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Articles

EffiEval: Efficient and Generalizable Model Evaluation via Capability Coverage Maximization

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By Jiahao Ying, Yixin Cao, Yubo Ma, Yugang Jiang
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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Revisiting LLM Evaluation through Mechanism Interpretability: a New Metric and Model Utility Law

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By Jiahao Ying, Xipeng Qiu, Xuanjing Huang, Yaoning Wang
| arXiv
Work on one of the world's most important websites and make an impact on open science.
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Paper page - Babel: Open Multilingual Large Language Models Serving Over 90% of Global Speakers

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By Yiran Zhao, Chaoqun Liu, Yue Wen Deng, Jiahao Ying
| huggingface.co
šŸŒŸ Key Highlights: 1ļøāƒ£ Convering 90% populationā€”supporting top 25 languages, prioritizing widely spoken but previously underexplored languages in open multilingual models. 2ļøāƒ£ Innovative architectureā€”Unlike traditional continued pretraining approaches, Babel expands its parameter count through model extension, raising its performance ceiling. 3ļøāƒ£ Two powerful variants šŸ’”Babel-9Bā€”Designed for efficient inference and fine-tuning.
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