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20242026
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cs.CL2026

ReFreeKV: Towards Threshold-Free KV Cache Compression

Xuanfan Ni, Liyan Xu, Chenyang Lyu +6

To reduce memory consumption during LLM inference, a handful of methods have been proposed for KV cache pruning. While these techniques can accomplish lossless memory reduction on…

cs.CL2026

SemEval-2026 Task 7: Everyday Knowledge Across Diverse Languages and Cultures

Nedjma Ousidhoum, Junho Myung, Carla Perez-Almendros +27

We present our shared task on evaluating the adaptability of LLMs and NLP systems across multiple languages and cultures. The task data consist of an extended version of our manual…

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.CL2025

Marco-Voice Technical Report

Fengping Tian, Chenyang Lyu, Xuanfan Ni +8

This paper presents a multifunctional speech synthesis system that integrates voice cloning and emotion control speech synthesis within a unified framework. The goal of this work i…

cs.CL2025

Marco-Bench-MIF: On Multilingual Instruction-Following Capability of Large Language Models

Bo Zeng, Chenyang Lyu, Sinuo Liu +14

Instruction-following capability has become a major ability to be evaluated for Large Language Models (LLMs). However, existing datasets, such as IFEval, are either predominantly m…