XNLI (Cross-lingual Natural Language Inference) is a multilingual NLI benchmark spanning 15 languages — testing whether models can perform natural language inference across languages, evaluating cross-lingual transfer and multilingual understanding.

What Is XNLI?

• Type: Cross-lingual NLI evaluation benchmark.
• Languages: 15 languages including English, French, German, Chinese, Arabic, etc.
• Source: Human translations of MultiNLI development/test sets.
• Task: Entailment/contradiction/neutral classification across languages.
• Purpose: Evaluate multilingual and cross-lingual models.

Why XNLI Matters

• Multilingual: Standard benchmark for multilingual models.
• Cross-lingual Transfer: Test zero-shot transfer to new languages.
• Coverage: 15 diverse languages (different families, scripts).
• Quality: Professional human translations.
• Standard: Used for mBERT, XLM-R, multilingual GPT evaluation.

Languages Covered

English, French, Spanish, German, Greek, Bulgarian, Russian, Turkish, Arabic, Vietnamese, Thai, Chinese, Hindi, Swahili, Urdu.

Evaluation Scenarios

• Translate-Train: Train on translated data.
• Translate-Test: Translate test to English, use English model.
• Zero-Shot: Train English only, test all languages.

XNLI is the gold standard for multilingual NLU — testing cross-lingual generalization.