In-Context Learning

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, Sercan Ö. Arık
From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation
Batch Calibration: Rethinking Calibration for In-Context Learning and Prompt Engineering
Mitigating prompt biases and unifying existing calibration approaches without labeled data.
Han Zhou, Xingchen Wan, Lev Proleev, Diana Mincu, Jilin Chen, Katherine Heller, Subhrajit Roy
Batch Calibration: Rethinking Calibration for In-Context Learning and Prompt Engineering
Can Large Language Models Achieve Calibration with In-Context Learning?
Can Large Language Models Achieve Calibration with In-Context Learning?
Chengzu Li, Han Zhou, Goran Glavaš, Anna Korhonen, Ivan Vulić