al. (2024) · arXiv

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 256

Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning

Chen, T., Liu, S., Chang, S., Cheng, Y., Amini, L., & Wang, Z. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Adversarial Robustness in Machine LearningMachine Learning and Data Classification

Cited by 205

Chasing Sparsity in Vision Transformers: An End-to-End Exploration

Chen, T., Cheng, Y., Gan, Z., Yuan, L., Zhang, L., & Wang, Z. (2021) · arXiv

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 111

Anti-Oversmoothing in Deep Vision Transformers via the Fourier Domain Analysis: From Theory to Practice

Wang, P., Zheng, W., Chen, T., & Wang, Z. (2022) · arXiv

Advanced Neural Network ApplicationsVisual Attention and Saliency Detection

Cited by 80

In Prospect and Retrospect: Reflective Memory Management for Long-term Personalized Dialogue Agents

Tan, Z., Yan, J., Hsu, I.-H., Han, R., Wang, Z., Le, L. T., Song, Y., Chen, Y., Palangi, H., Lee, G., et al. (2025) · arXiv

Speech and dialogue systemsAI in Service Interactions

Cited by 54

The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training

Liu, S., Chen, T., Chen, X., Shen, L., Mocanu, D. C., Wang, Z., & Pechenizkiy, M. (2022) · arXiv

Advanced Neural Network ApplicationsMachine Learning and Data Classification

Cited by 43

GTBench: Uncovering the Strategic Reasoning Capabilities of LLMs via Game-Theoretic Evaluations

Duan, J., Zhang, R., Diffenderfer, J., Kailkhura, B., Sun, L., Stengel-Eskin, E., Bansal, M., Chen, T., & Xu, K. (2024) · Advances in Neural Information Processing Systems 37

Artificial Intelligence in GamesGame Theory and Applications

Cited by 39

A Survey on Trustworthy LLM Agents: Threats and Countermeasures

Yu, M., Meng, F., Zhou, X., Wang, S., Mao, J., Pang, L., Chen, T., Wang, K., Li, X., Zhang, Y., et al. (2025) · arXiv

Access Control and TrustAdversarial Robustness in Machine Learning

Cited by 34

A Survey on Trustworthy LLM Agents: Threats and Countermeasures

Yu, M., Meng, F., Zhou, X., Wang, S., Mao, J., Pan, L., Chen, T., Wang, K., Li, X., Zhang, Y., et al. (2025) · Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2

Access Control and TrustAdversarial Robustness in Machine Learning

Cited by 34

VAQF: Fully Automatic Software-hardware Co-design Framework for Low-bit Vision Transformer

Sun, M., Ma, H., Kang, G., Jiang, Y., Chen, T., Ma, X., Wang, Z., & Wang, Y. (2022) · arXiv

Embedded Systems Design TechniquesAdvanced Neural Network Applications

Cited by 32

MedHallu: A Comprehensive Benchmark for Detecting Medical Hallucinations in Large Language Models

Pandit, S., Xu, J., Hong, J., Wang, Z., Chen, T., Xu, K., & Ding, Y. (2025) · arXiv

Topic ModelingArtificial Intelligence in Healthcare and Education

Cited by 31

In Prospect and Retrospect: Reflective Memory Management for Long-term Personalized Dialogue Agents

Tan, Z., Yan, J., Hsu, I.-H., Han, R., Wang, Z., Le, L., Song, Y., Chen, Y., Palangi, H., Lee, G., et al. (2025) · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Speech and dialogue systemsAI-based Problem Solving and Planning

Cited by 16

Two sequence- and two structure-based ML models have learned different aspects of protein biochemistry

Kulikova, A. V., Diaz, D. J., Chen, T., Cole, T. J., Ellington, A. D., & Wilke, C. O. (2023) · Scientific Reports

Machine Learning in BioinformaticsProtein Structure and Dynamics

Cited by 15

FFN-SkipLLM: A Hidden Gem for Autoregressive Decoding with Adaptive Feed Forward Skipping

Jaiswal, A. K., Hu, B., Yin, L., Ro, Y., Chen, T., Liu, S., & Akella, A. (2024) · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 4

Cut the Crap: An Economical Communication Pipeline for LLM-based Multi-Agent Systems

Zhang, G., Yue, Y., Li, Z., Yun, S., Wan, G., Wang, K., Cheng, D., Yu, J. X., & Chen, T. (2024) · arXiv

Multi-Agent Systems and NegotiationAdversarial Robustness in Machine Learning

Cited by 4

GTBench: Uncovering the Strategic Reasoning Limitations of LLMs via Game-Theoretic Evaluations

Duan, J., Zhang, R., Diffenderfer, J., Kailkhura, B., Sun, L., Stengel-Eskin, E., Bansal, M., Chen, T., & Xu, K. (2024) · arXiv

Artificial Intelligence in GamesTopic Modeling

Cited by 4

Cross-Lingual Multi-Hop Knowledge Editing

Khandelwal, A., Singh, H., Gu, H., Chen, T., & Zhou, K. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 2

AnyMAC: Cascading Flexible Multi-Agent Collaboration via Next-Agent Prediction

Wang, S., Tan, Z., Chen, Z., Zhou, S., Chen, T., & Li, J. (2025) · arXiv

Multi-Agent Systems and NegotiationDistributed Control Multi-Agent Systems

Cited by 0

HarnessRisk: A Lifecycle-Oriented Benchmark for Agent Harness Safety

Bai, Y., Duan, J., Peng, J., Wu, X., Liu, S., Wang, S., & Chen, T. (2026) · arXiv

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 0