"Rethinking Pivot Programming Languages in Code Language Models" accepted in Empirical Methods in Natural Language Processing (EMNLP 2026)

Universität Ulm

We are pleased to announce that our paper "Rethinking Pivot Programming Languages in Code Language Models" has been accepted in Empirical Methods in Natural Language Processing as a main conference paper.

The paper investigates whether certain programming languages play a central role in multilingual code language models. Analyzing three models, we find that no single language serves as a universal pivot: Python aligns particularly well with English, while different languages facilitate different types of cross-language transfer.

This paper has been a cooperation between Lukas Galke Poech at the University of South Denmark, and Andor Diera and Matthias Tichy of Ulm University.

The preprint of the paper can be found at https://arxiv.org/abs/2609.22988