2 papers
cs.HC2026
Improving LLM Interpretability with User-Centric Chain-of-Thought Reasoning
Philipp Schröppel
Advancing reasoning capabilities allow large language models (LLMs) to tackle increasingly complex problems, while reasoning traces - intermediate steps toward solutions - open up…
cs.LG2025
Navigating the Rashomon Effect: How Personalization Can Help Adjust Interpretable Machine Learning Models to Individual Users
Julian Rosenberger, Philipp Schröppel, Sven Kruschel +3
The Rashomon effect describes the observation that in machine learning (ML) multiple models often achieve similar predictive performance while explaining the underlying relationshi…