🧑‍🔬 Donald Metzler — all works

34 works · ← Donald Metzler

1. Emergent Abilities of Large Language ModelsarXiv:2206.07682Jason Wei, Yi Tay, Rishi Bommasani, et al.2022 · 2,269 citations
2. Efficient Transformers: A SurveyACM Computing SurveysYi Tay, Mostafa Dehghani, Dara Bahri, et al.2020 · 1,539 citations
3. Long Range Arena: A Benchmark for Efficient TransformersarXiv:2011.04006Yi Tay, Mostafa Dehghani, Samira Abnar, et al.2020 · 414 citations
4. Large Language Models are Effective Text Rankers with Pairwise Ranking PromptingFindings of the Association for Computational LinguisticsZhen Qin, Rolf Jagerman, Kai Hui, et al.2024 · 300 citations
5. Synthesizer: Rethinking Self-Attention in Transformer Models ↗arXiv (Cornell University) · no summary yetYi Tay et al.2020 · 245 citations
6. A New Generation of Perspective API: Efficient Multilingual Character-level TransformersACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)Alyssa Lees, Vinh Q. Tran, Yi Tay, et al.2022 · 238 citations
7. Sparse Sinkhorn Attention ↗arXiv (Cornell University) · no summary yetYi Tay et al.2020 · 200 citations
8. Confident Adaptive Language Modeling ↗neural information processing systems · no summary yetTal Schuster et al.2022 · 197 citations
9. Charformer: Fast Character Transformers via Gradient-based Subword TokenizationarXiv:2106.12672Yi Tay, Vinh Q. Tran, Sebastian Ruder, et al.2021 · 186 citations
10. Improving search relevance for implicitly temporal queries ↗International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · no summary yetDonald Metzler et al.2009 · 140 citations
11. Rethinking search ↗ACM SIGIR Forum · no summary yetDonald Metzler et al.2021 · 119 citations
12. Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers ↗arXiv (Cornell University) · no summary yetYi Tay et al.2021 · 104 citations
13. Scaling Laws vs Model Architectures: How does Inductive Bias Influence Scaling?Findings of the Association for Computational LinguisticsYi Tay, Mostafa Dehghani, Samira Abnar, et al.2023 · 86 citations
14. Building enriched document representations using aggregated anchor text ↗International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · no summary yetDonald Metzler et al.2009 · 67 citations
15. HyperPrompt: Prompt-based Task-Conditioning of Transformers ↗arXiv (Cornell University) · no summary yetYun He et al.2022 · 62 citations
16. Are Pretrained Convolutions Better than Pretrained Transformers? ↗Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · no summary yetYi Tay et al.2021 · 61 citations
17. The Benchmark Lottery ↗arXiv:2107.07002 · no summary yetMostafa Dehghani et al.2021 · 50 citations
18. StructFormer: Joint Unsupervised Induction of Dependency and Constituency Structure from Masked Language Modeling ↗Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · no summary yetYikang Shen et al.2021 · 48 citations
19. Synthesizer: Rethinking Self-Attention for Transformer Models ↗International Conference on Machine Learning · no summary yetYi Tay et al.2021 · 44 citations
20. Transcending Scaling Laws with 0.1% Extra ComputearXiv:2210.11399Yi Tay, Jason Wei, Hyung Won Chung, et al.2022 · 42 citations
21. Are Pre-trained Convolutions Better than Pre-trained Transformers? ↗arXiv (Cornell University) · no summary yetYi Tay et al.2021 · 42 citations
22. DSI++: Updating Transformer Memory with New Documents ↗Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · no summary yetSanket Vaibhav Mehta et al.2023 · 35 citations
23. Reverse Engineering Configurations of Neural Text Generation Models ↗Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · no summary yetYi Tay et al.2020 · 28 citations
24. How Reliable are Model Diagnostics? ↗Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 · no summary yetVamsi Aribandi, Yi Tay, Donald Metzler2021 · 27 citations
25. HyperGrid Transformers: Towards A Single Model for Multiple Tasks ↗International Conference on Learning Representations · no summary yetYi Tay et al.2021 · 25 citations
26. Choppy: Cut Transformer for Ranked List Truncation ↗International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · no summary yetDara Bahri et al.2020 · 24 citations
27. An Empirical Evaluation of Data-Driven Paraphrase Generation Techniques ↗Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies · no summary yetDonald Metzler, Eduard Hovy, Chunliang Zhang2011 · 23 citations
28. ED2LM: Encoder-Decoder to Language Model for Faster Document Re-ranking Inference ↗Findings of the Association for Computational Linguistics: ACL 2022 · no summary yetKai Hui et al.2022 · 12 citations
29. SCARF: Self-Supervised Contrastive Learning using Random Feature Corruption ↗arXiv (Cornell University) · no summary yetDara Bahri et al.2021 · 12 citations
30. Generative Models are Unsupervised Predictors of Page Quality: A Colossal-Scale Study ↗ACM International Conference on Web Search and Data Mining (WSDM) · no summary yetDara Bahri et al.2021 · 10 citations
31. HyperGrid: Efficient Multi-Task Transformers with Grid-wise Decomposable Hyper Projections ↗arXiv (Cornell University) · no summary yetYi Tay et al.2020 · 9 citations
32. Surprise: Result List Truncation via Extreme Value Theory ↗International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · no summary yetDara Bahri et al.2020 · 3 citations
33. Dense Feature Memory Augmented Transformers for COVID-19 Vaccination Search Classification ↗Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track · no summary yetJai Gupta et al.2022
34. Label Smoothed Embedding Hypothesis for Out-of-Distribution Detection ↗arXiv (Cornell University) · no summary yetDara Bahri et al.2021

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