🔗 Who cites «Visualizing and Understanding the Effectiveness of BERT»

55 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 «Visualizing and Understanding the Effectiveness of BERT»

1. Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-TuningArmen Aghajanyan, Sonal Gupta, Luke Zettlemoyer2021 · 531 citations
2. On the Effectiveness of Adapter-based Tuning for Pretrained Language Model AdaptationRuidan He, Linlin Liu, Hai Ye, et al.2021 · 204 citations
3. Generative Data Augmentation for Commonsense ReasoningYiben Yang, Chaitanya Malaviya, Jared Fernandez, et al.2020 · 195 citations
4. BinaryBERT: Pushing the Limit of BERT QuantizationHaoli Bai, Wei Zhang, Lu Hou, et al.2021 · 165 citations
5. Full Parameter Fine-tuning for Large Language Models with Limited ResourcesKai Lv, Yuqing Yang, Tengxiao Liu, et al.2024 · 143 citations
6. Wider and Deeper LLM Networks are Fairer LLM EvaluatorsXinghua Zhang, Bowen Yu, Haiyang Yu, et al.2023 · 74 citations
7. HATFormer: Historic Handwritten Arabic Text Recognition with TransformersAdrian Chan, Anupam Mijar, Mehreen Saeed, et al.2024 · 5 citations
8. BERT's The Word : Sarcasm Target Detection using BERTPradeesh Parameswaran, Andrew Trotman, Veronica Liesaputra, et al.2021 · 2 citations

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

This page in Markdown → https://md.research.elamur.ai/free/en/papers/hao-2019-visualizing-understanding-effectiveness-bert/cited-by/