5 papers
SAILS: Surrogate-based Analysis of Interactions via Local Effect Smooths
Timo HeiÃ, Julia Herbinger, Bernd Bischl +1
Feature interactions drive much of the predictive power of machine learning models, yet existing explanation methods only detect and quantify interactions without revealing their f…
MO-CAPO: Multi-Objective Cost-Aware Prompt Optimization
Jan Büssing, Moritz Schlager, Timo Heià +2
Large language models (LLMs) achieve strong performance across a wide range of tasks but are highly sensitive to prompt design, motivating the need for automatic prompt optimizatio…
Analyzing Error Sources in Global Feature Effect Estimation
Timo HeiÃ, Coco Bögel, Bernd Bischl +1
Global feature effects such as partial dependence (PD) and accumulated local effects (ALE) plots are widely used to interpret black-box models. However, they are only estimates of…
promptolution: A Unified, Modular Framework for Prompt Optimization
Tom Zehle, Timo HeiÃ, Moritz Schlager +2
Prompt optimization has become crucial for enhancing the performance of large language models (LLMs) across a broad range of tasks. Although many research papers demonstrate its ef…
CAPO: Cost-Aware Prompt Optimization
Tom Zehle, Moritz Schlager, Timo Heià +1
Large language models (LLMs) have revolutionized natural language processing by solving a wide range of tasks simply guided by a prompt. Yet their performance is highly sensitive t…