XLGoBench: Detecting cross-lingual skill gaps with algorithmic tasks

Researchers introduce XLGoBench, a synthetic benchmark using algorithmic tasks to identify cross-lingual performance gaps in large language models across different languages. The benchmark is scalable, objective, and transparent, revealing persistent gaps in state-of-the-art models despite their claimed multilingual capabilities.

XLGoBench addresses a critical blind spot in large language model evaluation: whether models truly understand language-agnostic concepts or merely pattern-match language-specific training data. By designing algorithmic tasks that require identical logical reasoning across languages, researchers can isolate genuine comprehension gaps from surface-level language proficiency issues.

The benchmark's significance stems from the rapid deployment of LLMs in non-English-speaking markets without rigorous validation of their cross-lingual reasoning abilities. Many organizations assume that models trained on multilingual corpora perform equivalently across languages, but this research demonstrates persistent performance degradation. The synthetic nature of tasks—generated from simple templates—creates an auditable methodology that distinguishes translation errors from genuine capability gaps.

For AI developers and enterprises, these findings have immediate implications. Companies deploying LLMs for critical applications in non-English markets may be using tools with undetected reasoning failures. The scalability aspect allows developers to test models at varying complexity levels, creating opportunities to identify at what threshold linguistic barriers impact algorithmic reasoning. This could inform model selection for specific use cases and prompt retraining efforts focused on cross-lingual reasoning rather than mere language translation.

The research trajectory suggests future work will likely expand beyond algorithmic tasks to broader reasoning domains. Developers should anticipate increased scrutiny of multilingual capabilities, potentially influencing model selection criteria and training methodologies. The transparency of XLGoBench's template-based approach may also influence how other benchmarks are designed, setting standards for auditable cross-lingual evaluation.

• →XLGoBench uses objective algorithmic tasks to measure cross-lingual reasoning gaps in large language models independent of translation quality.
• →State-of-the-art multilingual models show persistent performance degradation across languages despite claims of broad multilingual capability.
• →The benchmark's scalability allows adaptation to different model capabilities and complexity requirements for comprehensive evaluation.
• →Template-based task generation enables transparent auditing for translation errors, distinguishing linguistic from logical failures.
• →Results indicate enterprises deploying LLMs in non-English markets may face undetected reasoning gaps in critical applications.