In-Context Learning

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
Survival of the Most Influential Prompts: Efficient Black-Box Prompt Search via Clustering and Pruning
Efficient prompt optimization via influential search space pruning.
Han Zhou, Xingchen Wan, Ivan Vulić, Anna Korhonen
Survival of the Most Influential Prompts: Efficient Black-Box Prompt Search via Clustering and Pruning