Explanation sensitivity to the randomness of large language models: the case of journalistic text classification

Understand

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 elements in the training of LLMs on the explainability of their predictions.
• We do so on a task of opinionated journalistic text classification in French.
• Using a fine-tuned CamemBERT model and an explanation method based on relevance propagation, we find that training with different random seeds produces models with similar accuracy but variable explanations.