4 papers
Explanation Bias is a Product: Revealing the Hidden Lexical and Position Preferences in Post-Hoc Feature Attribution
Jonathan Kamp, Roos Bakker, Dominique Blok
Good quality explanations strengthen the understanding of language models and data. Feature attribution methods, such as Integrated Gradient, are a type of post-hoc explainer that…
Learning from Sufficient Rationales: Analysing the Relationship Between Explanation Faithfulness and Token-level Regularisation Strategies
Jonathan Kamp, Lisa Beinborn, Antske Fokkens
Human explanations of natural language, rationales, form a tool to assess whether models learn a label for the right reasons or rely on dataset-specific shortcuts. Sufficiency is a…
The Role of Syntactic Span Preferences in Post-Hoc Explanation Disagreement
Jonathan Kamp, Lisa Beinborn, Antske Fokkens
Post-hoc explanation methods are an important tool for increasing model transparency for users. Unfortunately, the currently used methods for attributing token importance often yie…
Dynamic Top-k Estimation Consolidates Disagreement between Feature Attribution Methods
Jonathan Kamp, Lisa Beinborn, Antske Fokkens
Feature attribution scores are used for explaining the prediction of a text classifier to users by highlighting a k number of tokens. In this work, we propose a way to determine th…