About me
I am a Research Scientist at Google Cloud AI Research based in the San Francisco Bay Area.
Previously, I did my DPhil (the Oxford way of saying PhD) in the Machine Learning Research Group, Department of Engineering Science, University of Oxford.
Academic Services
Reviewer/program committee member at ACL (2023-24), AutoML-Conf (2023-24), COLM (2024), CVPR (2024), ECCV (2024), EMNLP (2023-24), ICLR (2024-25), ICML (2023-24), JMLR, Machine Learning, NeurIPS (2022-23), WACV (2022-24), etc.
Area chair/senior program committee member at NeurIPS (2024), ICML (2025), ACL ARR (2025-); Action editor at TMLR.
Interests
- Large language models
- Bayesian optimization
- Automated machine learning (AutoML)
- Machine learning on graphs
Education
-
DPhil (PhD), Machine Learning, 2019 - 2023
University of Oxford
-
BA, MEng, Engineering Science. First-class Honours, 2015 - 2019
University of Oxford
Recent News
💡 Preprint: Automating multi-agent designs
Our new work on automating prompt and topology design in multi-agent LLM systems is now available as an arXiv preprint!
📄 ICLR acceptance: Self-improving long-context reasoners
Our work, From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation, was accepted at ICLR 2025.
💡 Preprint: Astute RAG
Presenting AstuteRAG, our new work on overcoming imperfect retrieval and knowledge conflict.
📄 3 papers accepted at NeurIPS 2024 + 1 paper accepted at EMNLP 2024!
- A comprehensive study of instructions and demonstrations in automatic prompt design, which also powers the recently launched Vertex AI Prompt Optimizer [Google Cloud Blog] [Google for Developers Blog] [Vertex AI Documentation];
- Unstructured Query Engine (UQE), an LLM-powered database engine on unstructured data;
- GraphComBO [code], a novel Bayesian optimization framework operating over node subsets in graphs.
- ZePO(code) which aligns LLM-as-a-judge with human preferences with intelligent prompting was accepted at EMNLP 2024 as a main paper.
Featured
Publications
View all listed publications or view by tags. For a complete list including preprints & working papers, refer to my Google Scholar.
(2025).
From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation.
International Conference on Learning Representations (ICLR).
(2025).
Multi-Agent Design: Optimizing Agents with Better Prompts and Topologies.
arXiv preprint arXiv:2502.02533.
(2024).
Teach Better or Show Smarter? On Instructions and Exemplars in Automatic Prompt Optimization.
Advances in Neural Information Processing Systems (NeurIPS).
(2024).
UQE: A Query Engine for Unstructured Databases.
Advances in Neural Information Processing Systems (NeurIPS).
(2024).
Bayesian Optimization of Functions over Node Subsets in Graphs.
Advances in Neural Information Processing Systems (NeurIPS).
(2024).
Fairer Preferences Elicit Improved Human-Aligned Large Language Model Judgments.
Empirical Methods in Natural Language Processing (EMNLP).
(2024).
Astute RAG: Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts for Large Language Models.
arXiv preprint arXiv:2410.07176.
(2024).
Batch Calibration: Rethinking Calibration for In-Context Learning and Prompt Engineering.
International Conference on Learning Representations (ICLR).
Experience
Research Scientist
Research Intern
Research Intern
Contact
- xingchenw [at] google.com
- 1600 Amphitheatre Parkway, Mountain View, CA, 94043