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From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation
Self-improving reasoners taking advantage of long-context capabilities (ICLR 2025)
Xingchen Wan, Han Zhou, Ruoxi Sun, Hootan Nakhost, Ke Jiang, Sercan Ö. Arık
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From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation
Teach Better or Show Smarter? On Instructions and Exemplars in Automatic Prompt Optimization
Comprehensive study powering the Google Cloud Vertex AI Prompt Optimizer (NeurIPS 2024).
Xingchen Wan, Ruoxi Sun, Hootan Nakhost, Sercan Ö. Arık
PDF Cite Abstract OpenReview
Teach Better or Show Smarter? On Instructions and Exemplars in Automatic Prompt Optimization
UQE: A Query Engine for Unstructured Databases
Neural Information Processing Systems (NeurIPS), 2024
Hanjun Dai, Bethany Yixin Wang, Xingchen Wan, Bo Dai, Sherry Yang, Azade Nova, Pengcheng Yin, Phitchaya Mangpo Phothilimthana, Charles Sutton, Dale Schuurmans
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UQE: A Query Engine for Unstructured Databases
Bayesian Optimization of Functions over Node Subsets in Graphs
Neural Information Processing Systems (NeurIPS), 2024
Huidong Liang, Xingchen Wan, Xiaowen Dong
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Bayesian Optimization of Functions over Node Subsets in Graphs
Fairer Preferences Elicit Improved Human-Aligned Large Language Model Judgments
EMNLP 2024
Han Zhou, Xingchen Wan, Yinhong Liu, Nigel Collier, Ivan Vulić, Anna Korhonen
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Fairer Preferences Elicit Improved Human-Aligned Large Language Model Judgments
Batch Calibration: Rethinking Calibration for In-Context Learning and Prompt Engineering
Mitigating prompt biases and unifying existing calibration approaches without labeled data (ICLR 2024)
Han Zhou, Xingchen Wan, Lev Proleev, Diana Mincu, Jilin Chen, Katherine Heller, Subhrajit Roy
PDF Cite Google Research Blog Abstract OpenReview
Batch Calibration: Rethinking Calibration for In-Context Learning and Prompt Engineering
Adaptive Batch Sizes for Active Learning: A Probabilistic Numerics Approach
International Conference on Artificial Intelligence and Statistics (AISTATS)
Masaki Adachi, Satoshi Hayakawa, Martin Jørgensen, Xingchen Wan, Vu Nguyen, Harald Oberhauser, Michael Osborne
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Adaptive Batch Sizes for Active Learning: A Probabilistic Numerics Approach
Working Memory Capacity of ChatGPT: An Empirical Study
AAAI Conference on Artificial Intelligence (AAAI), 2024
Dongyu Gong, Xingchen Wan, Dingmin Wang
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Working Memory Capacity of ChatGPT: An Empirical Study
Universal Self-Adaptive Prompting
Empirical Methods in Natural Language Processing (EMNLP), 2023
Xingchen Wan, Ruoxi Sun, Hootan Nakhost, Hanjun Dai, Julian Martin Eisenschlos, Sercan Ö. Arık, Tomas Pfister
PDF Cite Google Research Blog Abstract (Google Research) OpenReview ACL Anthology US Patent Slides & Talk
Universal Self-Adaptive Prompting
Survival of the Most Influential Prompts: Efficient Black-Box Prompt Search via Clustering and Pruning
Orders-of-magnitude faster hard prompt search with SoTA performance (EMNLP Findings 2023)
Han Zhou, Xingchen Wan, Ivan Vulić, Anna Korhonen
PDF Cite Code Abstract OpenReview ACL Anthology
Survival of the Most Influential Prompts: Efficient Black-Box Prompt Search via Clustering and Pruning