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cs.AI2025
llmSHAP: A Principled Approach to LLM Explainability
Filip Naudot, Tobias Sundqvist, Timotheus Kampik
Feature attribution methods help make machine learning-based inference explainable by determining how much one or several features have contributed to a model's output. A particula…
cs.AI2025
Set Contribution Functions for Quantitative Bipolar Argumentation and their Principles
Filip Naudot, Andreas Brännström, Vicenç Torra +1
We present functions that quantify the contribution of a set of arguments in quantitative bipolar argumentation graphs to (the final strength of) an argument of interest, a so-call…