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My research lies in the areas of data science, machine learning, and AI. My goal is to build advanced large language models and AI agents for complex task-solving and decision-making. My technical efforts center on addressing key challenges in data efficiency, computation efficiency, and model robustness. On the application front, I am deeply interested in harnessing foundation models to advance AI for science.
I'm interested in teaching AI, foundation models, and machine learning.
1. Yuchen Zhuang, Xiang Chen, Tong Yu, Saayan Mitra, Victor Bursztyn, Ryan A. Rossi, Somdeb Sarkhel, Chao Zhang, "ToolChain*: Efficient Action Space Navigation in Large Language Models with A* Search", International Conference on Learning Representations (ICLR), 2024
2. Haotian Sun, Yuchen Zhuang, Wei Wei, Chao Zhang, Bo Dai, "BBox-Adapter: Lightweight Adapting for Black-Box Large Language Models", International Conference on Machine Learning (ICML), 2024
3. Haotian Sun, Yuchen Zhuang, Lingkai Kong, Bo Dai, Chao Zhang, "AdaPlanner: Adaptive Planning from Feedback with Language Models", Annual Conference on Neural Information Processing Systems (NeurIPS), 2023
4. Yuchen Zhuang, Yue Yu, Kuan Wang, Haotian Sun, Chao Zhang, "ToolQA: A Dataset for LLM Question Answering with External Tools", Annual Conference on Neural Information Processing Systems (NeurIPS), 2023
5. Yue Yu, Yuchen Zhuang, Jieyu Zhang, Yu Meng, Alexander Ratner, Ranjay Krishna, Jiaming Shen, Chao Zhang, "Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias" Annual Conference on Neural Information Processing Systems (NeurIPS), 2023
6. Rongzhi Zhang, Yue Yu, Shetty Pranav, Le Song and Chao Zhang. "PRBoost: Prompt-Based Rule Discovery and Boosting for Interactive Weakly-Supervised Learning", Annual Meeting of the Association for Computational Linguistics (ACL), 2022.
7. Yue Yu, Simiao Zuo, Haoming Jiang, Wendi Ren, Tuo Zhao, Chao Zhang, "Fine-Tuning Pre-trained Language Model with Weak Supervision: A Contrastive-Regularized Self-Training Approach", Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2021
8. Yinghao Li, Pranav Shetty, Lucas Liu, Chao Zhang, Le Song, "BERTifying Hidden Markov Models for Multi-Source Weakly Supervised Named Entity Recognition", Annual Meeting of the Association for Computational Linguistics (ACL), 2021
9. Lingkai Kong, Jimeng Sun, Chao Zhang, "SDE-Net: Equipping Deep Neural Networks with Uncertainty Estimates", Proceedings of the International Conference on Machine Learning (ICML), 2020
10. Lingkai Kong, Haoming Jiang, Yuchen Zhuang, Jie Lyu, Tuo Zhao and Chao Zhang, "Calibrated Language Model Fine-Tuning for In- and Out-of-Distribution Data", Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020