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We present a novel Transformer-based multi-agent system for simulating the judicial rulings of the 2010-2016 Supreme Court of the United States.
Language models are few-shot learners
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Originalism as a Political Practice: The Right’s Living Constitution
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Uses and Abuses of Textualism and Originalism in Establishment Clause Interpretation
Esbeck, C. H. 2011 · 2011
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Decision Making by the Modern Supreme Court
Jr, R. L. P.; Curry, B. W.; and Marshall, B. W. 2011 · 2011
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Adam: A Method for Stochastic Optimization
Kingma, D. P.; and Ba, J. 2014 · 2014
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A general approach for predicting the behavior of the Supreme Court of the United States
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Evaluating the effect of law clerk gender on voting at the United States Supreme Court
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Object Hallucination in Image Captioning
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’Don’t Confuse Me with the Facts’: The Use and Misuse of Social Science on the United States Supreme Court
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GPT-2 Domains
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Language models are unsupervised multitask learners
Radford, A.; Wu, J.; Child, R.; Luan, D.; Amodei, D.; Sutskever, I.; et al. 2019 · 2019
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Long-tail Query Expansion using Extractive and Generative Methods
Kim, L.-S.; Kim, S.-s.; Jang, H.-S.; Park, S.-W.; and Kang, I.-H. 2020 · 2020
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2020 Supreme Court Database
Spaeth, H.; Epstein, L.; Ruger, T.; Whittington, K.; Segal, J.; and Martin, A. D. 2014 · 2020
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Unpacking the Supreme Court: The Age of Trump, Law, and Psychohistory
Allcorn, S.; and Stein, H. F. 2021 · 2021
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Liu, P.; Yuan, W.; Fu, J.; Jiang, Z.; Hayashi, H.; and Neubig, G. 2021 · 2021
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LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale
Dettmers, T.; Lewis, M.; Belkada, Y.; and Zettlemoyer, L. 2022 · 2022
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A neural network solves, explains, and generates university math problems by program synthesis and few-shot learning at human level
Drori, I.; Zhang, S.; Shuttleworth, R.; Tang, L.; Lu, A.; Ke, E.; Liu, K.; Chen, L.; Tran, S.; Cheng, N.; et al. 2022 · 2022
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The COVID That Wasn’t: Counterfactual Journalism Using GPT
Hamilton, S.; and Piper, A. 2022 · 2022
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No Language Left Behind: Scaling Human-Centered Machine Translation
NLLB Team. 2022 · 2022
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Training language models to follow instructions with human feedback
Ouyang, L.; Wu, J.; Jiang, X.; Almeida, D.; Wainwright, C. L.; Mishkin, P.; and Zhang, C. 2022 · 2022
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Peterson, J. C.; Giallouri, T.; and Menounou, E. 2021 · 2021
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Out of One, Many: Using Language Models to Simulate Human Samples
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Natural-Language Multi-Agent Simulations of Argumentative Opinion Dynamics
Betz, G. 2022 · 2022
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GPT-NeoX-20B: An Open-Source Autoregressive Language Model
Black, S.; Biderman, S.; Hallahan, E.; Anthony, Q.; Gao, L.; Golding, L.; He, H.; Leahy, C.; McDonell, K.; Phang, J.; and Pieler, M. 2022 · 2022
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Park, J. S.; Popowski, L.; Cai, C. J.; Morris, M. R.; Liang, P.; and Bernstein, M. S. 2022 · 2022
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Generalizing over Long Tail Concepts for Medical Term Normalization
Portelli, B.; Scaboro, S.; Santus, E.; Sedghamiz, H.; Chersoni, E.; and Serra, G. 2022 · 2022
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Scaling Up Models and Data with t5x
Roberts, A.; Chung, H. W.; Levskaya, A.; Mishra, G.; Bradbury, J.; Andor, D.; Narang, S.; Lester, B.; Gaffney, C.; Mohiuddin, A.; Hawthorne, C.; and Lewkowycz, A. 2022 · 2022
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Chain of thought prompting elicits reasoning in large language models
Wei, J.; Wang, X.; Schuurmans, D.; Bosma, M.; Chi, E.; Le, Q.; and Zhou, D. 2022 · 2022
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