A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
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SweEval is a cross-lingual safety benchmark that evaluates LLMs on handling swear words across 8 languages, revealing higher vulnerability in Indic languages and transliterated contexts.
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Multitask prompted fine-tuning on English tasks enables zero-shot cross-lingual generalization in large multilingual models.
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XLM-RoBERTa scales multilingual masked language modeling to 100 languages, achieving strong cross-lingual transfer without translation objectives.
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Proposes unsupervised and supervised methods for learning cross-lingual language models (XLMs) using monolingual or parallel data.