Riedel, Sebastian

Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-T., Rocktäschel, T., et al. (2020) · arXiv

Safety Systems Engineering in AutonomyEthics and Social Impacts of AI

Cited by 3,101

Convolutional 2D Knowledge Graph Embeddings

Dettmers, T., Minervini, P., Stenetorp, P., & Riedel, S. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence

Advanced Graph Neural NetworksTopic Modeling

Cited by 2,486

Complex Embeddings for Simple Link Prediction

Trouillon, T., Welbl, J., Riedel, S., Gaussier, É., & Bouchard, G. (2016) · arXiv

Advanced Graph Neural NetworksTopic Modeling

Cited by 2,100

Language Models as Knowledge Bases?

Petroni, F., Rocktäschel, T., Riedel, S., Lewis, P., Bakhtin, A., Wu, Y., & Miller, A. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Topic ModelingNatural Language Processing Techniques

Cited by 1,834

Gemini: A Family of Highly Capable Multimodal Models

Gemini Team, Anil, R., Borgeaud, S., Alayrac, J.-B., Yu, J., Soricut, R., Schalkwyk, J., Dai, A. M., Hauth, A., Millican, K., et al. (2023) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 990

Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

Lu, Y., Bartolo, M., Moore, A., Riedel, S., & Stenetorp, P. (2021) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 615

Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

Lu, Y., Bartolo, M., Moore, A., Riedel, S., & Stenetorp, P. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 610

TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data

Yin, P., Neubig, G., Yih, W.-T., & Riedel, S. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Topic ModelingNatural Language Processing Techniques

Cited by 487

Fact Checking: Task definition and dataset construction

Vlachos, A., & Riedel, S. (2014) · Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science

Misinformation and Its ImpactsTopic Modeling

Cited by 402

KILT: a Benchmark for Knowledge Intensive Language Tasks

Petroni, F., Piktus, A., Fan, A., Lewis, P., Yazdani, M., De Cao, N., Thorne, J., Jernite, Y., Karpukhin, V., Maillard, J., et al. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Topic ModelingNatural Language Processing Techniques

Cited by 357

Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Gemini Team, Georgiev, P., Lei, V. I., Burnell, R., Bai, L., Gulati, A., Tanzer, G., Vincent, D., Pan, Z., Wang, S., et al. (2024) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 306

Dynabench: Rethinking Benchmarking in NLP

Kiela, D., Bartolo, M., Nie, Y., Kaushik, D., Geiger, A., Wu, Z., Vidgen, B., Prasad, G., Singh, A., Ringshia, P., et al. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Topic ModelingNatural Language Processing Techniques

Cited by 254

Atlas: Few-shot Learning with Retrieval Augmented Language Models

Izacard, G., Lewis, P., Lomeli, M., Hosseini, L., Petroni, F., Schick, T., Dwivedi-Yu, J., Joulin, A., Riedel, S., & Grave, E. (2022) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 213

Unsupervised Dense Information Retrieval with Contrastive Learning

Izacard, G., Caron, M., Hosseini, L., Riedel, S., Bojanowski, P., Joulin, A., & Grave, E. (2021) · arXiv

Information Retrieval and Search BehaviorTopic Modeling

Cited by 172

How Context Affects Language Models' Factual Predictions

Petroni, F., Lewis, P., Piktus, A., Rocktäschel, T., Wu, Y., Miller, A. H., & Riedel, S. (2020) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 154

Question and Answer Test-Train Overlap in Open-Domain Question Answering Datasets

Lewis, P., Stenetorp, P., & Riedel, S. (2021) · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Topic ModelingInformation Retrieval and Search Behavior

Cited by 145

Interpretation of Natural Language Rules in Conversational Machine Reading

Saeidi, M., Bartolo, M., Lewis, P., Singh, S., Rocktäschel, T., Sheldon, M., Bouchard, G., & Riedel, S. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 141

Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language Models

Logan IV, R., Balazevic, I., Wallace, E., Petroni, F., Singh, S., & Riedel, S. (2022) · Findings of the Association for Computational Linguistics: ACL 2022

Domain Adaptation and Few-Shot LearningTopic Modeling

Cited by 133

Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval

Xiong, W., Li, X. L., Iyer, S., Du, J., Lewis, P., Wang, W. Y., Mehdad, Y., Yih, W.-T., Riedel, S., Kiela, D., et al. (2020) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 112

UCL Machine Reading Group: Four Factor Framework For Fact Finding (HexaF)

Yoneda, T., Mitchell, J., Welbl, J., Stenetorp, P., & Riedel, S. (2018) · Proceedings of the First Workshop on Fact Extraction and VERification (FEVER)

Topic ModelingInformation Retrieval and Search Behavior

Cited by 101

Autoregressive Search Engines: Generating Substrings as Document Identifiers

Bevilacqua, M., Ottaviano, G., Lewis, P., Yih, W.-T., Riedel, S., & Petroni, F. (2022) · arXiv

Algorithms and Data CompressionInformation Retrieval and Search Behavior

Cited by 80

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Comanici, G., Bieber, E., Schaekermann, M., Pasupat, I., Sachdeva, N., Dhillon, I., Blistein, M., Ram, O., Zhang, D., Rosen, E., et al. (2025) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 69

ProoFVer: Natural Logic Theorem Proving for Fact Verification

Krishna, A., Riedel, S., & Vlachos, A. (2022) · Transactions of the Association for Computational Linguistics

Topic ModelingMisinformation and Its Impacts

Cited by 51

PEER: A Collaborative Language Model

Schick, T., Dwivedi-Yu, J., Jiang, Z., Petroni, F., Lewis, P., Izacard, G., You, Q., Nalmpantis, C., Grave, E., & Riedel, S. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 49