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Lucas Resck

2 accepted papers

2025

Explainability and Interpretability of Multilingual Large Language Models: A Survey

EMNLP 2025

Multilingual large language models (MLLMs) demonstrate state-of-the-art capabilities across diverse cross-lingual and multilingual tasks. Their complex internal mechanisms, however, often lack transparency, posing significant challenges in elucidating their internal processing of multilingualism, cr

Cited by 0SourcePDFScholar
2024

Exploring the Trade-off Between Model Performance and Explanation Plausibility of Text Classifiers Using Human Rationales

NAACL 2024findings

Saliency post-hoc explainability methods are important tools for understanding increasingly complex NLP models. While these methods can reflect the model’s reasoning, they may not align with human intuition, making the explanations not plausible. In this work, we present a methodology for incorporat…