XLGoBench: Detecting cross-lingual skill gaps with algorithmic tasks
📰 ArXiv cs.AI
Learn to detect cross-lingual skill gaps in large language models using XLGoBench, a benchmark for evaluating language models' abilities across languages
Action Steps
- Generate synthetic algorithmic tasks using XLGoBench to detect cross-lingual gaps
- Evaluate language models' performance on these tasks to identify skill gaps
- Analyze results to quantify cross-lingual gaps and compare models' abilities
- Use XLGoBench to adapt tasks to models with different capabilities and complexity levels
- Apply findings to improve language models' cross-lingual performance and scalability
Who Needs to Know This
NLP researchers and developers can use XLGoBench to evaluate and improve their language models' cross-lingual capabilities, while data scientists can utilize it to analyze language models' performance across different languages
Key Insight
💡 XLGoBench provides a commensurate, scalable, and quantifiable way to evaluate language models' cross-lingual abilities
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🚀 Introducing XLGoBench: a benchmark for detecting cross-lingual skill gaps in large language models 🤖
Key Takeaways
Learn to detect cross-lingual skill gaps in large language models using XLGoBench, a benchmark for evaluating language models' abilities across languages
Full Article
Title: XLGoBench: Detecting cross-lingual skill gaps with algorithmic tasks
Abstract:
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 noti
Abstract:
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 noti
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