---
title: "Who cites Language Models are Few-Shot Learners — page 9"
description: "10,782 papers of the Elamur Research library cite «Language Models are Few-Shot Learners», found in their bibliographies · page 9 of 9 · 801–808; the most…"
canonical: "https://research.elamur.ai/free/en/papers/brown-2020-language-models-few-shot-learners/cited-by/page/9/"
---
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[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Language Models are Few-Shot Learners](https://md.research.elamur.ai/free/en/papers/brown-2020-language-models-few-shot-learners/) / [cited by](https://md.research.elamur.ai/free/en/papers/brown-2020-language-models-few-shot-learners/cited-by/) / page 9

# Who cites «Language Models are Few-Shot Learners»

10,782 papers of our library cite it (of the 140,884 whose bibliographies we read) · 808 with a page here, the most cited first · page 9 of 9 · 801–808 · [← the paper](https://md.research.elamur.ai/free/en/papers/brown-2020-language-models-few-shot-learners/)

[← prev](https://md.research.elamur.ai/free/en/papers/brown-2020-language-models-few-shot-learners/cited-by/page/8/) [1](https://md.research.elamur.ai/free/en/papers/brown-2020-language-models-few-shot-learners/cited-by/) … [8](https://md.research.elamur.ai/free/en/papers/brown-2020-language-models-few-shot-learners/cited-by/page/8/) **9**

801. [**NeoDictaBERT: Pushing the Frontier of BERT models for Hebrew**Shaltiel Shmidman, Avi Shmidman, Moshe Koppel*2025*](https://md.research.elamur.ai/free/en/papers/shmidman-2025-neodictabert-pushing-frontier-bert-models/)
802. [**Poem Meter Classification of Recited Arabic Poetry: Integrating High-Resource Systems for a Low-Resource Task**Maged S. Al-Shaibani, Zaid Alyafeai, Irfan Ahmad*2025*](https://md.research.elamur.ai/free/en/papers/al-shaibani-2025-poem-meter-classification-recited-arabic/)
803. [**Survey on Key Technologies for Large Language Model Pre-training Systems (大语言模型预训练系统关键技术综述)**Yanjie Gao (高彦杰), Yueguo Chen (陈跃国)*2025*](https://md.research.elamur.ai/free/en/papers/gao-2025-key-technologies-large/)
804. [**Can generative AI recognise textual entailment? (生成 AI は含意関係認識ができるのか)**荒沢康平, 狩野芳伸*2024*](https://md.research.elamur.ai/free/en/papers/arasawa-2024-can-generative-ai-recognise/)
805. [**Complexity factors of language models in reading comprehension: a controlled experiment (Étude des facteurs de complexité des modèles de langage dans une tâche de compréhension de lecture à l'aide d'une expérience contrôlée sémantiquement)**Elie Antoine, Frederic Bechet, Géraldine Damnati, et al.*2024*](https://md.research.elamur.ai/free/en/papers/antoine-2024-etude-des-facteurs-de-complexite/)
806. [**Human Temporal Inferences Go Beyond Aspectual Class**Katarzyna Pruś, Mark Steedman, Adam Lopez*2024*](https://md.research.elamur.ai/free/en/papers/prus-2024-human-temporal-inferences-go-beyond/)
807. [**On the importance of Data Scale in Pretraining Arabic Language Models**Abbas Ghaddar, Philippe Langlais, Mehdi Rezagholizadeh, et al.*2024*](https://md.research.elamur.ai/free/en/papers/ghaddar-2024-importance-data-scale-pretraining-arabic/)
808. [**Questions After ChatGPT: Opportunities and Challenges of Very Large Pre-trained Models (问答ChatGPT之后: 超大预训练模型的机遇和挑战)**Jingwei Lu (卢经纬), Chao Guo (郭超), Xingyuan Dai (戴星原), et al.*2023*](https://md.research.elamur.ai/free/en/papers/lu-2023-questions-after-chatgpt/)

[← prev](https://md.research.elamur.ai/free/en/papers/brown-2020-language-models-few-shot-learners/cited-by/page/8/) [1](https://md.research.elamur.ai/free/en/papers/brown-2020-language-models-few-shot-learners/cited-by/) … [8](https://md.research.elamur.ai/free/en/papers/brown-2020-language-models-few-shot-learners/cited-by/page/8/) **9**

9,974 more papers of our library cite it; they have no page here yet.

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