Prompt Optimization

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation
From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation
Xingchen Wan, Han Zhou, Ruoxi Sun, Sercan Ö. Arık
From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation
Fairer Preferences Elicit Improved Human-Aligned Large Language Model Judgments
Fairer Preferences Elicit Improved Human-Aligned Large Language Model Judgments
Han Zhou, Xingchen Wan, Yinhong Liu, Nigel Collier, Ivan Vulić, Anna Korhonen
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