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arXiv Machine Learning By Bobby Cheng, Adam Gaber, Zhengyuan Liu, Catherine Arnett, Omer Goldman, Cheston Tan, Leshem Choshen

Skill Issue: Are Skills Language-Invariant in LLMs?

Read the original on arXiv Machine Learning →

The paper investigates whether large language models (LLMs) exhibit language‑specific skill differences by having two identical model instances play a text‑based game in different languages. Using a multilingual extension of TextArena, the authors evaluate three open‑weight models across eight languages and six games, finding that the same model can show markedly different performance—varying win–loss margins, invalid actions, and strategic choices—depending on the language interface. Analyses pinpoint language‑specific failures in spatial reasoning, card‑conditioned decisions, and optimal move selection, and demonstrate that adjusting the intermediate reasoning language can recover much of the lost performance.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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