| Retrieval-augmented generation for knowledge-intensive nlp tasks P Lewis, E Perez, A Piktus, F Petroni, V Karpukhin, N Goyal, H Küttler, ... Advances in Neural Information Processing Systems 33, 9459-9474, 2020 | 4721 | 2020 |
| Complex embeddings for simple link prediction T Trouillon, J Welbl, S Riedel, É Gaussier, G Bouchard International conference on machine learning, 2071-2080, 2016 | 3733 | 2016 |
| Convolutional 2d knowledge graph embeddings T Dettmers, P Minervini, P Stenetorp, S Riedel Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018 | 3073 | 2018 |
| Language models as knowledge bases? F Petroni, T Rocktäschel, P Lewis, A Bakhtin, Y Wu, AH Miller, S Riedel arXiv preprint arXiv:1909.01066, 2019 | 2825 | 2019 |
| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, JB Alayrac, J Yu, R Soricut, J Schalkwyk, ... arXiv preprint arXiv:2312.11805, 2023 | 2155 | 2023 |
| Modeling relations and their mentions without labeled text S Riedel, L Yao, A McCallum Machine Learning and Knowledge Discovery in Databases: European Conference …, 2010 | 1627 | 2010 |
| Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity Y Lu, M Bartolo, A Moore, S Riedel, P Stenetorp arXiv preprint arXiv:2104.08786, 2021 | 977 | 2021 |
| The CoNLL 2007 shared task on dependency parsing J Nivre, J Hall, S Kübler, R McDonald, J Nilsson, S Riedel, D Yuret Proceedings of the 2007 Joint Conference on Empirical Methods in Natural …, 2007 | 885 | 2007 |
| Relation extraction with matrix factorization and universal schemas S Riedel, L Yao, A McCallum, BM Marlin Proceedings of the 2013 conference of the North American chapter of the …, 2013 | 807 | 2013 |
| Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context G Team, P Georgiev, VI Lei, R Burnell, L Bai, A Gulati, G Tanzer, ... arXiv preprint arXiv:2403.05530, 2024 | 676 | 2024 |
| Few-shot learning with retrieval augmented language models G Izacard, P Lewis, M Lomeli, L Hosseini, F Petroni, T Schick, ... arXiv preprint arXiv:2208.03299 1 (2), 4, 2022 | 621* | 2022 |
| Unsupervised dense information retrieval with contrastive learning G Izacard, M Caron, L Hosseini, S Riedel, P Bojanowski, A Joulin, ... arXiv preprint arXiv:2112.09118, 2021 | 597 | 2021 |
| Constructing datasets for multi-hop reading comprehension across documents J Welbl, P Stenetorp, S Riedel Transactions of the Association for Computational Linguistics 6, 287-302, 2018 | 573 | 2018 |
| TaBERT: Pretraining for joint understanding of textual and tabular data P Yin, G Neubig, W Yih, S Riedel arXiv preprint arXiv:2005.08314, 2020 | 569 | 2020 |
| Fact checking: Task definition and dataset construction A Vlachos, S Riedel Proceedings of the ACL 2014 workshop on language technologies and …, 2014 | 558 | 2014 |
| Scalable zero-shot entity linking with dense entity retrieval L Wu, F Petroni, M Josifoski, S Riedel, L Zettlemoyer arXiv preprint arXiv:1911.03814, 2019 | 540 | 2019 |
| Autoregressive entity retrieval N De Cao, G Izacard, S Riedel, F Petroni arXiv preprint arXiv:2010.00904, 2020 | 516 | 2020 |
| KILT: a benchmark for knowledge intensive language tasks F Petroni, A Piktus, A Fan, P Lewis, M Yazdani, N De Cao, J Thorne, ... arXiv preprint arXiv:2009.02252, 2020 | 499 | 2020 |
| End-to-end differentiable proving T Rocktäschel, S Riedel Advances in neural information processing systems 30, 2017 | 491 | 2017 |
| MLQA: Evaluating cross-lingual extractive question answering P Lewis, B Oğuz, R Rinott, S Riedel, H Schwenk arXiv preprint arXiv:1910.07475, 2019 | 477 | 2019 |
Tim RocktäschelDirector and Open-Endedness Team Lead at Google DeepMind, Professor of AI at UCL, Fellow ELLIS在 cs.ucl.ac.uk 的电子邮件经过验证
