arXiv:2605.30788v1 Announce Type: cross 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 read
The proliferation of large language models across diverse linguistic contexts necessitates robust evaluation methods to ensure equitable and functional AI. This research responds to the growing need for comparable benchmarks across languages to identify performance disparities.
A strategic reader should care about XLGoBench because it provides a standardized, scalable, and quantifiable way to assess critical cross-lingual performance gaps in LLMs. This directly impacts the global applicability and fairness of AI systems.
This benchmark offers a new, objective tool for evaluating LLMs, moving beyond anecdotal observations to a systematic detection of 'skill gaps' in different languages. It enables developers to identify and address weaknesses more effectively.
- · LLM researchers
- · Multilingual AI developers
- · Non-English speaking markets
- · AI fairness initiatives
- · LLMs with unaddressed cross-lingual biases
- · Companies with subpar multilingual AI offerings
- · Benchmarking methods lacking comparability
Identification of specific linguistic and algorithmic weaknesses in current large language models.
Accelerated development of more robust and equitable multilingual LLMs, potentially leading to increased adoption in non-English speaking regions.
Increased pressure on AI developers to demonstrate cross-lingual proficiency, potentially fostering a more globally inclusive AI ecosystem and reducing digital divides.
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Read at arXiv cs.LG