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LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models
Autor:
Guha, Neel, Nyarko, Julian, Ho, Daniel E., Ré, Christopher, Chilton, Adam, Narayana, Aditya, Chohlas-Wood, Alex, Peters, Austin, Waldon, Brandon, Rockmore, Daniel N., Zambrano, Diego, Talisman, Dmitry, Hoque, Enam, Surani, Faiz, Fagan, Frank, Sarfaty, Galit, Dickinson, Gregory M., Porat, Haggai, Hegland, Jason, Wu, Jessica, Nudell, Joe, Niklaus, Joel, Nay, John, Choi, Jonathan H., Tobia, Kevin, Hagan, Margaret, Ma, Megan, Livermore, Michael, Rasumov-Rahe, Nikon, Holzenberger, Nils, Kolt, Noam, Henderson, Peter, Rehaag, Sean, Goel, Sharad, Gao, Shang, Williams, Spencer, Gandhi, Sunny, Zur, Tom, Iyer, Varun, Li, Zehua
The advent of large language models (LLMs) and their adoption by the legal community has given rise to the question: what types of legal reasoning can LLMs perform? To enable greater study of this question, we present LegalBench: a collaboratively co
Externí odkaz:
http://arxiv.org/abs/2308.11462
Autor:
Chohlas-Wood, Alex, Coots, Madison, Nudell, Joe, Nyarko, Julian, Brunskill, Emma, Rogers, Todd, Goel, Sharad
Millions of Americans must attend mandatory court dates every year. To boost appearance rates, jurisdictions nationwide are increasingly turning to automated reminders, but previous research offers mixed evidence on their effectiveness. In partnershi
Externí odkaz:
http://arxiv.org/abs/2306.12389
Autor:
Koenecke, Allison, Nam, Andrew, Lake, Emily, Nudell, Joe, Quartey, Minnie, Mengesha, Zion, Toups, Connor, Rickford, John R., Jurafsky, Dan, Goel, Sharad
Publikováno v:
Proceedings of the National Academy of Sciences of the United States of America, 2020 Apr 01. 117(14), 7684-7689.
Externí odkaz:
https://www.jstor.org/stable/26929712