[18] Neel Guha, Daniel E Ho, Julian Nyarko, and Christopher Ré. Legalbench: Prototyping a collaborative benchmark for legal reasoning. arXiv preprint arXiv:2209.06120, 2022.
[19] Neel Guha, Julian Nyarko, Daniel E. Ho, Christopher Ré, Adam Chilton, Aditya Narayana, Alex Chohlas-Wood, Austin Peters, Brandon Waldon, Daniel N. Rockmore, Diego Zambrano, Dmitry Talisman, Enam Hoque, Faiz Surani, Frank Fagan, Galit Sarfaty, Gregory M. Dickinson, Haggai Porat, Jason Hegland, Jessica Wu, Joe Nudell, Joel Niklaus, John Nay, Jonathan H. Choi, Kevin Tobia, Margaret Hagan, Megan Ma, Michael Livermore, Nikon Rasumov-Rahe, Nils Holzenberger, Noam Kolt, Peter Henderson, Sean Rehaag, Sharad Goel, Shang Gao, Spencer Williams, Sunny Gandhi, Tom Zur, Varun Iyer, and Zehua Li. Legalbench: A collaboratively built benchmark for measuring legal reasoning in large language models, 2023.
[20] Suchin Gururangan, Ana Marasović, Swabha Swayamdipta, Kyle Lo, Iz Beltagy, Doug Downey, and Noah A Smith. Don't stop pretraining: adapt language models to domains and tasks. arXiv preprint arXiv:2004.10964, 2020.
[21] Xu Han, Hao Zhu, Pengfei Yu, Ziyun Wang, Yuan Yao, Zhiyuan Liu, and Maosong Sun. Fewrel: A large-scale supervised few-shot relation classification dataset with state-of-the-art evaluation. arXiv preprint arXiv:1810.10147, 2018.
[22] Peter Hase, Mona Diab, Asli Celikyilmaz, Xian Li, Zornitsa Kozareva, Veselin Stoyanov, Mohit Bansal, and Srinivasan Iyer. Do language models have beliefs? methods for detecting, updating, and visualizing model beliefs. arXiv preprint arXiv:2111.13654, 2021.
[23] Xingwei He, Zhenghao Lin, Yeyun Gong, A-Long Jin, Hang Zhang, Chen Lin, Jian Jiao, Siu Ming Yiu, Nan Duan, and Weizhu Chen. Annolm: Making large language models to be better crowdsourced annotators, 2023.
[24] Peter Henderson, Mark S Krass, Lucia Zheng, Neel Guha, Christopher D Manning, Dan Jurafsky, and Daniel E Ho. Pile of law: Learning responsible data filtering from the law and a 256gb open-source legal dataset. arXiv preprint arXiv:2207.00220, 2022.
[25] Dan Hendrycks, Collin Burns, Anya Chen, and Spencer Ball. CUAD: an expert-annotated NLP dataset for legal contract review. CoRR, abs/2103.06268, 2021.
[26] Cheng-Yu Hsieh, Chun-Liang Li, Chih-Kuan Yeh, Hootan Nakhost, Yasuhisa Fujii, Alexander Ratner, Ranjay Krishna, Chen-Yu Lee, and Tomas Pfister. Distilling step-by-step! outperforming larger language models with less training data and smaller model sizes, 2023.
[27] Jiaxin Huang, Shixiang Shane Gu, Le Hou, Yuexin Wu, Xuezhi Wang, Hongkun Yu, and Jiawei Han. Large language models can self-improve. arXiv preprint arXiv:2210.11610, 2022.
[28] Qiao Jin, Bhuwan Dhingra, William W Cohen, and Xinghua Lu. Probing biomedical embeddings from language models. arXiv preprint arXiv:1904.02181, 2019.
[29] Jaehun Jung, Lianhui Qin, Sean Welleck, Faeze Brahman, Chandra Bhagavatula, Ronan Le Bras, and Yejin Choi. Maieutic prompting: Logically consistent reasoning with recursive explanations. arXiv preprint arXiv:2205.11822, 2022.
[30] Martin Krallinger, Obdulia Rabal, Saber Ahmad Akhondi, Martin Pérez Pérez, Jesus Santamaría, Gael Pérez Rodríguez, Georgios Tsatsaronis, Ander Intxaurrondo, J. A. Lopez, Umesh K. Nandal, Erin M. van Buel, Ambika Chandrasekhar, Marleen Rodenburg, Astrid Lægreid, Marius A. Doornenbal, Julien Oyarzábal, Anália Lourenço, and Alfonso Valencia. Overview of the biocreative vi chemical-protein interaction track. 2017.
[31] Taja Kuzman, Igor Mozetic, and Nikola Ljubesic. Chatgpt: Beginning of an end of manual linguistic data annotation? use case of automatic genre identification. ArXiv, abs/2303.03953, 2023.
[32] Stanford Legal Design Lab. Legal issues taxonomy, 2023.
[33] Suffolk Law School Legal Innovation & Technology Lab. Spot's training data, 2022.