SWE-bench: Can language models resolve real-world github issues? CE Jimenez, J Yang, A Wettig, S Yao, K Pei, O Press, K Narasimhan International Conference on Learning Representations (ICLR), 2024 | 184 | 2024 |
Should You Mask 15% in Masked Language Modeling? A Wettig, T Gao, Z Zhong, D Chen Conference of the European Chapter of the Association for Computational ¡¦, 2023 | 153 | 2023 |
Adapting language models to compress contexts A Chevalier, A Wettig, A Ajith, D Chen Conference on Empirical Methods in Natural Language Processing (EMNLP), 2023 | 100 | 2023 |
SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering J Yang, CE Jimenez, A Wettig, K Lieret, S Yao, K Narasimhan, O Press | 73* | 2024 |
A Kernel-Based View of Language Model Fine-Tuning S Malladi, A Wettig, D Yu, D Chen, S Arora International Conference on Machine Learning (ICML), 2023 | 52 | 2023 |
Phrase Retrieval Learns Passage Retrieval, Too J Lee, A Wettig, D Chen Conference on Empirical Methods in Natural Language Processing (EMNLP), 2021 | 46 | 2021 |
Poisoning retrieval corpora by injecting adversarial passages Z Zhong, Z Huang, A Wettig, D Chen Conference on Empirical Methods in Natural Language Processing (EMNLP), 2023 | 33 | 2023 |
Learning Transformer Programs D Friedman, A Wettig, D Chen NeurIPS, 2023 | 27 | 2023 |
QuRating: Selecting High-Quality Data for Training Language Models A Wettig, A Gupta, S Malik, D Chen International Conference on Machine Learning (ICML), 2024 | 18 | 2024 |
Swe-bench: Can language models resolve real-world github issues?, 2024 CE Jimenez, J Yang, A Wettig, S Yao, K Pei, O Press, K Narasimhan URL https://arxiv. org/abs/2310.06770, 2023 | 12 | 2023 |
Finding Dataset Shortcuts with Grammar Induction D Friedman, A Wettig, D Chen Conference on Empirical Methods in Natural Language Processing (EMNLP), 2022 | 11 | 2022 |
Language Models as Science Tutors A Chevalier, J Geng, A Wettig, H Chen, S Mizera, T Annala, MJ Aragon, ... International Conference on Machine Learning (ICML), 2024 | 9 | 2024 |
How to train long-context language models (effectively) T Gao, A Wettig, H Yen, D Chen arXiv preprint arXiv:2410.02660, 2024 | 3 | 2024 |
OLMoE: Open Mixture-of-Experts Language Models N Muennighoff, L Soldaini, D Groeneveld, K Lo, J Morrison, S Min, W Shi, ... arXiv preprint arXiv:2409.02060, 2024 | 1 | 2024 |
Finding transformer circuits with edge pruning A Bhaskar, A Wettig, D Friedman, D Chen arXiv preprint arXiv:2406.16778, 2024 | 1 | 2024 |