4 papers
Sensivity of LLMs' Explanations to the Training Randomness:Context, Class & Task Dependencies
Romain Loncour, Jérémie Bogaert, François-Xavier Standaert
Transformer models are now a cornerstone in natural language processing. Yet, explaining their decisions remains a challenge. It was shown recently that the same model trained on t…
Explanation sensitivity to the randomness of large language models: the case of journalistic text classification
Jeremie Bogaert, Marie-Catherine de Marneffe, Antonin Descampe +3
Large language models (LLMs) perform very well in several natural language processing tasks but raise explainability challenges. In this paper, we examine the effect of random elem…
User Preferences for Large Language Model versus Template-Based Explanations of Movie Recommendations: A Pilot Study
Julien Albert, Martin Balfroid, Miriam Doh +6
Recommender systems have become integral to our digital experiences, from online shopping to streaming platforms. Still, the rationale behind their suggestions often remains opaque…
A Question on the Explainability of Large Language Models and the Word-Level Univariate First-Order Plausibility Assumption
Jeremie Bogaert, Francois-Xavier Standaert
The explanations of large language models have recently been shown to be sensitive to the randomness used for their training, creating a need to characterize this sensitivity. In t…