🔗 Who cites «BinaryBERT: Pushing the Limit of BERT Quantization»

74 papers of our library cite it (of the 141,840 whose bibliographies we read) · 8 with a page here, the most cited first · ← back to «BinaryBERT: Pushing the Limit of BERT Quantization»

1. LLM-Pruner: On the Structural Pruning of Large Language ModelsXinyin Ma, Gongfan Fang, Xinchao Wang2023 · 448 citations
2. ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale TransformersZhewei Yao, Reza Yazdani Aminabadi, Minjia Zhang, et al.2022 · 302 citations
3. AdapterDrop: On the Efficiency of Adapters in TransformersAndreas Rücklé, Gregor Geigle, Max Glockner, et al.2021 · 295 citations
4. LLM-QAT: Data-Free Quantization Aware Training for Large Language ModelsZechun Liu, Barlas Oguz, Changsheng Zhao, et al.2024 · 275 citations
5. SqueezeLLM: Dense-and-Sparse QuantizationSehoon Kim, Coleman Hooper, Amir Gholami, et al.2023 · 168 citations
6. Efficient Methods for Natural Language Processing: A SurveyMarcos Treviso, Ji-Ung Lee, Tianchu Ji, et al.2023 · 145 citations
7. I-BERT: Integer-only BERT QuantizationSehoon Kim, Amir Gholami, Zhewei Yao, et al.2021 · 120 citations
8. MoEfication: Transformer Feed-forward Layers are Mixtures of ExpertsZhengyan Zhang, Yankai Lin, Zhiyuan Liu, et al.2022 · 115 citations

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

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