---
title: "Who cites OCNLI: Original Chinese Natural Language Inference"
description: "67 papers of the Elamur Research library cite «OCNLI: Original Chinese Natural Language Inference», found in their bibliographies; the most…"
canonical: "https://research.elamur.ai/free/en/papers/hu-2020-ocnli-original-chinese-natural/cited-by/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/hu-2020-ocnli-original-chinese-natural/cited-by/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [OCNLI: Original Chinese Natural Language Inference](https://md.research.elamur.ai/free/en/papers/hu-2020-ocnli-original-chinese-natural/) / cited by

# Who cites «OCNLI: Original Chinese Natural Language Inference»

67 papers of our library cite it (of the 141,840 whose bibliographies we read) · 12 with a page here, the most cited first · [← back to «OCNLI: Original Chinese Natural Language Inference»](https://md.research.elamur.ai/free/en/papers/hu-2020-ocnli-original-chinese-natural/)

1. [**Qwen Technical Report**Jinze Bai, Shuai Bai, Yunfei Chu, et al.*2023 · 1,645 citations*](https://md.research.elamur.ai/free/en/papers/bai-2023-qwen-technical-report/)
2. [**Pre-Training with Whole Word Masking for Chinese BERT**Yiming Cui, Wanxiang Che, Ting Liu, et al.*2019 · 1,490 citations*](https://md.research.elamur.ai/free/en/papers/cui-2019-pre-training-whole-word-masking/)
3. [**PPT: Pre-trained Prompt Tuning for Few-shot Learning**Yuxian Gu, Xu Han, Zhiyuan Liu, et al.*2022 · 469 citations*](https://md.research.elamur.ai/free/en/papers/gu-2022-ppt-pre-trained-prompt-tuning/)
4. [**Masked Language Modeling and the Distributional Hypothesis: Order Word Matters Pre-training for Little**Koustuv Sinha, Robin Jia, Dieuwke Hupkes, et al.*2021 · 292 citations*](https://md.research.elamur.ai/free/en/papers/sinha-2021-masked-language-modeling-distributional-hypothesis/)
5. [**CLUE: A Chinese Language Understanding Evaluation Benchmark**Liang Xu, Hai Hu, Xuanwei Zhang, et al.*2020 · 243 citations*](https://md.research.elamur.ai/free/en/papers/xu-2020-clue-chinese-language-understanding/)
6. [**CPM: A Large-scale Generative Chinese Pre-trained Language Model**Zhengyan Zhang, Xu Han, Hao Zhou, et al.*2020 · 149 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2020-cpm-large-scale-generative/)
7. [**UnNatural Language Inference**Koustuv Sinha, Prasanna Parthasarathi, Joelle Pineau, et al.*2021 · 132 citations*](https://md.research.elamur.ai/free/en/papers/sinha-2021-unnatural-language-inference/)
8. [**BUFFET: Benchmarking Large Language Models for Few-shot Cross-lingual Transfer**Akari Asai, Sneha Kudugunta, Xinyan Yu, et al.*2024 · 85 citations*](https://md.research.elamur.ai/free/en/papers/asai-2024-buffet-benchmarking-large-language-models/)
9. [**SeaEval for Multilingual Foundation Models: From Cross-Lingual Alignment to Cultural Reasoning**Bin Wang, Zhengyuan Liu, Xin Huang, et al.*2024 · 85 citations*](https://md.research.elamur.ai/free/en/papers/wang-2024-seaeval-multilingual-foundation-models-cross/)
10. [**Culturally Aware Natural Language Inference**Jing Huang, Diyi Yang*2023 · 61 citations*](https://md.research.elamur.ai/free/en/papers/huang-2023-culturally-aware-natural-language-inference/)
11. [**ArNLI: Arabic Natural Language Inference for Entailment and Contradiction Detection**Khloud Al Jallad, Nada Ghneim*2022 · 9 citations*](https://md.research.elamur.ai/free/en/papers/jallad-2022-arnli-arabic-natural-language-inference/)
12. [**Large Language Models Put to the Test on Chinese Noun Compounds: Experiments on Natural Language Inference and Compound Semantics**Le Qiu, Emmanuele Chersoni, He Zhou, et al.*2026*](https://md.research.elamur.ai/free/en/papers/qiu-2026-large-language-models-put-test/)

55 more papers of our library cite it; they have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/papers/hu-2020-ocnli-original-chinese-natural/) — OCNLI: Original Chinese Natural Language Inference