Author

Irina Proskurina

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#natural language process... Preprint Sep 2026

Debias-SparseGPT: Bias-Aware Pruning for Large Language Models

Model compression techniques such as pruning and quantization facilitate the efficient deployment and acceleration of Large Language Models (LLMs). However, recent studies show that weight sparsification methods, such as SparseGPT, can amplify existing biases in models, with outputs varying significantly depending on p...

Irina Proskurina, Guillaume Metzler, Antoine Gourru et al. · 0 citations

#natural language process... Preprint Aug 2026

Are You Sure You're Sure? On the Impact of Instruction Tuning on Confidence and Lexical Diversity

Instruction-tuned language models achieve strong performance across a range of generation tasks, but have also recently been shown to exhibit verbalized overconfidence. In question answering, verbalized model overconfidence may be associated with the consistency of the generated supporting rationales. In this paper, we...

Irina Proskurina, Mayank Kumar, Oyindolapo Olabisi Komolafe · 0 citations