XLGoBench: Detecting cross-lingual skill gaps with algorithmic tasks
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Computer Science > Computation and Language
Title:XLGoBench: Detecting cross-lingual skill gaps with algorithmic tasks
Abstract:We introduce a set of synthetic algorithmic tasks to detect cross-lingual gaps in the abilities of large language models. Our benchmark is commensurate across languages, since it requires models to perform the same underlying task in different languages; scalable, since each task can be generated at varying levels of complexity allowing it to be adapted to models with different capabilities; quantifiable, since every task admits an objective notion of correctness; and transparent, since tasks are generated from simple templates that can be readily audited for translation errors. Because our benchmark focuses on algorithmic tasks, differential performance is a sufficient -- but not necessary -- indicator of cross-lingual gaps. Nevertheless, we show through extensive experiments that our benchmark exposes persistent cross-lingual gaps in multiple state-of-the-art models.
| Comments: | 8+37pages |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.30788 [cs.CL] |
| (or arXiv:2605.30788v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.30788
arXiv-issued DOI via DataCite (pending registration)
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