Yao, Huaxiu

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

Holistic Evaluation of Language Models

Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 255

Joint Modeling of Local and Global Temporal Dynamics for Multivariate Time Series Forecasting with Missing Values

Tang, X., Yao, H., Sun, Y., Aggarwal, C., Mitra, P., & Wang, S. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence

Time Series Analysis and ForecastingForecasting Techniques and Applications

Cited by 125

Just ask for calibration: Strategies for eliciting calibrated confidence scores from language models fine-tuned with human feedback

Tian, K., Mitchell, E., Zhou, A., Sharma, A., Rafailov, R., Yao, H., Finn, C., & Manning, C. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Topic ModelingAdversarial Robustness in Machine Learning

Cited by 123

RULE: Reliable Multimodal RAG for Factuality in Medical Vision Language Models

Xia, P., Zhu, K., Li, H., Zhu, H., Li, Y., Li, G., Zhang, L., & Yao, H. (2024) · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing

Multimodal Machine Learning ApplicationsArtificial Intelligence in Healthcare and Education

Cited by 67

CARES: A Comprehensive Benchmark of Trustworthiness in Medical Vision Language Models

Xia, P., Chen, Z., Tian, J., Gong, Y., Hou, R., Xu, Y., Wu, Z., Fan, Z., Zhou, Y., Zhu, K., et al. (2024) · Advances in Neural Information Processing Systems 37

Multimodal Machine Learning ApplicationsArtificial Intelligence in Healthcare and Education

Cited by 32

Fine-tuning Language Models for Factuality

Tian, K., Mitchell, E., Yao, H., Manning, C. D., & Finn, C. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 14

Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedback

Tian, K., Mitchell, E., Zhou, A., Sharma, A., Rafailov, R., Yao, H., Finn, C., & Manning, C. D. (2023) · arXiv

Topic ModelingAdversarial Robustness in Machine Learning

Cited by 5

Verifiable Format Control for Large Language Model Generations

Wang, Z., Jiang, J., Zhou, H., Zheng, W., Zhang, X., Bansal, C., & Yao, H. (2025) · Findings of the Association for Computational Linguistics: NAACL 2025

Topic ModelingNatural Language Processing Techniques

Cited by 5

MDocAgent: A Multi-Modal Multi-Agent Framework for Document Understanding

Han, S., Xia, P., Zhang, R., Sun, T., Li, Y., Zhu, H., & Yao, H. (2025) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 4

AutoTrust: Benchmarking Trustworthiness in Large Vision Language Models for Autonomous Driving

Xing, S., Hua, H., Gao, X., Zhu, S., Li, R., Tian, K., Li, X., Huang, H., Yang, T., Wang, Z., et al. (2024) · arXiv

Explainable Artificial Intelligence (XAI)Adversarial Robustness in Machine Learning

Cited by 3

Agent World Model: Infinity Synthetic Environments for Agentic Reinforcement Learning

Wang, Z., Xu, C., Liu, B., Wang, Y., Han, S., Yao, Z., Yao, H., & He, Y. (2026) · arXiv

Reinforcement Learning in RoboticsAI-based Problem Solving and Planning

Cited by 0

Scaling Agent Learning via Experience Synthesis

Chen, Z., Zhao, Z., Zhang, K., Liu, B., Qi, Q., Wu, Y., Kalluri, T., Cao, S., Xiong, Y., Tong, H., et al. (2025) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 0

SimpleMem: Efficient Lifelong Memory for LLM Agents

Liu, J., Su, Y., Xia, P., Han, S., Zheng, Z., Xie, C., Ding, M., & Yao, H. (2026) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 0

Agent0: Unleashing Self-Evolving Agents from Zero Data via Tool-Integrated Reasoning

Xia, P., Zeng, K., Liu, J., Qin, C., Wu, F., Zhou, Y., Xiong, C., & Yao, H. (2025) · arXiv

Evolutionary Algorithms and ApplicationsAI-based Problem Solving and Planning

Cited by 0

SkillRL: Evolving Agents via Recursive Skill-Augmented Reinforcement Learning

Xia, P., Chen, J., Wang, H., Liu, J., Zeng, K., Wang, Y., Han, S., Zhou, Y., Zhao, X., Chen, H., et al. (2026) · arXiv

Reinforcement Learning in RoboticsAI-based Problem Solving and Planning

Cited by 0

Alignment Tipping Process: How Self-Evolution Pushes LLM Agents Off the Rails

Han, S., Xiong, K., Liu, J., Ye, X., Su, Y., Duan, W., Liu, X., Xie, C., Bansal, M., Ding, M., et al. (2025) · arXiv

Ethics and Social Impacts of AIAdversarial Robustness in Machine Learning

Cited by 0

RRSI: Regularized Recursive Self-Improvement of Agent Harnesses

Xia, P., Han, R., Wang, Z., Chen, Y., Zhuang, Y., Lee, Y., Huang, C., Yu, H., CuiZhu, Z., Ming, Y., et al. (2026) · arXiv

Evolutionary Algorithms and ApplicationsAI-based Problem Solving and Planning

Cited by 0