Fetching the paper…
Reading the bibliography…
Seeking legal advice is often expensive.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 1901
Earlier work this paper cites.
Obtaining well calibrated probabilities using bayesian binning
Mahdi Pakdaman Naeini, Gregory Cooper, and Milos Hauskrecht. 2015 · 2015
Earlier work this paper cites.
On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger. 2017 · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Earlier work this paper cites.
SciBERT: A pretrained language model for scientific text
Iz Beltagy, Kyle Lo, and Arman Cohan. 2019 · 2019
Earlier work this paper cites.
Neural legal judgment prediction in English
Ilias Chalkidis, Ion Androutsopoulos, and Nikolaos Aletras. 2019 · 2019
Earlier work this paper cites.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Earlier work this paper cites.
Parameter-efficient transfer learning for nlp
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly. 2019 · 2019
Earlier work this paper cites.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, Donghyeon Kim, Sunkyu Kim, Chan Ho So, and Jaewoo Kang. 2019 · 2019
Earlier work this paper cites.
Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 2019
Earlier work this paper cites.
Natural language understanding with the quora question pairs dataset
Lakshay Sharma, Laura Graesser, Nikita Nangia, and Utku Evci. 2019 · 2019
Earlier work this paper cites.
Legal area classification: A comparative study of text classifiers on Singapore Supreme Court judgments
Jerrold Soh, How Khang Lim, and Ian Ernst Chai. 2019 · 2019
Cited alongside, same era.
COMETA: A corpus for medical entity linking in the social media
Marco Basaldella, Fangyu Liu, Ehsan Shareghi, and Nigel Collier. 2020 · 2020
Cited alongside, same era.
The pushshift reddit dataset
Jason Baumgartner, Savvas Zannettou, Brian Keegan, Megan Squire, and Jeremy Blackburn. 2020 · 2020
Cited alongside, same era.
LEGAL-BERT: The muppets straight out of law school
Ilias Chalkidis, Manos Fergadiotis, Prodromos Malakasiotis, Nikolaos Aletras, and Ion Androutsopoulos. 2020 · 2020
Cited alongside, same era.
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020 · 2020
Cited alongside, same era.
Scruples: A corpus of community ethical judgments on 32,000 real-life anecdotes
Nicholas Lourie, Ronan Le Bras, and Yejin Choi. 2021 · 2021
Later among the works it cites.
True few-shot learning with language models
Ethan Perez, Douwe Kiela, and Kyunghyun Cho. 2021 · 2021
Later among the works it cites.
Unsupervised domain adaptation with adapter
Rongsheng Zhang, Yinhe Zheng, Xiaoxi Mao, and Minlie Huang. 2021 · 2021
Later among the works it cites.
When does pretraining help? assessing self-supervised learning for law and the casehold dataset of 53,000+ legal holdings
Lucia Zheng, Neel Guha, Brandon R. Anderson, Peter Henderson, and Daniel E. Ho. 2021 · 2021
Later among the works it cites.
Interpretable low-resource legal decision making
Rohan Bhambhoria, Hui Liu, Samuel Dahan, and Xiaodan Zhu. 2022 · 2022
Closest in time.
LexGLUE: A benchmark dataset for legal language understanding in English
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Zein Shaheen, Gerhard Wohlgenannt, and Erwin Filtz. 2020 · 2020
Cited alongside, same era.
Duplicate question detection with deep learning in stack overflow
Liting Wang, Li Zhang, and Jing Jiang. 2020 · 2020
Cited alongside, same era.
Dank or not? analyzing and predicting the popularity of memes on reddit
Kate Barnes, Tiernon Riesenmy, Minh Duc Trinh, Eli Lleshi, Nora Balogh, and Roland Molontay. 2021 · 2021
Cited alongside, same era.
Deberta: Decoding-enhanced bert with disentangled attention
Pengcheng He, Xiaodong Liu, Jianfeng Gao, and Weizhu Chen. 2021 · 2021
Cited alongside, same era.
Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang. 2021 · 2021
Cited alongside, same era.
Ilias Chalkidis, Abhik Jana, Dirk Hartung, Michael Bommarito, Ion Androutsopoulos, Daniel Katz, and Nikolaos Aletras. 2022 · 2022
Closest in time.
Domain adaptation via prompt learning
Chunjiang Ge, Rui Huang, Mixue Xie, Zihang Lai, Shiji Song, Shuang Li, and Gao Huang. 2022 · 2022
Closest in time.
PPT: Pre-trained prompt tuning for few-shot learning
Yuxian Gu, Xu Han, Zhiyuan Liu, and Minlie Huang. 2022 · 2022
Closest in time.
P-tuning: Prompt tuning can be comparable to fine-tuning across scales and tasks
Xiao Liu, Kaixuan Ji, Yicheng Fu, Weng Tam, Zhengxiao Du, Zhilin Yang, and Jie Tang. 2022 · 2022
Closest in time.
SPoT: Better frozen model adaptation through soft prompt transfer
Tu Vu, Brian Lester, Noah Constant, Rami Al-Rfou’, and Daniel Cer. 2022 · 2022
Closest in time.