| Bloom: A 176b-parameter open-access multilingual language model T Le Scao, A Fan, C Akiki, E Pavlick, S Ilić, D Hesslow, R Castagné, ... | 1743 | 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 | 263 | 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 | 194* | 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 | 120 | 2020 |
| 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 | 103* | 2021 |
| 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 | 85* | 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 | 84* | 2024 |
| 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 | 78* | 2023 |
| Can llms generate novel research ideas? a large-scale human study with 100+ nlp researchers C Si, D Yang, T Hashimoto arXiv preprint arXiv:2409.04109, 2024 | 55 | 2024 |
| What does bert learn from multiple-choice reading comprehension datasets? C Si, S Wang, MY Kan, J Jiang arXiv preprint arXiv:1910.12391, 2019 | 51 | 2019 |
| Design2code: How far are we from automating front-end engineering? C Si, Y Zhang, Z Yang, R Liu, D Yang arXiv e-prints, arXiv: 2403.03163, 2024 | 38* | 2024 |
| 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 | 33 | 2023 |
| Re-examining calibration: The case of question answering C Si, C Zhao, S Min, J Boyd-Graber arXiv preprint arXiv:2205.12507, 2022 | 32* | 2022 |
| 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 |
| 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 | 25* | 2023 |
| 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 | 24 | 2021 |
| 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 | 20 | 2023 |
| Towards Bidirectional Human-AI Alignment: A Systematic Review for Clarifications, Framework, and Future Directions H Shen, T Knearem, R Ghosh, K Alkiek, K Krishna, Y Liu, Z Ma, S Petridis, ... arXiv preprint arXiv:2406.09264, 2024 | 17 | 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 | 17 | 2023 |
| Sentiment aware neural machine translation C Si, K Wu, A Aw, MY Kan Proceedings of the 6th Workshop on Asian Translation, 200-206, 2019 | 16 | 2019 |
Jordan Boyd-GraberFull Professor, University of MarylandBestätigte E-Mail-Adresse bei umiacs.umd.edu