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cs.CL2026
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…
cs.CL2024
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…
cs.CL2024
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…