Author

Zhengzhe Liu

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Preprint Aug 2026

Skill Issue: Are Skills Language-Invariant in LLMs?

This work quantifies cross-lingual skill inconsistency orthogonally from knowledge and general benchmark performance via multilingual self-play, and shows that skill discrepancies are a measurable major roadblock in the development of truly multilingual models.

Bobby Cheng, Adam Gaber, Zhengzhe Liu et al. · 1 citation

Preprint Aug 2026

MGAL: A Multilingual Granularity-Aware Long-Context Benchmark

MGAL is the first multilingual, granularity- and position-aware long-context benchmark, constructed from United Nations reports spanning 8K to 128K tokens across the six official UN languages, and finds that LLMs perform well at word-level tasks but struggle with coarser-grained ones.

Chunhan Li, Chenglin Xu, Zongyang Zhang et al. · 0 citations