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The integration of generative Large Language Models (LLMs) into various applications, including the legal domain, has been accelerated by their expansive and versatile nature.
Roberta: A robustly optimized bert pretraining approach
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Irac: One more time
Howard Gensler. 1985 · 1985
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Some simple effective approximations to the 2-poisson model for probabilistic weighted retrieval
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Finite-time analysis of the multiarmed bandit problem
Peter Auer, Nicolo Cesa-Bianchi, and Paul Fischer. 2002 · 2002
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Dataset cartography: Mapping and diagnosing datasets with training dynamics
Swabha Swayamdipta, Roy Schwartz, Nicholas Lourie, Yizhong Wang, Hannaneh Hajishirzi, Noah A Smith, and Yejin Choi. 2020 · 2009
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Higher demand, lower supply-a comparative assessment of the legal resource landscape for ordinary americans
Gillian K Hadfield. 2010 · 2010
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An empirical evaluation of thompson sampling
Olivier Chapelle and Lihong Li. 2011 · 2011
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Contextual bandits with linear payoff functions
Wei Chu, Lihong Li, Lev Reyzin, and Robert Schapire. 2011 · 2011
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Exploiting machine learning models for chinese legal documents labeling, case classification, and sentencing prediction
Wan-Chen Lin, Tsung-Ting Kuo, Tung-Jia Chang, Chueh-An Yen, Chao-Ju Chen, and Shou-de Lin. 2012 · 2012
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A text mining approach to assist the general public in the retrieval of legal documents
Yen-Liang Chen, Yi-Hung Liu, and Wu-Liang Ho. 2013 · 2013
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Embracing disruption: How technological change in the delivery of legal services can improve access to justice
Raymond H Brescia, Walter McCarthy, Ashley McDonald, Kellan Potts, and Cassandra Rivais. 2014 · 2014
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Convex formulation for learning from positive and unlabeled data
Marthinus Du Plessis, Gang Niu, and Masashi Sugiyama. 2015 · 2015
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Demand for law and the security of property rights: The case of post-soviet russia
Jordan Gans-Morse. 2017 · 2017
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Positive-unlabeled learning with non-negative risk estimator
Ryuichi Kiryo, Gang Niu, Marthinus C Du Plessis, and Masashi Sugiyama. 2017 · 2017
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Few-shot charge prediction with discriminative legal attributes
Zikun Hu, Xiang Li, Cunchao Tu, Zhiyuan Liu, and Maosong Sun. 2018 · 2018
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Positive and unlabeled learning for detecting software functional clones with adversarial training
Huihui Wei and Ming Li. 2018 · 2018
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Hai Ye, Xin Jiang, Zhunchen Luo, and Wenhan Chao. 2018 · 2018
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Legal judgment prediction via topological learning
Haoxi Zhong, Zhipeng Guo, Cunchao Tu, Chaojun Xiao, Zhiyuan Liu, and Maosong Sun. 2018 · 2018
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Demand against law and using authority in corruption criminal action
Indra Gunawan Purba and Alvi Syahrin. 2019 · 2019
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Pure: Positive-unlabeled recommendation with generative adversarial network
Yao Zhou, Jianpeng Xu, Jun Wu, Zeinab Taghavi, Evren Korpeoglu, Kannan Achan, and Jingrui He. 2021 · 2021
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A few-shot transfer learning approach using text-label embedding with legal attributes for law article prediction
Yuh-Shyan Chen, Shin-Wei Chiang, and Meng-Luen Wu. 2022 · 2022
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Wanli: Worker and ai collaboration for natural language inference dataset creation
Alisa Liu, Swabha Swayamdipta, Noah A Smith, and Yejin Choi. 2022 · 2022
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Dist-pu: Positive-unlabeled learning from a label distribution perspective
Yunrui Zhao, Qianqian Xu, Yangbangyan Jiang, Peisong Wen, and Qingming Huang. 2022 · 2022
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Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023 · 2023
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Self-pu: Self boosted and calibrated positive-unlabeled training
Xuxi Chen, Wuyang Chen, Tianlong Chen, Ye Yuan, Chen Gong, Kewei Chen, and Zhangyang Wang. 2020 · 2020
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4 growth, inequality, and unfairness
Steven Horwitz. 2020 · 2020
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Fned: a deep network for fake news early detection on social media
Yang Liu and Yi-Fang Brook Wu. 2020 · 2020
Cited alongside, same era.
Digraph inception convolutional networks
Zekun Tong, Yuxuan Liang, Changsheng Sun, Xinke Li, David Rosenblum, and Andrew Lim. 2020 · 2020
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De-biased court’s view generation with causality
Yiquan Wu, Kun Kuang, Yating Zhang, Xiaozhong Liu, Changlong Sun, Jun Xiao, Yueting Zhuang, Luo Si, and Fei Wu. 2020 · 2020
Cited alongside, same era.
Neural contextual bandits with ucb-based exploration
Dongruo Zhou, Lihong Li, and Quanquan Gu. 2020 · 2020
Cited alongside, same era.
Predictive adversarial learning from positive and unlabeled data
Wenpeng Hu, Ran Le, Bing Liu, Feng Ji, Jinwen Ma, Dongyan Zhao, and Rui Yan. 2021 · 2021
Cited alongside, same era.
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Disco: distilling counterfactuals with large language models
Zeming Chen, Qiyue Gao, Antoine Bosselut, Ashish Sabharwal, and Kyle Richardson. 2023 · 2023
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Chatlaw: Open-source legal large language model with integrated external knowledge bases
Jiaxi Cui, Zongjian Li, Yang Yan, Bohua Chen, and Li Yuan. 2023 · 2023
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Toolformer: Language models can teach themselves to use tools
Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom. 2023 · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. 2023 · 2023
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Community-based hierarchical positive-unlabeled (pu) model fusion for chronic disease prediction
Yang Wu, Xurui Li, Xuhong Zhang, Yangyang Kang, Changlong Sun, and Xiaozhong Liu. 2023 · 2023
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Why johnny can’t prompt: how non-ai experts try (and fail) to design llm prompts
JD Zamfirescu-Pereira, Richmond Y Wong, Bjoern Hartmann, and Qian Yang. 2023 · 2023
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Caselaw access project data
Caselaw Access Project. 2024 · 2024
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Llm maybe longlm: Self-extend llm context window without tuning
Hongye Jin, Xiaotian Han, Jingfeng Yang, Zhimeng Jiang, Zirui Liu, Chia-Yuan Chang, Huiyuan Chen, and Xia Hu. 2024 · 2024
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