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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

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  1. NeoDictaBERT: Pushing the Frontier of BERT models for HebrewShaltiel Shmidman, Avi Shmidman, Moshe Koppel2025
  2. Poem Meter Classification of Recited Arabic Poetry: Integrating High-Resource Systems for a Low-Resource TaskMaged S. Al-Shaibani, Zaid Alyafeai, Irfan Ahmad2025
  3. 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
  4. 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
  5. 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
  6. Human Temporal Inferences Go Beyond Aspectual ClassKatarzyna Pruś, Mark Steedman, Adam Lopez2024
  7. On the importance of Data Scale in Pretraining Arabic Language ModelsAbbas Ghaddar, Philippe Langlais, Mehdi Rezagholizadeh, et al.2024
  8. 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

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

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