| Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference B Warner, A Chaffin, B Clavié, O Weller, O Hallström, S Taghadouini, ... ACL 2025, 2024 | 519 | 2024 |
| Humanity's Last Exam L Phan, A Gatti, Z Han, N Li, J Hu, H Zhang, CBC Zhang, M Shaaban, ... arXiv preprint arXiv:2501.14249, 2025 | 306 | 2025 |
| Humor Detection: A Transformer Gets the Last Laugh O Weller, K Seppi EMNLP 2019, 2019 | 158 | 2019 |
| Learning from Task Descriptions O Weller, N Lourie, M Gardner, ME Peters EMNLP 2020, 2020 | 95 | 2020 |
| MMTEB: Massive Multilingual Text Embedding Benchmark K Enevoldsen, I Chung, I Kerboua, M Kardos, A Mathur, D Stap, J Gala, ... ICLR 2025, 2025 | 81 | 2025 |
| Dated Data: Tracing Knowledge Cutoffs in Large Language Models J Cheng, M Marone, O Weller, D Lawrie, D Khashabi, B Van Durme CoLM 2024 (Top 4 Outstanding Paper Award), 2024 | 71 | 2024 |
| " According to...": Prompting Language Models Improves Quoting from Pre-Training Data O Weller, M Marone, N Weir, D Lawrie, D Khashabi, B Van Durme EACL 2024, 2023 | 63* | 2023 |
| FollowIR: Evaluating and Teaching Information Retrieval Models to Follow Instructions O Weller, B Chang, S MacAvaney, K Lo, A Cohan, B Van Durme, ... NAACL 2025, 2024 | 61 | 2024 |
| Pretrained Models for Multilingual Federated Learning O Weller, M Marone, V Braverman, D Lawrie, B Van Durme NAACL 2022, 2022 | 56 | 2022 |
| Promptriever: Instruction-Trained Retrievers can be Prompted like Language Models O Weller, B Van Durme, D Lawrie, A Paranjape, Y Zhang, J Hessel ICLR 2025, 2024 | 48 | 2024 |
| Predicting Suicidal Thoughts and Behavior among Adolescents using the Risk and Protective Factor Framework: A Large-Scale Machine Learning Approach O Weller, L Sagers, C Hanson, M Barnes, Q Snell, ES Tass Plos one 16 (11), e0258535, 2021 | 45 | 2021 |
| When to use Multi-Task Learning vs Intermediate Fine-Tuning for Pre-trained Encoder Transfer Learning O Weller, K Seppi, M Gardner ACL 2022, 2022 | 43 | 2022 |
| When do Generative Query and Document Expansions Fail? A Comprehensive Study across Methods, Retrievers, and Datasets O Weller, K Lo, D Wadden, D Lawrie, B Van Durme, A Cohan, L Soldaini Findings of EACL 2024, 2023 | 41 | 2023 |
| The rJokes Dataset: a Large Scale Humor Collection O Weller, K Seppi LREC 2020, 2020 | 40 | 2020 |
| Self-[In]Correct: LLMs Struggle with Refining Self-Generated Responses D Jiang, J Zhang, O Weller, N Weir, B Van Durme, D Khashabi AAAI 2025, 2024 | 38* | 2024 |
| Rank1: Test-Time Compute for Reranking in Information Retrieval O Weller, K Ricci, E Yang, A Yates, D Lawrie, B Van Durme ICLR 2025, 2025 | 37 | 2025 |
| End-to-End Speech Translation for Code Switched Speech O Weller, M Sperber, T Pires, H Setiawan, C Gollan, D Telaar, M Paulik ACL 2022, 2022 | 37 | 2022 |
| NevIR: Negation in Neural Information Retrieval O Weller, D Lawrie, B Van Durme EACL 2024, 2023 | 36 | 2023 |
| CLERC: A Dataset for Legal Case Retrieval and Retrieval-Augmented Analysis Generation AB Hou, O Weller, G Qin, E Yang, D Lawrie, N Holzenberger, ... Findings of NAACL 2025, 2024 | 33 | 2024 |
| Defending Against Disinformation Attacks in Open-Domain Question Answering O Weller, A Khan, N Weir, D Lawrie, B Van Durme EACL 2024, 2024 | 33 | 2024 |
Benjamin Van DurmeJohns Hopkins University / MicrosoftVerified email at jhu.edu
