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LLMs show language-specific skill differences in multilingual self-play

By PulseAugur Editorial · [1 sources] ·

A new research paper explores how large language models (LLMs) exhibit varying skill sets depending on the language they use for interaction. By employing a multilingual self-play setup in a text-based game called TextArena, researchers found that the same LLM can perform significantly differently across eight languages. The study highlights that language can impact various stages of the decision-making process, leading to skill discrepancies that hinder the development of truly multilingual models. AI

IMPACT Highlights a significant roadblock in developing truly multilingual AI models, suggesting language can affect core decision-making processes.

RANK_REASON Research paper analyzing LLM behavior across languages. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLMs show language-specific skill differences in multilingual self-play

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26 / 100
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Research paper analyzing LLM behavior across languages. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Bobby Cheng, Adam Gaber, Zhengyuan Liu, Catherine Arnett, Omer Goldman, Cheston Tan, Leshem Choshen ·

    Skill Issue: Are Skills Language-Invariant in LLMs?

    arXiv:2608.25832v1 Announce Type: new Abstract: Large language models access knowledge inconsistently across languages, but to what extent do they differ in their skill sets when interacting with different languages? This work quantifies cross-lingual skill inconsistency orthogon…

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