3 papers
cs.CL2026
Marco-MoE: Open Multilingual Mixture-of-Expert Language Models with Efficient Upcycling
Fan Jiang, Yu Zhao, Chenyang Lyu +5
We present Marco-MoE, a suite of fully open multilingual sparse Mixture-of-Experts (MoE) models. Marco-MoE features a highly sparse design in which only around 5\% of the total par…
cs.CL2026
CulturALL: Benchmarking Multilingual and Multicultural Competence of LLMs on Grounded Tasks
Peiqin Lin, Chenyang Lyu, Wenjiang Luo +22
Large language models (LLMs) are now deployed worldwide, inspiring a surge of benchmarks that measure their multilingual and multicultural abilities. However, these benchmarks prio…
cs.IR2025
Language Bias in Information Retrieval: The Nature of the Beast and Mitigation Methods
Jinrui Yang, Fan Jiang, Timothy Baldwin
Language fairness in multilingual information retrieval (MLIR) systems is crucial for ensuring equitable access to information across diverse languages. This paper sheds light on t…