Year: 2025
Type: conference-paper
Abstract: Weight quantization has become a key tool for democratizing access to large language models (LLMs).Despite the technique's growing popularity and potential to aid speakers of diverse languages worldwide, new LLM quantization methods are predominantly validated in monolingual English contexts.This study explores ways to consistently evaluate the multilingual performance of a variety of LLaMA-based models under different quantization configurations.We identify links between the multilingual performance of widely adopted LLM quantization methods and multiple factors such as language's prevalence in the training set and similarity to model's dominant language.1 The evaluation code alongside extended evaluation results are available at https:// github.com/MaksymIakovenko/llm_quant_across_langs.
Authors: Maksym Iakovenko, Stéphane Dupont
Institution: University of Mons
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4409479585