| Bloom: A 176b-parameter open-access multilingual language model T Le Scao, A Fan, C Akiki, E Pavlick, S Ilić, D Hesslow, R Castagné, ... | 1350 | 2023 |
| Prompting gpt-3 to be reliable C Si, Z Gan, Z Yang, S Wang, J Wang, J Boyd-Graber, L Wang arXiv preprint arXiv:2210.09150, 2022 | 186 | 2022 |
| Between words and characters: A brief history of open-vocabulary modeling and tokenization in NLP SJ Mielke, Z Alyafeai, E Salesky, C Raffel, M Dey, M Gallé, A Raja, C Si, ... arXiv preprint arXiv:2112.10508, 2021 | 132* | 2021 |
| Better robustness by more coverage: Adversarial and mixup data augmentation for robust finetuning C Si, Z Zhang, F Qi, Z Liu, Y Wang, Q Liu, M Sun Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 …, 2021 | 91* | 2021 |
| CharBERT: Character-aware pre-trained language model W Ma, Y Cui, C Si, T Liu, S Wang, G Hu arXiv preprint arXiv:2011.01513, 2020 | 91 | 2020 |
| What does bert learn from multiple-choice reading comprehension datasets? C Si, S Wang, MY Kan, J Jiang arXiv preprint arXiv:1910.12391, 2019 | 46 | 2019 |
| Benchmarking robustness of machine reading comprehension models C Si, Z Yang, Y Cui, W Ma, T Liu, S Wang arXiv preprint arXiv:2004.14004, 2020 | 29 | 2020 |
| Measuring inductive biases of in-context learning with underspecified demonstrations C Si, D Friedman, N Joshi, S Feng, D Chen, H He arXiv preprint arXiv:2305.13299, 2023 | 23 | 2023 |
| Re-examining calibration: The case of question answering C Si, C Zhao, S Min, J Boyd-Graber arXiv preprint arXiv:2205.12507, 2022 | 23 | 2022 |
| What's in a name? answer equivalence for open-domain question answering C Si, C Zhao, J Boyd-Graber arXiv preprint arXiv:2109.05289, 2021 | 21 | 2021 |
| Sentiment aware neural machine translation C Si, K Wu, A Aw, MY Kan Proceedings of the 6th Workshop on Asian Translation, 200-206, 2019 | 15 | 2019 |
| Sub-character tokenization for Chinese pretrained language models C Si, Z Zhang, Y Chen, F Qi, X Wang, Z Liu, Y Wang, Q Liu, M Sun Transactions of the Association for Computational Linguistics 11, 469-487, 2023 | 13* | 2023 |
| Ignore this title and HackAPrompt: Exposing systemic vulnerabilities of LLMs through a global prompt hacking competition S Schulhoff, J Pinto, A Khan, LF Bouchard, C Si, S Anati, V Tagliabue, ... Proceedings of the 2023 Conference on Empirical Methods in Natural Language …, 2023 | 11 | 2023 |
| Dataset mention extraction and classification A Prasad, C Si, MY Kan Proceedings of the Workshop on Extracting Structured Knowledge from …, 2019 | 10 | 2019 |
| Large Language Models Help Humans Verify Truthfulness--Except When They Are Convincingly Wrong C Si, N Goyal, ST Wu, C Zhao, S Feng, H Daumé III, J Boyd-Graber arXiv preprint arXiv:2310.12558, 2023 | 9 | 2023 |
| Design2Code: How Far Are We From Automating Front-End Engineering? C Si, Y Zhang, Z Yang, R Liu, D Yang arXiv preprint arXiv:2403.03163, 2024 | 8 | 2024 |
| Getting more out of mixture of language model reasoning experts C Si, W Shi, C Zhao, L Zettlemoyer, J Boyd-Graber arXiv preprint arXiv:2305.14628, 2023 | 7 | 2023 |
| The Prompt Report: A Systematic Survey of Prompting Techniques S Schulhoff, M Ilie, N Balepur, K Kahadze, A Liu, C Si, Y Li, A Gupta, ... arXiv preprint arXiv:2406.06608, 2024 | 4 | 2024 |
| Best practices and lessons learned on synthetic data for language models R Liu, J Wei, F Liu, C Si, Y Zhang, J Rao, S Zheng, D Peng, D Yang, ... arXiv preprint arXiv:2404.07503, 2024 | 4 | 2024 |
| READIN: A Chinese multi-task benchmark with realistic and diverse input noises C Si, Z Zhang, Y Chen, X Wang, Z Liu, M Sun arXiv preprint arXiv:2302.07324, 2023 | 1 | 2023 |
Jordan Boyd-GraberAssociate Professor, University of MarylandVerified email at umiacs.umd.edu
