mlsanityData to 7 Oct 2026

XNLI: Evaluating Cross-lingual Sentence Representations

arXiv 1809.05053 ↗First posted 13 Sep 2018cs.CL cs.AI cs.LGcited by 913

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Cited by 24 in LLM reasoning, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

State-of-the-art natural language processing systems rely on supervision in the form of annotated data to learn competent models. These models are generally trained on data in a single language (usually English), and cannot be directly used beyond that language. Since collecting data in every language is not realistic, there has been a growing interest in cross-lingual language understanding (XLU) and low-resource cross-language transfer. In this work, we construct an evaluation set for XLU by extending the development and test sets of the Multi-Genre Natural Language Inference Corpus (MultiNLI) to 15 languages, including low-resource languages such as Swahili and Urdu. We hope that our dataset, dubbed XNLI, will catalyze research in cross-lingual sentence understanding by providing an informative standard evaluation task. In addition, we provide several baselines for multilingual sentence understanding, including two based on machine translation systems, and two that use parallel data to train aligned multilingual bag-of-words and LSTM encoders. We find that XNLI represents a practical and challenging evaluation suite, and that directly translating the test data yields the best performance among available baselines.

Key facts. Cited by 913 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Sep 2018.

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Citing papers per year: 2021: 139; 2022: 138; 2023: 174; 2024: 207; 2025: 160; 2026: 95.

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  5. 2021XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scalecited by 589
  6. 2022Language Models are Multilingual Chain-of-Thought Reasonerscited by 518
  7. 2021ByT5: Towards a token-free future with pre-trained byte-to-byte modelscited by 504
  8. 2021Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Surveycited by 457
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  10. 2021Pre-Trained Models: Past, Present and Futurecited by 319
  11. 2023The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variantscited by 300
  12. 2023MEGA: Multilingual Evaluation of Generative AIcited by 291
38 more
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  2. 2021ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generationcited by 258
  3. 2021Quality at a Glance: An Audit of Web-Crawled Multilingual Datasetscited by 241
  4. 2024mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrievalcited by 233
  5. 2023Multilingual Jailbreak Challenges in Large Language Modelscited by 222
  6. 2022Challenges and Strategies in Cross-Cultural NLPcited by 216
  7. 2023ChatGPT Beyond English: Towards a Comprehensive Evaluation of Large Language Models in Multilingual Learningcited by 203
  8. 2023Okapi: Instruction-tuned Large Language Models in Multiple Languages with Reinforcement Learning from Human Feedbackcited by 189
  9. 2021Word Alignment by Fine-tuning Embeddings on Parallel Corporacited by 186
  10. 2023Do All Languages Cost the Same? Tokenization in the Era of Commercial Language Modelscited by 186
  11. 2024Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuningcited by 174
  12. 2023CulturaX: A Cleaned, Enormous, and Multilingual Dataset for Large Language Models in 167 Languagescited by 172
  13. 2021XTREME-R: Towards More Challenging and Nuanced Multilingual Evaluationcited by 167
  14. 2023Not All Languages Are Created Equal in LLMs: Improving Multilingual Capability by Cross-Lingual-Thought Promptingcited by 163
  15. 2021Charformer: Fast Character Transformers via Gradient-based Subword Tokenizationcited by 158
  16. 2022On the Representation Collapse of Sparse Mixture of Expertscited by 156
  17. 2024Eagle and Finch: RWKV with Matrix-Valued States and Dynamic Recurrencecited by 155
  18. 2022Repairing the Cracked Foundation: A Survey of Obstacles in Evaluation Practices for Generated Textcited by 154
  19. 2023SIB-200: A Simple, Inclusive, and Big Evaluation Dataset for Topic Classification in 200+ Languages and Dialectscited by 149
  20. 2021On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptationcited by 147
  21. 2023UniMax: Fairer and more Effective Language Sampling for Large-Scale Multilingual Pretrainingcited by 147
  22. 2022Lifting the Curse of Multilinguality by Pre-training Modular Transformerscited by 145
  23. 2021Composable Sparse Fine-Tuning for Cross-Lingual Transfercited by 144
  24. 2022mGPT: Few-Shot Learners Go Multilingualcited by 141
  25. 2023Understanding Catastrophic Forgetting in Language Models via Implicit Inferencecited by 138
  26. 2023Similarity of Neural Network Models: A Survey of Functional and Representational Measurescited by 128
  27. 2021BanglaBERT: Language Model Pretraining and Benchmarks for Low-Resource Language Understanding Evaluation in Banglacited by 123
  28. 2021Entailment as Few-Shot Learnercited by 122
  29. 2024Aya 23: Open Weight Releases to Further Multilingual Progresscited by 121
  30. 2021Systematic Inequalities in Language Technology Performance across the World's Languagescited by 118
  31. 2021mMARCO: A Multilingual Version of the MS MARCO Passage Ranking Datasetcited by 116
  32. 2021KLUE: Korean Language Understanding Evaluationcited by 113
  33. 2021BERT: A Review of Applications in Natural Language Processing and Understandingcited by 109
  34. 2023M3Exam: A Multilingual, Multimodal, Multilevel Benchmark for Examining Large Language Modelscited by 109
  35. 2022mSLAM: Massively multilingual joint pre-training for speech and textcited by 108
  36. 2023Tokenizer Choice For LLM Training: Negligible or Crucial?cited by 105
  37. 2024Language Models are Homer Simpson! Safety Re-Alignment of Fine-tuned Language Models through Task Arithmeticcited by 105
  38. 2021LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understandingcited by 103

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