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Main Authors: Loncour, Romain, Bogaert, Jérémie, Standaert, François-Xavier
Format: Preprint
Published: 2026
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Online Access:https://arxiv.org/abs/2603.08241
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author Loncour, Romain
Bogaert, Jérémie
Standaert, François-Xavier
author_facet Loncour, Romain
Bogaert, Jérémie
Standaert, François-Xavier
contents 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 the same data with a different randomness can lead to very different explanations. In this paper, we investigate how the (syntactic) context, the classes to be learned and the tasks influence this explanations' sensitivity to randomness. We show that they all have statistically significant impact: smallest for the (syntactic) context, medium for the classes and largest for the tasks.
format Preprint
id arxiv_https___arxiv_org_abs_2603_08241
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Sensivity of LLMs' Explanations to the Training Randomness:Context, Class & Task Dependencies
Loncour, Romain
Bogaert, Jérémie
Standaert, François-Xavier
Computation and Language
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 the same data with a different randomness can lead to very different explanations. In this paper, we investigate how the (syntactic) context, the classes to be learned and the tasks influence this explanations' sensitivity to randomness. We show that they all have statistically significant impact: smallest for the (syntactic) context, medium for the classes and largest for the tasks.
title Sensivity of LLMs' Explanations to the Training Randomness:Context, Class & Task Dependencies
topic Computation and Language
url https://arxiv.org/abs/2603.08241