| Get to the point: Summarization with pointer-generator networks A See, PJ Liu, CD Manning Association for Computational Linguistics (ACL), 2017 | 3718 | 2017 |
| Improving alignment of dialogue agents via targeted human judgements A Glaese, N McAleese, M Trębacz, J Aslanides, V Firoiu, T Ewalds, ... arXiv preprint arXiv:2209.14375, 2022 | 446 | 2022 |
| What makes a good conversation? how controllable attributes affect human judgments A See, S Roller, D Kiela, J Weston North American Chapter of the Association for Computational Linguistics (NAACL), 2019 | 261 | 2019 |
| Compression of neural machine translation models via pruning A See, MT Luong, CD Manning Computational Natural Language Learning (CoNLL), 2016 | 227 | 2016 |
| Do Massively Pretrained Language Models Make Better Storytellers? A See, A Pappu, R Saxena, A Yerukola, CD Manning Computational Natural Language Learning (CoNLL), 2019 | 165 | 2019 |
| Ramsey vs. lexicographic termination proving B Cook, A See, F Zuleger Tools and Algorithms for the Construction and Analysis of Systems: 19th …, 2013 | 126 | 2013 |
| Neural Generation Meets Real People: Towards Emotionally Engaging Mixed-Initiative Conversations A Paranjape, A See, K Kenealy, H Li, A Hardy, P Qi, KR Sadagopan, ... 3rd Proceedings of Alexa Prize (Alexa Prize 2019), 2020 | 48 | 2020 |
| Understanding and predicting user dissatisfaction in a neural generative chatbot A See, CD Manning Special Interest Group on Discourse and Dialogue (SIGDIAL), 2021 | 34 | 2021 |
| TinkerBell: Cross-lingual Cold-Start Knowledge Base Construction. M Al-Badrashiny, J Bolton, AT Chaganty, K Clark, C Harman, L Huang, ... TAC, 2017 | 18 | 2017 |
| The cost of principles: analyzing power in compatibility weighted voting games A See, Y Bachrach, P Kohli Proceedings of the 2014 international conference on Autonomous agents and …, 2014 | 14 | 2014 |
| Scalable watermarking for identifying large language model outputs S Dathathri, A See, S Ghaisas, PS Huang, R McAdam, J Welbl, V Bachani, ... Nature 634 (8035), 818-823, 2024 | 11 | 2024 |
| Neural generation meets real people: Building a social, informative open-domain dialogue agent EA Chi, A Paranjape, A See, C Chiam, T Chang, K Kenealy, SK Lim, ... arXiv preprint arXiv:2207.12021, 2022 | 11 | 2022 |
| Stanford at TAC KBP 2017: Building a Trilingual Relational Knowledge Graph. AT Chaganty, A Paranjape, J Bolton, M Lamm, J Lei, A See, K Clark, ... TAC, 2017 | 7 | 2017 |
| Consensus, dissensus and synergy between clinicians and specialist foundation models in radiology report generation R Tanno, DGT Barrett, A Sellergren, S Ghaisas, S Dathathri, A See, ... arXiv preprint arXiv:2311.18260, 2023 | 6 | 2023 |
| Neural Generation of Open-Ended Text and Dialogue A See Stanford University, 2021 | 2 | 2021 |
| Collaboration between clinicians and vision–language models in radiology report generation R Tanno, DGT Barrett, A Sellergren, S Ghaisas, S Dathathri, A See, ... Nature Medicine, 1-10, 2024 | 1 | 2024 |
| Multi-stage watermarking of a digital object generated by a machine learning model S Dathathri, AE See, B de Balle Pigem, SK Ghaisas, P Kohli, P Huang, ... US Patent App. 18/611,417, 2024 | | 2024 |
